From 465f68276bb93fb9bac9a03fc6161197415e358d Mon Sep 17 00:00:00 2001 From: "mmore500.login+git@gmail.com" Date: Tue, 27 Apr 2021 13:43:08 -0400 Subject: [PATCH 001/113] Add joss boilerplate --- .github/workflows/ci.yml | 21 ++++++++ joss/paper.bib | 59 +++++++++++++++++++++ joss/paper.md | 112 +++++++++++++++++++++++++++++++++++++++ 3 files changed, 192 insertions(+) create mode 100644 joss/paper.bib create mode 100644 joss/paper.md diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index d9aeeed3..1faaaf8d 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -138,6 +138,26 @@ jobs: - run: docker build -t mmore500/signalgp-lite . - run: docker ps -a - run: sudo docker run --cap-add=SYS_ADMIN mmore500/signalgp-lite /bin/bash -c "cd /opt/signalgp-lite && make test" + paper: + runs-on: ubuntu-latest + name: Paper Draft + steps: + - name: Checkout + uses: actions/checkout@v2 + - name: Build draft PDF + uses: openjournals/openjournals-draft-action@master + with: + journal: joss + # This should be the path to the paper within your repo. + paper-path: joss/paper.md + - name: Upload + uses: actions/upload-artifact@v1 + with: + name: paper + # This is the output path where Pandoc will write the compiled + # PDF. Note, this should be the same directory as the input + # paper.md + path: joss/paper.pdf deploy-github-pages: runs-on: ubuntu-18.04 name: Deploy to GitHub Pages @@ -152,6 +172,7 @@ jobs: - microbenchmarks - source-clang - source-gcc + - paper env: GH_TOKEN: ${{ github.token }} steps: diff --git a/joss/paper.bib b/joss/paper.bib new file mode 100644 index 00000000..c27c0be9 --- /dev/null +++ b/joss/paper.bib @@ -0,0 +1,59 @@ +@article{Pearson:2017, + url = {http://adsabs.harvard.edu/abs/2017arXiv170304627P}, + Archiveprefix = {arXiv}, + Author = {{Pearson}, S. and {Price-Whelan}, A.~M. and {Johnston}, K.~V.}, + Eprint = {1703.04627}, + Journal = {ArXiv e-prints}, + Keywords = {Astrophysics - Astrophysics of Galaxies}, + Month = mar, + Title = {{Gaps in Globular Cluster Streams: Pal 5 and the Galactic Bar}}, + Year = 2017 +} + +@book{Binney:2008, + url = {http://adsabs.harvard.edu/abs/2008gady.book.....B}, + Author = {{Binney}, J. and {Tremaine}, S.}, + Booktitle = {Galactic Dynamics: Second Edition, by James Binney and Scott Tremaine.~ISBN 978-0-691-13026-2 (HB).~Published by Princeton University Press, Princeton, NJ USA, 2008.}, + Publisher = {Princeton University Press}, + Title = {{Galactic Dynamics: Second Edition}}, + Year = 2008 +} + +@article{gaia, + author = {{Gaia Collaboration}}, + title = "{The Gaia mission}", + journal = {Astronomy and Astrophysics}, + archivePrefix = "arXiv", + eprint = {1609.04153}, + primaryClass = "astro-ph.IM", + keywords = {space vehicles: instruments, Galaxy: structure, astrometry, parallaxes, proper motions, telescopes}, + year = 2016, + month = nov, + volume = 595, + doi = {10.1051/0004-6361/201629272}, + url = {http://adsabs.harvard.edu/abs/2016A%26A...595A...1G}, +} + +@article{astropy, + author = {{Astropy Collaboration}}, + title = "{Astropy: A community Python package for astronomy}", + journal = {Astronomy and Astrophysics}, + archivePrefix = "arXiv", + eprint = {1307.6212}, + primaryClass = "astro-ph.IM", + keywords = {methods: data analysis, methods: miscellaneous, virtual observatory tools}, + year = 2013, + month = oct, + volume = 558, + doi = {10.1051/0004-6361/201322068}, + url = {http://adsabs.harvard.edu/abs/2013A%26A...558A..33A} +} + +@misc{fidgit, + author = {A. Smith}, + title = {Fidgit: An ungodly union of GitHub and Figshare}, + year = {2020}, + publisher = {GitHub}, + journal = {GitHub repository}, + url = {https://github.com/arfon/fidgit} +} diff --git a/joss/paper.md b/joss/paper.md new file mode 100644 index 00000000..294a84b4 --- /dev/null +++ b/joss/paper.md @@ -0,0 +1,112 @@ +--- +title: 'Gala: A Python package for galactic dynamics' +tags: + - Python + - astronomy + - dynamics + - galactic dynamics + - milky way +authors: + - name: Adrian M. Price-Whelan^[Custom footnotes for e.g. denoting who the corresponding author is can be included like this.] + orcid: 0000-0003-0872-7098 + affiliation: "1, 2" # (Multiple affiliations must be quoted) + - name: Author Without ORCID + affiliation: 2 + - name: Author with no affiliation + affiliation: 3 +affiliations: + - name: Lyman Spitzer, Jr. Fellow, Princeton University + index: 1 + - name: Institution Name + index: 2 + - name: Independent Researcher + index: 3 +date: 13 August 2017 +bibliography: paper.bib + +# Optional fields if submitting to a AAS journal too, see this blog post: +# https://blog.joss.theoj.org/2018/12/a-new-collaboration-with-aas-publishing +aas-doi: 10.3847/xxxxx <- update this with the DOI from AAS once you know it. +aas-journal: Astrophysical Journal <- The name of the AAS journal. +--- + +# Summary + +The forces on stars, galaxies, and dark matter under external gravitational +fields lead to the dynamical evolution of structures in the universe. The orbits +of these bodies are therefore key to understanding the formation, history, and +future state of galaxies. The field of "galactic dynamics," which aims to model +the gravitating components of galaxies to study their structure and evolution, +is now well-established, commonly taught, and frequently used in astronomy. +Aside from toy problems and demonstrations, the majority of problems require +efficient numerical tools, many of which require the same base code (e.g., for +performing numerical orbit integration). + +# Statement of need + +`Gala` is an Astropy-affiliated Python package for galactic dynamics. Python +enables wrapping low-level languages (e.g., C) for speed without losing +flexibility or ease-of-use in the user-interface. The API for `Gala` was +designed to provide a class-based and user-friendly interface to fast (C or +Cython-optimized) implementations of common operations such as gravitational +potential and force evaluation, orbit integration, dynamical transformations, +and chaos indicators for nonlinear dynamics. `Gala` also relies heavily on and +interfaces well with the implementations of physical units and astronomical +coordinate systems in the `Astropy` package [@astropy] (`astropy.units` and +`astropy.coordinates`). + +`Gala` was designed to be used by both astronomical researchers and by +students in courses on gravitational dynamics or astronomy. It has already been +used in a number of scientific publications [@Pearson:2017] and has also been +used in graduate courses on Galactic dynamics to, e.g., provide interactive +visualizations of textbook material [@Binney:2008]. The combination of speed, +design, and support for Astropy functionality in `Gala` will enable exciting +scientific explorations of forthcoming data releases from the *Gaia* mission +[@gaia] by students and experts alike. + +# Mathematics + +Single dollars ($) are required for inline mathematics e.g. $f(x) = e^{\pi/x}$ + +Double dollars make self-standing equations: + +$$\Theta(x) = \left\{\begin{array}{l} +0\textrm{ if } x < 0\cr +1\textrm{ else} +\end{array}\right.$$ + +You can also use plain \LaTeX for equations +\begin{equation}\label{eq:fourier} +\hat f(\omega) = \int_{-\infty}^{\infty} f(x) e^{i\omega x} dx +\end{equation} +and refer to \autoref{eq:fourier} from text. + +# Citations + +Citations to entries in paper.bib should be in +[rMarkdown](http://rmarkdown.rstudio.com/authoring_bibliographies_and_citations.html) +format. + +If you want to cite a software repository URL (e.g. something on GitHub without a preferred +citation) then you can do it with the example BibTeX entry below for @fidgit. + +For a quick reference, the following citation commands can be used: +- `@author:2001` -> "Author et al. (2001)" +- `[@author:2001]` -> "(Author et al., 2001)" +- `[@author1:2001; @author2:2001]` -> "(Author1 et al., 2001; Author2 et al., 2002)" + +# Figures + +Figures can be included like this: +![Caption for example figure.\label{fig:example}](figure.png) +and referenced from text using \autoref{fig:example}. + +Figure sizes can be customized by adding an optional second parameter: +![Caption for example figure.](figure.png){ width=20% } + +# Acknowledgements + +We acknowledge contributions from Brigitta Sipocz, Syrtis Major, and Semyeong +Oh, and support from Kathryn Johnston during the genesis of this project. + +# References From 7cfa02bff6010ab06147c38d90a10231f42a6eb3 Mon Sep 17 00:00:00 2001 From: "mmore500.login+git@gmail.com" Date: Tue, 27 Apr 2021 16:47:03 -0400 Subject: [PATCH 002/113] Comment out dummy figure, citations --- joss/paper.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index 294a84b4..0009385f 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -90,19 +90,19 @@ format. If you want to cite a software repository URL (e.g. something on GitHub without a preferred citation) then you can do it with the example BibTeX entry below for @fidgit. -For a quick reference, the following citation commands can be used: + # Figures -Figures can be included like this: + # Acknowledgements From a2c59496aeff753a15c9ced06b84d02b053a3dce Mon Sep 17 00:00:00 2001 From: "mmore500.login+git@gmail.com" Date: Tue, 27 Apr 2021 22:23:07 -0500 Subject: [PATCH 003/113] Replace tabs with spaces --- joss/paper.bib | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/joss/paper.bib b/joss/paper.bib index c27c0be9..b97bc1e8 100644 --- a/joss/paper.bib +++ b/joss/paper.bib @@ -1,22 +1,22 @@ @article{Pearson:2017, - url = {http://adsabs.harvard.edu/abs/2017arXiv170304627P}, - Archiveprefix = {arXiv}, - Author = {{Pearson}, S. and {Price-Whelan}, A.~M. and {Johnston}, K.~V.}, - Eprint = {1703.04627}, - Journal = {ArXiv e-prints}, - Keywords = {Astrophysics - Astrophysics of Galaxies}, - Month = mar, - Title = {{Gaps in Globular Cluster Streams: Pal 5 and the Galactic Bar}}, - Year = 2017 + url = {http://adsabs.harvard.edu/abs/2017arXiv170304627P}, + Archiveprefix = {arXiv}, + Author = {{Pearson}, S. and {Price-Whelan}, A.~M. and {Johnston}, K.~V.}, + Eprint = {1703.04627}, + Journal = {ArXiv e-prints}, + Keywords = {Astrophysics - Astrophysics of Galaxies}, + Month = mar, + Title = {{Gaps in Globular Cluster Streams: Pal 5 and the Galactic Bar}}, + Year = 2017 } @book{Binney:2008, - url = {http://adsabs.harvard.edu/abs/2008gady.book.....B}, - Author = {{Binney}, J. and {Tremaine}, S.}, - Booktitle = {Galactic Dynamics: Second Edition, by James Binney and Scott Tremaine.~ISBN 978-0-691-13026-2 (HB).~Published by Princeton University Press, Princeton, NJ USA, 2008.}, - Publisher = {Princeton University Press}, - Title = {{Galactic Dynamics: Second Edition}}, - Year = 2008 + url = {http://adsabs.harvard.edu/abs/2008gady.book.....B}, + Author = {{Binney}, J. and {Tremaine}, S.}, + Booktitle = {Galactic Dynamics: Second Edition, by James Binney and Scott Tremaine.~ISBN 978-0-691-13026-2 (HB).~Published by Princeton University Press, Princeton, NJ USA, 2008.}, + Publisher = {Princeton University Press}, + Title = {{Galactic Dynamics: Second Edition}}, + Year = 2008 } @article{gaia, From 200fb141e326b611a2dec4762b4d58ec1dac7fe2 Mon Sep 17 00:00:00 2001 From: "mmore500.login+git@gmail.com" Date: Tue, 27 Apr 2021 21:42:18 -0500 Subject: [PATCH 004/113] Replace redundant submodules with symlinks --- .gitmodules | 13 ------------- third-party/Catch | 2 +- third-party/Empirical | 2 +- third-party/cereal | 2 +- 4 files changed, 3 insertions(+), 16 deletions(-) mode change 160000 => 120000 third-party/Catch mode change 160000 => 120000 third-party/Empirical mode change 160000 => 120000 third-party/cereal diff --git a/.gitmodules b/.gitmodules index b0b6d61a..06a9b962 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,22 +1,9 @@ -[submodule "third-party/Catch"] - path = third-party/Catch - url = https://github.com/philsquared/Catch.git -[submodule "third-party/Empirical"] - path = third-party/Empirical - url = https://github.com/devosoft/Empirical.git - branch = master [submodule "third-party/force-cover"] path = third-party/force-cover url = https://github.com/emilydolson/force-cover.git -[submodule "third-party/emsdk"] - path = third-party/emsdk - url = https://github.com/emscripten-core/emsdk.git [submodule "third-party/SignalGP"] path = third-party/SignalGP url = https://github.com/mmore500/SignalGP.git -[submodule "third-party/cereal"] - path = third-party/cereal - url = https://github.com/mmore500/cereal.git [submodule "third-party/conduit"] path = third-party/conduit url = https://github.com/mmore500/conduit.git diff --git a/third-party/Catch b/third-party/Catch deleted file mode 160000 index 44900d53..00000000 --- a/third-party/Catch +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 44900d5371a7595cc43316725f00f7ff41bfc1b7 diff --git a/third-party/Catch b/third-party/Catch new file mode 120000 index 00000000..19fd3208 --- /dev/null +++ b/third-party/Catch @@ -0,0 +1 @@ +Empirical/third-party/Catch \ No newline at end of file diff --git a/third-party/Empirical b/third-party/Empirical deleted file mode 160000 index e7bd66cc..00000000 --- a/third-party/Empirical +++ /dev/null @@ -1 +0,0 @@ -Subproject commit e7bd66cce3c6342d597ad26a745f72118516b5e2 diff --git a/third-party/Empirical b/third-party/Empirical new file mode 120000 index 00000000..ac3cc2fc --- /dev/null +++ b/third-party/Empirical @@ -0,0 +1 @@ +conduit/third-party/Empirical \ No newline at end of file diff --git a/third-party/cereal b/third-party/cereal deleted file mode 160000 index 93ce8c7e..00000000 --- a/third-party/cereal +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 93ce8c7e0cecab6cdc58038e7c806b61df773be4 diff --git a/third-party/cereal b/third-party/cereal new file mode 120000 index 00000000..0b999f44 --- /dev/null +++ b/third-party/cereal @@ -0,0 +1 @@ +Empirical/third-party/cereal \ No newline at end of file From 91a30fe7c554eac2f9e05c4c1f234a1008cb2c19 Mon Sep 17 00:00:00 2001 From: "mmore500.login+git@gmail.com" Date: Tue, 27 Apr 2021 21:43:06 -0500 Subject: [PATCH 005/113] Replace emsdk subrepo with pinned install script --- third-party/emsdk | 1 - third-party/install_emsdk.sh | 16 ++-------------- 2 files changed, 2 insertions(+), 15 deletions(-) delete mode 160000 third-party/emsdk diff --git a/third-party/emsdk b/third-party/emsdk deleted file mode 160000 index aec1e16f..00000000 --- a/third-party/emsdk +++ /dev/null @@ -1 +0,0 @@ -Subproject commit aec1e16f518517ab140a25f59af0840ae754c86c diff --git a/third-party/install_emsdk.sh b/third-party/install_emsdk.sh index aa840b15..e78bedbc 100755 --- a/third-party/install_emsdk.sh +++ b/third-party/install_emsdk.sh @@ -1,20 +1,8 @@ #!/bin/bash -# Get the emsdk repo -git clone https://github.com/emscripten-core/emsdk.git - -# Enter that directory +git clone --recursive https://github.com/emscripten-core/emsdk cd emsdk - -# Fetch the latest version of the emsdk (not needed the first time you clone) -git pull - -# Download and install pinned SDK tools. +git reset --hard e88a3c5bbfef172a5b947768204ef734e2fb6e04 ./emsdk install 1.38.48 - -# Make the pinned SDK "active" for the current user. (writes ~/.emscripten file) ./emsdk activate 1.38.48 - -# Activate PATH and other environment variables in the current terminal source ./emsdk_env.sh -cd .. From 319e736653d89623e6a156201f109052bc54c789 Mon Sep 17 00:00:00 2001 From: "mmore500.login+git@gmail.com" Date: Tue, 27 Apr 2021 22:01:13 -0500 Subject: [PATCH 006/113] Replace force-cover with symlink --- .gitmodules | 3 --- third-party/force-cover | 2 +- 2 files changed, 1 insertion(+), 4 deletions(-) mode change 160000 => 120000 third-party/force-cover diff --git a/.gitmodules b/.gitmodules index 06a9b962..45946115 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,6 +1,3 @@ -[submodule "third-party/force-cover"] - path = third-party/force-cover - url = https://github.com/emilydolson/force-cover.git [submodule "third-party/SignalGP"] path = third-party/SignalGP url = https://github.com/mmore500/SignalGP.git diff --git a/third-party/force-cover b/third-party/force-cover deleted file mode 160000 index fa52e317..00000000 --- a/third-party/force-cover +++ /dev/null @@ -1 +0,0 @@ -Subproject commit fa52e3177812320ddce39dc11223981c5dee958c diff --git a/third-party/force-cover b/third-party/force-cover new file mode 120000 index 00000000..58c1e37c --- /dev/null +++ b/third-party/force-cover @@ -0,0 +1 @@ +Empirical/third-party/force-cover \ No newline at end of file From a3f11071c77aae29bc9fb8818f5f55f1a14ea84a Mon Sep 17 00:00:00 2001 From: "mmore500.login+git@gmail.com" Date: Tue, 27 Apr 2021 22:01:51 -0500 Subject: [PATCH 007/113] Bump conduit pin --- third-party/conduit | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/third-party/conduit b/third-party/conduit index 7ee5014e..81713ec3 160000 --- a/third-party/conduit +++ b/third-party/conduit @@ -1 +1 @@ -Subproject commit 7ee5014e25a92ce7ddf86b52599c6daea965836d +Subproject commit 81713ec3d292138c012d3dedde3bbee1c6471450 From 04d26c33d32a549f3ebb13137f19bf2b37528407 Mon Sep 17 00:00:00 2001 From: "mmore500.login+git@gmail.com" Date: Tue, 4 May 2021 19:34:21 -0400 Subject: [PATCH 008/113] Fix fuzzing compile-time error --- fuzzing/execute.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/fuzzing/execute.cpp b/fuzzing/execute.cpp index 6a5c1dc1..0b9356a3 100644 --- a/fuzzing/execute.cpp +++ b/fuzzing/execute.cpp @@ -26,7 +26,7 @@ void fuzz_one() { cpu.InitializeAnchors( program ); for ( [[maybe_unused]] auto j : timekeeper_t{ std::chrono::seconds{ 1 } }) { - while ( cpu.TryLaunchCore( emp::BitSet<32>(rng) ) ) ; + while ( cpu.TryLaunchCore( emp::BitSet<64>(rng) ) ) ; sgpl::execute_cpu( std::kilo::num, cpu, program ); } From 41c0a616c3655151c5a39c8b9f6e37d272a32b96 Mon Sep 17 00:00:00 2001 From: "mmore500.login+git@gmail.com" Date: Tue, 4 May 2021 19:35:47 -0400 Subject: [PATCH 009/113] Mark core virtual CPU methods noexcept --- include/sgpl/hardware/Core.hpp | 38 ++++++----- include/sgpl/hardware/Cpu.hpp | 66 ++++++++++--------- include/sgpl/hardware/JumpTable.hpp | 40 ++++++----- include/sgpl/library/OpLibrary.hpp | 12 ++-- include/sgpl/operations/actions/ForkIf.hpp | 2 +- include/sgpl/operations/actions/Nop.hpp | 2 +- .../sgpl/operations/actions/TerminateIf.hpp | 2 +- include/sgpl/operations/binary/Add.hpp | 2 +- include/sgpl/operations/binary/Divide.hpp | 2 +- include/sgpl/operations/binary/Modulo.hpp | 2 +- include/sgpl/operations/binary/Multiply.hpp | 2 +- include/sgpl/operations/binary/Subtract.hpp | 2 +- .../sgpl/operations/bitwise/BitwiseAnd.hpp | 2 +- .../sgpl/operations/bitwise/BitwiseNot.hpp | 2 +- include/sgpl/operations/bitwise/BitwiseOr.hpp | 2 +- .../sgpl/operations/bitwise/BitwiseShift.hpp | 2 +- .../sgpl/operations/bitwise/BitwiseXor.hpp | 2 +- include/sgpl/operations/bitwise/CountOnes.hpp | 2 +- .../sgpl/operations/bitwise/RandomFill.hpp | 2 +- include/sgpl/operations/comparison/Equal.hpp | 2 +- .../operations/comparison/GreaterThan.hpp | 2 +- .../sgpl/operations/comparison/LessThan.hpp | 2 +- .../sgpl/operations/comparison/LogicalAnd.hpp | 2 +- .../sgpl/operations/comparison/LogicalOr.hpp | 2 +- .../sgpl/operations/comparison/NotEqual.hpp | 2 +- .../sgpl/operations/flow_global/Anchor.hpp | 2 +- .../sgpl/operations/flow_global/JumpIf.hpp | 2 +- .../sgpl/operations/flow_global/JumpIfNot.hpp | 2 +- .../operations/flow_global/RegulatorAdj.hpp | 2 +- .../operations/flow_global/RegulatorDecay.hpp | 2 +- .../operations/flow_global/RegulatorGet.hpp | 2 +- .../operations/flow_global/RegulatorSet.hpp | 2 +- include/sgpl/operations/flow_local/Anchor.hpp | 2 +- include/sgpl/operations/flow_local/JumpIf.hpp | 2 +- .../sgpl/operations/flow_local/JumpIfNot.hpp | 2 +- .../operations/flow_local/RegulatorAdj.hpp | 2 +- .../operations/flow_local/RegulatorDecay.hpp | 2 +- .../operations/flow_local/RegulatorGet.hpp | 2 +- .../operations/flow_local/RegulatorSet.hpp | 2 +- include/sgpl/operations/unary/Decrement.hpp | 2 +- include/sgpl/operations/unary/Increment.hpp | 2 +- include/sgpl/operations/unary/Negate.hpp | 2 +- include/sgpl/operations/unary/Not.hpp | 2 +- include/sgpl/operations/unary/RandomBool.hpp | 4 +- include/sgpl/operations/unary/RandomDraw.hpp | 12 ++-- include/sgpl/operations/unary/Terminal.hpp | 12 ++-- include/sgpl/program/Instruction.hpp | 6 +- 47 files changed, 149 insertions(+), 119 deletions(-) diff --git a/include/sgpl/hardware/Core.hpp b/include/sgpl/hardware/Core.hpp index 78a85be8..ee94985d 100644 --- a/include/sgpl/hardware/Core.hpp +++ b/include/sgpl/hardware/Core.hpp @@ -41,20 +41,20 @@ class Core { sgpl::CappedSet fork_requests{}; - inline void Terminate() { + inline void Terminate() noexcept { program_counter = std::numeric_limits::max(); }; __attribute__ ((hot)) - inline bool HasTerminated() const { + inline bool HasTerminated() const noexcept { return program_counter == std::numeric_limits::max(); } __attribute__ ((hot)) - inline size_t GetProgramCounter() const { return program_counter; } + inline size_t GetProgramCounter() const noexcept { return program_counter; } __attribute__ ((hot)) - void AdvanceProgramCounter(const size_t program_length) { + void AdvanceProgramCounter(const size_t program_length) noexcept { // equivalent to // if ( HasTerminated() == false ) { // ++program_counter; @@ -68,14 +68,18 @@ class Core { emp_assert( has_termianted || program_counter < program_length ); } - inline bool HasLocalAnchors() const { return local_jump_table.GetSize(); } + inline bool HasLocalAnchors() const noexcept { + return local_jump_table.GetSize(); + } - inline void LoadLocalAnchors( const sgpl::Program& program ) { + inline void LoadLocalAnchors( const sgpl::Program& program ) noexcept { emp_assert( ! HasLocalAnchors() ); local_jump_table.InitializeLocalAnchors( program, GetProgramCounter() ); } - void JumpToGlobalAnchorMatch( const tag_t& query, const size_t jt_idx=0 ) { + void JumpToGlobalAnchorMatch( + const tag_t& query, const size_t jt_idx=0 + ) noexcept { const auto res { global_jump_tables->at( jt_idx ).MatchRegulated(query) }; if ( res.size() ) { program_counter = global_jump_tables->at( jt_idx ).GetVal(res.front()); @@ -83,39 +87,39 @@ class Core { local_jump_table.Clear(); } - void JumpToLocalAnchorMatch(const tag_t& query) { + void JumpToLocalAnchorMatch(const tag_t& query) noexcept { const auto res { local_jump_table.MatchRegulated(query) }; if ( res.size() ) program_counter = local_jump_table.GetVal( res.front() ); } - inline auto& GetLocalJumpTable() { return local_jump_table; } + inline auto& GetLocalJumpTable() noexcept { return local_jump_table; } - inline auto& GetGlobalJumpTable(const size_t jt_idx=0) { + inline auto& GetGlobalJumpTable(const size_t jt_idx=0) noexcept { return global_jump_tables->at( jt_idx ); } - bool RequestFork(const tag_t& tag) { + bool RequestFork(const tag_t& tag) noexcept { return fork_requests.try_push_back( tag ); } - void ResetRegisters() { registers.fill( {} ); } + void ResetRegisters() noexcept { registers.fill( {} ); } - void SetRegisters(const registers_t& set) { registers = set; } + void SetRegisters(const registers_t& set) noexcept { registers = set; } - const registers_t& GetRegisters() { return registers; } + const registers_t& GetRegisters() noexcept { return registers; } - void Reset() { + void Reset() noexcept { fork_requests.clear(); ResetRegisters(); local_jump_table.Clear(); program_counter = 0; } - void SetGlobalJumpTables(global_jump_table_array_t& j_tables) { + void SetGlobalJumpTables(global_jump_table_array_t& j_tables) noexcept { global_jump_tables = &j_tables; } - void DecayRegulators() { local_jump_table.DecayRegulators(); } + void DecayRegulators() noexcept { local_jump_table.DecayRegulators(); } }; diff --git a/include/sgpl/hardware/Cpu.hpp b/include/sgpl/hardware/Cpu.hpp index 4506283a..d869ed13 100644 --- a/include/sgpl/hardware/Cpu.hpp +++ b/include/sgpl/hardware/Cpu.hpp @@ -37,7 +37,7 @@ class Cpu { using tag_t = typename Spec::tag_t; - void RefreshCoreGlobalJumpTablePtrs() { + void RefreshCoreGlobalJumpTablePtrs() noexcept { for (size_t i{}; i < data.scheduler.GetCapacity(); ++i) { data.scheduler.GetBuffer()[i].SetGlobalJumpTables( data.global_jump_tables @@ -48,10 +48,12 @@ class Cpu { public: /// Default constructor. - Cpu() { RefreshCoreGlobalJumpTablePtrs(); } + Cpu() noexcept { RefreshCoreGlobalJumpTablePtrs(); } /// Copy constructor. - Cpu(const Cpu& other) : data(other.data) { RefreshCoreGlobalJumpTablePtrs(); } + Cpu(const Cpu& other) noexcept : data(other.data) { + RefreshCoreGlobalJumpTablePtrs(); + } /// Move constructor. Cpu(Cpu&& other) noexcept : data( std::move(other.data) ) { @@ -59,7 +61,7 @@ class Cpu { } /// Copy assignment operator. - Cpu& operator=(const Cpu& other) { return *this = Cpu(other); } + Cpu& operator=(const Cpu& other) noexcept { return *this = Cpu(other); } /// Move assignment operator. Cpu& operator=(Cpu&& other) noexcept { @@ -67,39 +69,39 @@ class Cpu { return *this; } - void ActivateNextCore() { + void ActivateNextCore() noexcept { emp_assert( GetNumBusyCores() ); ++data.active_core_idx %= GetNumBusyCores(); } - bool TryActivateNextCore() { + bool TryActivateNextCore() noexcept { if ( HasActiveCore() ) { ActivateNextCore(); return true; } else { emp_assert( data.active_core_idx == 0 ); return false; } } - void ActivatePrevCore() { + void ActivatePrevCore() noexcept { emp_assert( GetNumBusyCores() ); data.active_core_idx += GetNumBusyCores() - 1; data.active_core_idx %= GetNumBusyCores(); } - bool TryActivatePrevCore() { + bool TryActivatePrevCore() noexcept { if ( HasActiveCore() ) { ActivatePrevCore(); return true; } else { emp_assert( data.active_core_idx == 0 ); return false; } } __attribute__ ((hot)) - core_t& GetActiveCore() { + core_t& GetActiveCore() noexcept { emp_assert( HasActiveCore() ); return data.scheduler.Get( data.active_core_idx ); }; - core_t& GetFreshestCore() { + core_t& GetFreshestCore() noexcept { emp_assert( HasActiveCore() ); return data.scheduler.GetHead(); }; - void KillActiveCore() { + void KillActiveCore() noexcept { emp_assert( HasActiveCore() ); for ( const auto& req : GetActiveCore().fork_requests ) { if ( !TryLaunchCore(req) ) break; @@ -108,66 +110,66 @@ class Cpu { TryActivatePrevCore(); } - void KillStaleCore() { + void KillStaleCore() noexcept { emp_assert( !HasFreeCore() ); data.scheduler.ReleaseTail(); // no need to activate prev core, killed core is idx 0 } - void DoLaunchCore() { + void DoLaunchCore() noexcept { emp_assert( HasFreeCore() ); auto& acquired = data.scheduler.Acquire(); acquired.Reset(); } - bool TryLaunchCore() { + bool TryLaunchCore() noexcept { if ( ! HasFreeCore() ) return false; else { DoLaunchCore(); return true; } } - void ForceLaunchCore() { + void ForceLaunchCore() noexcept { if ( ! HasFreeCore() ) KillStaleCore(); DoLaunchCore(); } - void DoLaunchCore( const tag_t& tag, const size_t jt_idx=0 ) { + void DoLaunchCore( const tag_t& tag, const size_t jt_idx=0 ) noexcept { emp_assert( HasFreeCore() ); auto& acquired = data.scheduler.Acquire(); acquired.Reset(); acquired.JumpToGlobalAnchorMatch( tag, jt_idx ); } - bool TryLaunchCore( const tag_t& tag, const size_t jt_idx=0 ) { + bool TryLaunchCore( const tag_t& tag, const size_t jt_idx=0 ) noexcept { if ( ! HasFreeCore() ) return false; else { DoLaunchCore( tag, jt_idx ); return true; } } - void ForceLaunchCore( const tag_t& tag, const size_t jt_idx=0 ) { + void ForceLaunchCore( const tag_t& tag, const size_t jt_idx=0 ) noexcept { if ( ! HasFreeCore() ) KillStaleCore(); DoLaunchCore( tag, jt_idx ); } - size_t GetNumBusyCores() const { return data.scheduler.GetSize(); } + size_t GetNumBusyCores() const noexcept { return data.scheduler.GetSize(); } - size_t GetNumFreeCores() const { + size_t GetNumFreeCores() const noexcept { return data.scheduler.GetAvailableCapacity(); } - size_t GetMaxCores() const { return data.scheduler.GetCapacity(); } + size_t GetMaxCores() const noexcept { return data.scheduler.GetCapacity(); } __attribute__ ((hot)) - bool HasActiveCore() const { return GetNumBusyCores(); } + bool HasActiveCore() const noexcept { return GetNumBusyCores(); } __attribute__ ((hot)) - bool HasFreeCore() const { return GetNumFreeCores(); } + bool HasFreeCore() const noexcept { return GetNumFreeCores(); } - void Reset() { + void Reset() noexcept { data.scheduler.Reset(); data.active_core_idx = {}; for ( auto& table : data.global_jump_tables ) table.Clear(); } - void InitializeAnchors(const sgpl::Program& program) { + void InitializeAnchors(const sgpl::Program& program) noexcept { Reset(); for( size_t i{}; i < data.global_jump_tables.size() ; ++i ) { data.global_jump_tables[i].InitializeGlobalAnchors( @@ -177,21 +179,25 @@ class Cpu { } } - const core_t& GetCore( const size_t idx ) const { + const core_t& GetCore( const size_t idx ) const noexcept { return data.scheduler.Get( idx ); } - const global_jump_table_t& GetGlobalJumpTable( const size_t idx=0 ) const { + const global_jump_table_t& GetGlobalJumpTable( const size_t idx=0 ) + const noexcept + { return data.global_jump_tables[ idx ]; } - void DecayGlobalRegulators() { + void DecayGlobalRegulators() noexcept { for ( auto& gjt : data.global_jump_tables ) gjt.DecayRegulators(); } - void AdvanceCycleClock(const size_t amt) { data.lifetime_cycle_clock += amt; } + void AdvanceCycleClock(const size_t amt) noexcept { + data.lifetime_cycle_clock += amt; + } - size_t GetCyclesSinceConstruction() const { + size_t GetCyclesSinceConstruction() const noexcept { return data.lifetime_cycle_clock; } diff --git a/include/sgpl/hardware/JumpTable.hpp b/include/sgpl/hardware/JumpTable.hpp index e8417879..80678287 100644 --- a/include/sgpl/hardware/JumpTable.hpp +++ b/include/sgpl/hardware/JumpTable.hpp @@ -19,45 +19,55 @@ struct JumpTable { using program_t = sgpl::Program; using uid_t = size_t; - inline auto MatchRaw( const tag_t & query ) { + inline auto MatchRaw( const tag_t & query ) noexcept { return match_bin.MatchRaw(query); } - inline auto MatchRegulated( const tag_t & query ) { + inline auto MatchRegulated( const tag_t & query ) noexcept { return match_bin.MatchRegulated(query); } - inline void SetRegulator( const uid_t uid, const float set ) { + inline void SetRegulator( const uid_t uid, const float set ) noexcept { match_bin.SetRegulator(uid, set); } - inline void AdjRegulator( const uid_t uid, const float set ) { + inline void AdjRegulator( const uid_t uid, const float set ) noexcept { match_bin.AdjRegulator(uid, set); } - inline void DecayRegulator( const uid_t uid, const float amt ) { + inline void DecayRegulator( const uid_t uid, const float amt ) noexcept { match_bin.DecayRegulator(uid, amt); } - inline float ViewRegulator(const uid_t uid) const { + inline float ViewRegulator(const uid_t uid) const noexcept { return match_bin.ViewRegulator(uid); } - inline void DecayRegulators() { match_bin.DecayRegulators(); } + inline void DecayRegulators() noexcept { match_bin.DecayRegulators(); } - inline uid_t GetUid(const tag_t tag) const { return match_bin.GetUid(tag); } + inline uid_t GetUid(const tag_t tag) const noexcept { + return match_bin.GetUid(tag); + } - inline uid_t GetUid(const size_t pos) const { return match_bin.GetUid(pos); } + inline uid_t GetUid(const size_t pos) const noexcept { + return match_bin.GetUid(pos); + } - inline auto GetVal(const uid_t uid) const { return match_bin.GetVal( uid ); } + inline auto GetVal(const uid_t uid) const noexcept { + return match_bin.GetVal( uid ); + } - inline bool HasVal(const size_t pos) const { return match_bin.HasVal( pos ); } + inline bool HasVal(const size_t pos) const noexcept { + return match_bin.HasVal( pos ); + } - inline void Clear() { match_bin.Clear(); } + inline void Clear() noexcept { match_bin.Clear(); } - inline size_t GetSize() const { return match_bin.GetSize(); } + inline size_t GetSize() const noexcept { return match_bin.GetSize(); } - void InitializeLocalAnchors(const program_t& prog, const size_t start_pos) { + void InitializeLocalAnchors( + const program_t& prog, const size_t start_pos + ) noexcept { Clear(); if ( prog.empty() ) return; const size_t prog_len{ prog.size() }; @@ -78,7 +88,7 @@ struct JumpTable { void InitializeGlobalAnchors( const sgpl::Program& program, const size_t inclusion_mod=1 - ) { + ) noexcept { Clear(); for (size_t pos{}; pos < program.size(); ++pos) { const auto& instruction = program[pos]; diff --git a/include/sgpl/library/OpLibrary.hpp b/include/sgpl/library/OpLibrary.hpp index c1ea21ad..60dfc540 100644 --- a/include/sgpl/library/OpLibrary.hpp +++ b/include/sgpl/library/OpLibrary.hpp @@ -24,7 +24,7 @@ struct OpLibrary : public std::tuple { inline static sgpl::OpLookup lookup_table; - constexpr static bool IsAnchorLocalOpCode(const size_t op_code) { + constexpr static bool IsAnchorLocalOpCode(const size_t op_code) noexcept { if constexpr ( uitsl::tuple_has_type::value @@ -38,7 +38,7 @@ struct OpLibrary : public std::tuple { } - constexpr static bool IsAnchorGlobalOpCode(const size_t op_code) { + constexpr static bool IsAnchorGlobalOpCode(const size_t op_code) noexcept { if constexpr ( uitsl::tuple_has_type::value @@ -52,17 +52,19 @@ struct OpLibrary : public std::tuple { } - static bool IsNopOpCode(const size_t op_code) { + static bool IsNopOpCode(const size_t op_code) noexcept { return GetOpName( op_code ).rfind("Nop-", 0) == 0; } - constexpr static bool IsAnchorOpCode( const size_t op_code ) { + constexpr static bool IsAnchorOpCode( const size_t op_code ) noexcept { return IsAnchorLocalOpCode( op_code ) || IsAnchorGlobalOpCode( op_code ); } - constexpr static size_t GetSize() { return std::tuple_size(); } + constexpr static size_t GetSize() noexcept { + return std::tuple_size(); + } template using Operation = typename std::tuple_element::type; diff --git a/include/sgpl/operations/actions/ForkIf.hpp b/include/sgpl/operations/actions/ForkIf.hpp index af18fdbf..adbe8375 100644 --- a/include/sgpl/operations/actions/ForkIf.hpp +++ b/include/sgpl/operations/actions/ForkIf.hpp @@ -23,7 +23,7 @@ struct ForkIf { const sgpl::Instruction& inst, const sgpl::Program& program, typename Spec::peripheral_t& - ) { + ) noexcept { if ( core.registers[ inst.args[0] ] ) core.RequestFork( inst.tag ); } diff --git a/include/sgpl/operations/actions/Nop.hpp b/include/sgpl/operations/actions/Nop.hpp index 62978f45..65aa59d5 100644 --- a/include/sgpl/operations/actions/Nop.hpp +++ b/include/sgpl/operations/actions/Nop.hpp @@ -24,7 +24,7 @@ struct Nop { const sgpl::Instruction& inst, const sgpl::Program& program, typename Spec::peripheral_t& - ) { + ) noexcept { for (size_t i{}; i < NumRngTouches; ++i ) { sgpl::tlrand.Get().StepEngine(); } diff --git a/include/sgpl/operations/actions/TerminateIf.hpp b/include/sgpl/operations/actions/TerminateIf.hpp index 825565b1..134f4600 100644 --- a/include/sgpl/operations/actions/TerminateIf.hpp +++ b/include/sgpl/operations/actions/TerminateIf.hpp @@ -22,7 +22,7 @@ struct TerminateIf { const sgpl::Instruction& inst, const sgpl::Program& program, typename Spec::peripheral_t& - ) { + ) noexcept { if ( core.registers[ inst.args[0] ] ) core.Terminate(); } diff --git a/include/sgpl/operations/binary/Add.hpp b/include/sgpl/operations/binary/Add.hpp index 04c42a93..a4d06629 100644 --- a/include/sgpl/operations/binary/Add.hpp +++ b/include/sgpl/operations/binary/Add.hpp @@ -22,7 +22,7 @@ struct Add { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { const size_t a = inst.args[0], b = inst.args[1], c = inst.args[2]; core.registers[a] = core.registers[b] + core.registers[c]; } diff --git a/include/sgpl/operations/binary/Divide.hpp b/include/sgpl/operations/binary/Divide.hpp index 418eb880..8d0826d9 100644 --- a/include/sgpl/operations/binary/Divide.hpp +++ b/include/sgpl/operations/binary/Divide.hpp @@ -22,7 +22,7 @@ struct Divide { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { const size_t a = inst.args[0], b = inst.args[1], c = inst.args[2]; // division by zero allowed diff --git a/include/sgpl/operations/binary/Modulo.hpp b/include/sgpl/operations/binary/Modulo.hpp index 63e5285c..d8749236 100644 --- a/include/sgpl/operations/binary/Modulo.hpp +++ b/include/sgpl/operations/binary/Modulo.hpp @@ -22,7 +22,7 @@ struct Modulo { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { const size_t a = inst.args[0], b = inst.args[1], c = inst.args[2]; // TODO refactor this into a library call diff --git a/include/sgpl/operations/binary/Multiply.hpp b/include/sgpl/operations/binary/Multiply.hpp index 32a034e8..a74160cc 100644 --- a/include/sgpl/operations/binary/Multiply.hpp +++ b/include/sgpl/operations/binary/Multiply.hpp @@ -22,7 +22,7 @@ struct Multiply { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { const size_t a = inst.args[0], b = inst.args[1], c = inst.args[2]; core.registers[a] = core.registers[b] * core.registers[c]; } diff --git a/include/sgpl/operations/binary/Subtract.hpp b/include/sgpl/operations/binary/Subtract.hpp index 1933ea86..0278c268 100644 --- a/include/sgpl/operations/binary/Subtract.hpp +++ b/include/sgpl/operations/binary/Subtract.hpp @@ -22,7 +22,7 @@ struct Subtract { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { const size_t a = inst.args[0], b = inst.args[1], c = inst.args[2]; core.registers[a] = core.registers[b] - core.registers[c]; } diff --git a/include/sgpl/operations/bitwise/BitwiseAnd.hpp b/include/sgpl/operations/bitwise/BitwiseAnd.hpp index 18494181..495c3479 100644 --- a/include/sgpl/operations/bitwise/BitwiseAnd.hpp +++ b/include/sgpl/operations/bitwise/BitwiseAnd.hpp @@ -24,7 +24,7 @@ struct BitwiseAnd { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { const size_t a = inst.args[0], b = inst.args[1], c = inst.args[2]; std::transform( diff --git a/include/sgpl/operations/bitwise/BitwiseNot.hpp b/include/sgpl/operations/bitwise/BitwiseNot.hpp index 9bb8c779..ad3937cb 100644 --- a/include/sgpl/operations/bitwise/BitwiseNot.hpp +++ b/include/sgpl/operations/bitwise/BitwiseNot.hpp @@ -24,7 +24,7 @@ struct BitwiseNot { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { const size_t a = inst.args[0], b = inst.args[1]; std::transform( diff --git a/include/sgpl/operations/bitwise/BitwiseOr.hpp b/include/sgpl/operations/bitwise/BitwiseOr.hpp index a27c2530..235e27c0 100644 --- a/include/sgpl/operations/bitwise/BitwiseOr.hpp +++ b/include/sgpl/operations/bitwise/BitwiseOr.hpp @@ -24,7 +24,7 @@ struct BitwiseOr { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { const size_t a = inst.args[0], b = inst.args[1], c = inst.args[2]; std::transform( diff --git a/include/sgpl/operations/bitwise/BitwiseShift.hpp b/include/sgpl/operations/bitwise/BitwiseShift.hpp index 9d541d70..c36377a7 100644 --- a/include/sgpl/operations/bitwise/BitwiseShift.hpp +++ b/include/sgpl/operations/bitwise/BitwiseShift.hpp @@ -25,7 +25,7 @@ struct BitwiseShift { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { const size_t a = inst.args[0], b = inst.args[1], c = inst.args[2]; static_assert( sizeof(core.registers[b]) <= sizeof(size_t) ); diff --git a/include/sgpl/operations/bitwise/BitwiseXor.hpp b/include/sgpl/operations/bitwise/BitwiseXor.hpp index 3ae179d5..ad3d7fbc 100644 --- a/include/sgpl/operations/bitwise/BitwiseXor.hpp +++ b/include/sgpl/operations/bitwise/BitwiseXor.hpp @@ -24,7 +24,7 @@ struct BitwiseXor { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { const size_t a = inst.args[0], b = inst.args[1], c = inst.args[2]; std::transform( diff --git a/include/sgpl/operations/bitwise/CountOnes.hpp b/include/sgpl/operations/bitwise/CountOnes.hpp index 2d3b3215..854eae8b 100644 --- a/include/sgpl/operations/bitwise/CountOnes.hpp +++ b/include/sgpl/operations/bitwise/CountOnes.hpp @@ -25,7 +25,7 @@ struct CountOnes { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { const size_t a = inst.args[0], b = inst.args[1]; static_assert( sizeof(core.registers[a]) <= sizeof(size_t) ); diff --git a/include/sgpl/operations/bitwise/RandomFill.hpp b/include/sgpl/operations/bitwise/RandomFill.hpp index ecd49548..f21ac7fc 100644 --- a/include/sgpl/operations/bitwise/RandomFill.hpp +++ b/include/sgpl/operations/bitwise/RandomFill.hpp @@ -23,7 +23,7 @@ struct RandomFill { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { auto& target = core.registers[ inst.args[0] ]; diff --git a/include/sgpl/operations/comparison/Equal.hpp b/include/sgpl/operations/comparison/Equal.hpp index 1384ea65..07288193 100644 --- a/include/sgpl/operations/comparison/Equal.hpp +++ b/include/sgpl/operations/comparison/Equal.hpp @@ -22,7 +22,7 @@ struct Equal { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { const size_t a = inst.args[0], b = inst.args[1], c = inst.args[2]; core.registers[a] = core.registers[b] == core.registers[c]; } diff --git a/include/sgpl/operations/comparison/GreaterThan.hpp b/include/sgpl/operations/comparison/GreaterThan.hpp index 0ffbaff2..1219b4f9 100644 --- a/include/sgpl/operations/comparison/GreaterThan.hpp +++ b/include/sgpl/operations/comparison/GreaterThan.hpp @@ -22,7 +22,7 @@ struct GreaterThan { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { const size_t a = inst.args[0], b = inst.args[1], c = inst.args[2]; core.registers[a] = core.registers[b] > core.registers[c]; } diff --git a/include/sgpl/operations/comparison/LessThan.hpp b/include/sgpl/operations/comparison/LessThan.hpp index 83745c0c..5cbd67da 100644 --- a/include/sgpl/operations/comparison/LessThan.hpp +++ b/include/sgpl/operations/comparison/LessThan.hpp @@ -22,7 +22,7 @@ struct LessThan { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { const size_t a = inst.args[0], b = inst.args[1], c = inst.args[2]; core.registers[a] = core.registers[b] < core.registers[c]; } diff --git a/include/sgpl/operations/comparison/LogicalAnd.hpp b/include/sgpl/operations/comparison/LogicalAnd.hpp index 2d011237..aa1eb6aa 100644 --- a/include/sgpl/operations/comparison/LogicalAnd.hpp +++ b/include/sgpl/operations/comparison/LogicalAnd.hpp @@ -22,7 +22,7 @@ struct LogicalAnd { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { const size_t a = inst.args[0], b = inst.args[1], c = inst.args[2]; core.registers[a] = core.registers[b] && core.registers[c]; } diff --git a/include/sgpl/operations/comparison/LogicalOr.hpp b/include/sgpl/operations/comparison/LogicalOr.hpp index 5946d979..02b27dff 100644 --- a/include/sgpl/operations/comparison/LogicalOr.hpp +++ b/include/sgpl/operations/comparison/LogicalOr.hpp @@ -22,7 +22,7 @@ struct LogicalOr { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { const size_t a = inst.args[0], b = inst.args[1], c = inst.args[2]; core.registers[a] = core.registers[b] || core.registers[c]; } diff --git a/include/sgpl/operations/comparison/NotEqual.hpp b/include/sgpl/operations/comparison/NotEqual.hpp index f3bca163..4e235ea9 100644 --- a/include/sgpl/operations/comparison/NotEqual.hpp +++ b/include/sgpl/operations/comparison/NotEqual.hpp @@ -22,7 +22,7 @@ struct NotEqual { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { const size_t a = inst.args[0], b = inst.args[1], c = inst.args[2]; core.registers[a] = core.registers[b] != core.registers[c]; } diff --git a/include/sgpl/operations/flow_global/Anchor.hpp b/include/sgpl/operations/flow_global/Anchor.hpp index cb69481c..d7477b22 100644 --- a/include/sgpl/operations/flow_global/Anchor.hpp +++ b/include/sgpl/operations/flow_global/Anchor.hpp @@ -24,7 +24,7 @@ struct Anchor { const sgpl::Instruction& inst, const sgpl::Program& program, typename Spec::peripheral_t& peripheral - ) { + ) noexcept { if ( core.HasLocalAnchors() ) core.Terminate(); else core.LoadLocalAnchors( program ); } diff --git a/include/sgpl/operations/flow_global/JumpIf.hpp b/include/sgpl/operations/flow_global/JumpIf.hpp index e305d15c..e59efcbb 100644 --- a/include/sgpl/operations/flow_global/JumpIf.hpp +++ b/include/sgpl/operations/flow_global/JumpIf.hpp @@ -24,7 +24,7 @@ struct JumpIf { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { if ( core.registers[ inst.args[0] ] ) { core.JumpToGlobalAnchorMatch( inst.tag ); if ( core.registers[ inst.args[1] ] ) core.ResetRegisters(); diff --git a/include/sgpl/operations/flow_global/JumpIfNot.hpp b/include/sgpl/operations/flow_global/JumpIfNot.hpp index d986333b..c5423ec9 100644 --- a/include/sgpl/operations/flow_global/JumpIfNot.hpp +++ b/include/sgpl/operations/flow_global/JumpIfNot.hpp @@ -24,7 +24,7 @@ struct JumpIfNot { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { if ( !core.registers[ inst.args[0] ] ) { core.JumpToGlobalAnchorMatch( inst.tag ); if ( !core.registers[ inst.args[1] ] ) core.ResetRegisters(); diff --git a/include/sgpl/operations/flow_global/RegulatorAdj.hpp b/include/sgpl/operations/flow_global/RegulatorAdj.hpp index dc064266..344bb22b 100644 --- a/include/sgpl/operations/flow_global/RegulatorAdj.hpp +++ b/include/sgpl/operations/flow_global/RegulatorAdj.hpp @@ -25,7 +25,7 @@ struct RegulatorAdj { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { auto& jump_table = core.GetGlobalJumpTable( JUMP_TABLE_IDX ); for ( const auto uid : jump_table.MatchRaw(inst.tag) ) { diff --git a/include/sgpl/operations/flow_global/RegulatorDecay.hpp b/include/sgpl/operations/flow_global/RegulatorDecay.hpp index a2f83a06..46cff883 100644 --- a/include/sgpl/operations/flow_global/RegulatorDecay.hpp +++ b/include/sgpl/operations/flow_global/RegulatorDecay.hpp @@ -26,7 +26,7 @@ struct RegulatorDecay { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { auto& jump_table = core.GetGlobalJumpTable( JUMP_TABLE_IDX ); for ( const auto uid : jump_table.MatchRaw(inst.tag) ) { diff --git a/include/sgpl/operations/flow_global/RegulatorGet.hpp b/include/sgpl/operations/flow_global/RegulatorGet.hpp index 9dc7349b..de67d159 100644 --- a/include/sgpl/operations/flow_global/RegulatorGet.hpp +++ b/include/sgpl/operations/flow_global/RegulatorGet.hpp @@ -25,7 +25,7 @@ struct RegulatorGet { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { auto& jump_table = core.GetGlobalJumpTable( JUMP_TABLE_IDX ); for ( const auto uid : jump_table.MatchRaw(inst.tag) ) { diff --git a/include/sgpl/operations/flow_global/RegulatorSet.hpp b/include/sgpl/operations/flow_global/RegulatorSet.hpp index 8cbe895e..d96b49d4 100644 --- a/include/sgpl/operations/flow_global/RegulatorSet.hpp +++ b/include/sgpl/operations/flow_global/RegulatorSet.hpp @@ -25,7 +25,7 @@ struct RegulatorSet { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { auto& jump_table = core.GetGlobalJumpTable( JUMP_TABLE_IDX ); for ( const auto uid : jump_table.MatchRaw(inst.tag) ) { diff --git a/include/sgpl/operations/flow_local/Anchor.hpp b/include/sgpl/operations/flow_local/Anchor.hpp index c84625ce..ab1453f5 100644 --- a/include/sgpl/operations/flow_local/Anchor.hpp +++ b/include/sgpl/operations/flow_local/Anchor.hpp @@ -24,7 +24,7 @@ struct Anchor { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { ; } + ) noexcept { ; } static std::string name() { return "Local Anchor"; } diff --git a/include/sgpl/operations/flow_local/JumpIf.hpp b/include/sgpl/operations/flow_local/JumpIf.hpp index d4ce9153..3afbd63b 100644 --- a/include/sgpl/operations/flow_local/JumpIf.hpp +++ b/include/sgpl/operations/flow_local/JumpIf.hpp @@ -24,7 +24,7 @@ struct JumpIf { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { if ( core.registers[ inst.args[0] ] ) { core.JumpToLocalAnchorMatch( inst.tag ); } diff --git a/include/sgpl/operations/flow_local/JumpIfNot.hpp b/include/sgpl/operations/flow_local/JumpIfNot.hpp index 13f0aade..2f3f1faf 100644 --- a/include/sgpl/operations/flow_local/JumpIfNot.hpp +++ b/include/sgpl/operations/flow_local/JumpIfNot.hpp @@ -24,7 +24,7 @@ struct JumpIfNot { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { if ( !core.registers[ inst.args[0] ] ) { core.JumpToLocalAnchorMatch( inst.tag ); } diff --git a/include/sgpl/operations/flow_local/RegulatorAdj.hpp b/include/sgpl/operations/flow_local/RegulatorAdj.hpp index 7c4981bd..6c43ec48 100644 --- a/include/sgpl/operations/flow_local/RegulatorAdj.hpp +++ b/include/sgpl/operations/flow_local/RegulatorAdj.hpp @@ -24,7 +24,7 @@ struct RegulatorAdj { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { for ( const auto uid : core.GetLocalJumpTable().MatchRaw(inst.tag) ) { core.GetLocalJumpTable().AdjRegulator( diff --git a/include/sgpl/operations/flow_local/RegulatorDecay.hpp b/include/sgpl/operations/flow_local/RegulatorDecay.hpp index d0fc8ee5..0874a588 100644 --- a/include/sgpl/operations/flow_local/RegulatorDecay.hpp +++ b/include/sgpl/operations/flow_local/RegulatorDecay.hpp @@ -25,7 +25,7 @@ struct RegulatorDecay { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { for ( const auto uid : core.GetLocalJumpTable().MatchRaw(inst.tag) ) { core.GetLocalJumpTable().DecayRegulator( diff --git a/include/sgpl/operations/flow_local/RegulatorGet.hpp b/include/sgpl/operations/flow_local/RegulatorGet.hpp index a8172d7a..b9c86e44 100644 --- a/include/sgpl/operations/flow_local/RegulatorGet.hpp +++ b/include/sgpl/operations/flow_local/RegulatorGet.hpp @@ -24,7 +24,7 @@ struct RegulatorGet { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { for ( const auto uid : core.GetLocalJumpTable().MatchRaw(inst.tag) ) { // (+) values down regulate diff --git a/include/sgpl/operations/flow_local/RegulatorSet.hpp b/include/sgpl/operations/flow_local/RegulatorSet.hpp index de399bd4..7495d39a 100644 --- a/include/sgpl/operations/flow_local/RegulatorSet.hpp +++ b/include/sgpl/operations/flow_local/RegulatorSet.hpp @@ -24,7 +24,7 @@ struct RegulatorSet { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { for ( const auto uid : core.GetLocalJumpTable().MatchRaw(inst.tag) ) { // (+) values down regulate diff --git a/include/sgpl/operations/unary/Decrement.hpp b/include/sgpl/operations/unary/Decrement.hpp index ecd9d417..97b7ec7f 100644 --- a/include/sgpl/operations/unary/Decrement.hpp +++ b/include/sgpl/operations/unary/Decrement.hpp @@ -22,7 +22,7 @@ struct Decrement { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { --core.registers[ inst.args[0] ]; } diff --git a/include/sgpl/operations/unary/Increment.hpp b/include/sgpl/operations/unary/Increment.hpp index 1ef1a157..1f4c97f4 100644 --- a/include/sgpl/operations/unary/Increment.hpp +++ b/include/sgpl/operations/unary/Increment.hpp @@ -22,7 +22,7 @@ struct Increment { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { ++core.registers[ inst.args[0] ]; } diff --git a/include/sgpl/operations/unary/Negate.hpp b/include/sgpl/operations/unary/Negate.hpp index 0e489c64..80b11066 100644 --- a/include/sgpl/operations/unary/Negate.hpp +++ b/include/sgpl/operations/unary/Negate.hpp @@ -22,7 +22,7 @@ struct Negate { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { core.registers[ inst.args[0] ] = -core.registers[ inst.args[0] ]; } diff --git a/include/sgpl/operations/unary/Not.hpp b/include/sgpl/operations/unary/Not.hpp index 2e887eb2..4e7718c2 100644 --- a/include/sgpl/operations/unary/Not.hpp +++ b/include/sgpl/operations/unary/Not.hpp @@ -22,7 +22,7 @@ struct Not { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { core.registers[ inst.args[0] ] = !core.registers[ inst.args[0] ]; } diff --git a/include/sgpl/operations/unary/RandomBool.hpp b/include/sgpl/operations/unary/RandomBool.hpp index 7c7bb7e5..eb86f6bd 100644 --- a/include/sgpl/operations/unary/RandomBool.hpp +++ b/include/sgpl/operations/unary/RandomBool.hpp @@ -18,7 +18,7 @@ namespace sgpl { class RandomBool { template - static double map_to_unit( const typename Spec::tag_t& tag ) { + static double map_to_unit( const typename Spec::tag_t& tag ) noexcept { constexpr double max_double = Spec::tag_t::MaxDouble(); @@ -34,7 +34,7 @@ class RandomBool { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { const double p = map_to_unit( inst.tag ); diff --git a/include/sgpl/operations/unary/RandomDraw.hpp b/include/sgpl/operations/unary/RandomDraw.hpp index 02326e12..4ceedd0c 100644 --- a/include/sgpl/operations/unary/RandomDraw.hpp +++ b/include/sgpl/operations/unary/RandomDraw.hpp @@ -18,7 +18,9 @@ namespace sgpl { class RandomDraw { template - static double map_between_plusminus_one( const typename Spec::tag_t& tag ) { + static double map_between_plusminus_one( + const typename Spec::tag_t& tag + ) noexcept { constexpr double max = 1.0; constexpr double min = -1.0; @@ -29,7 +31,9 @@ class RandomDraw { } template - static bool is_odd(const typename Spec::tag_t& tag) { return tag.Get(0); } + static bool is_odd(const typename Spec::tag_t& tag) noexcept { + return tag.Get(0); + } static double map_up( const double plusminus_unit_val ) { emp_assert( plusminus_unit_val != 0 ); @@ -41,7 +45,7 @@ class RandomDraw { } template - static float map_draw() { + static float map_draw() noexcept { const typename Spec::tag_t tag( sgpl::tlrand.Get() ); return is_odd( tag ) ? map_up( map_between_plusminus_one(tag) ) @@ -57,7 +61,7 @@ class RandomDraw { const sgpl::Instruction& inst, const sgpl::Program&, typename Spec::peripheral_t& - ) { + ) noexcept { core.registers[ inst.args[0] ] = map_draw(); } diff --git a/include/sgpl/operations/unary/Terminal.hpp b/include/sgpl/operations/unary/Terminal.hpp index f591ef59..1204c3d7 100644 --- a/include/sgpl/operations/unary/Terminal.hpp +++ b/include/sgpl/operations/unary/Terminal.hpp @@ -17,7 +17,9 @@ namespace sgpl { class Terminal { template - static double map_between_plusminus_one( const typename Spec::tag_t& tag ) { + static double map_between_plusminus_one( + const typename Spec::tag_t& tag + ) noexcept { constexpr double max = 1.0; constexpr double min = -1.0; @@ -28,9 +30,11 @@ class Terminal { } template - static bool is_odd(const typename Spec::tag_t& tag) { return tag.Get(0); } + static bool is_odd(const typename Spec::tag_t& tag) noexcept { + return tag.Get(0); + } - static double map_up( const double plusminus_unit_val ) { + static double map_up( const double plusminus_unit_val ) noexcept { emp_assert( plusminus_unit_val != 0 ); return 1.0 / ( @@ -40,7 +44,7 @@ class Terminal { } template - static float map_tag( const typename Spec::tag_t& tag ) { + static float map_tag( const typename Spec::tag_t& tag ) noexcept { return is_odd( tag ) ? map_up( map_between_plusminus_one(tag) ) : map_between_plusminus_one( tag ) diff --git a/include/sgpl/program/Instruction.hpp b/include/sgpl/program/Instruction.hpp index 30a99144..2ec0164d 100644 --- a/include/sgpl/program/Instruction.hpp +++ b/include/sgpl/program/Instruction.hpp @@ -56,9 +56,9 @@ struct Instruction { if ( !library_t::IsAnchorOpCode( op_code ) ) NopOut(); } - bool IsNop() const { return library_t::IsNopOpCode( op_code ); } + bool IsNop() const noexcept { return library_t::IsNopOpCode( op_code ); } - bool IsOp() const { return !IsNop(); } + bool IsOp() const noexcept { return !IsNop(); } bool operator==(const Instruction& other) const { return ( @@ -76,7 +76,7 @@ struct Instruction { std::string GetOpName() const { return library_t::GetOpName(op_code); } - const tag_t& GetTag() const { return tag; } + const tag_t& GetTag() const noexcept { return tag; } auto GetDescriptors() const { return library_t::template GetOpDescriptors< Instruction >(op_code, *this); From 55989e41e25d33445ed0857d08632f4d21a36db9 Mon Sep 17 00:00:00 2001 From: "mmore500.login+git@gmail.com" Date: Tue, 4 May 2021 19:48:32 -0400 Subject: [PATCH 010/113] Bump conduit pin for noexcept --- third-party/conduit | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/third-party/conduit b/third-party/conduit index 81713ec3..9dd5fa0f 160000 --- a/third-party/conduit +++ b/third-party/conduit @@ -1 +1 @@ -Subproject commit 81713ec3d292138c012d3dedde3bbee1c6471450 +Subproject commit 9dd5fa0fdc2fba752dcee4f42558b7b0a25310da From d94562f26373cd31add4f4d52b6cdcb9ebbc33b1 Mon Sep 17 00:00:00 2001 From: Matthew Andres Moreno Date: Wed, 5 May 2021 23:38:17 -0400 Subject: [PATCH 011/113] Remove redundant installs --- Dockerfile | 31 ------------------------------- 1 file changed, 31 deletions(-) diff --git a/Dockerfile b/Dockerfile index 8b81c796..61757782 100644 --- a/Dockerfile +++ b/Dockerfile @@ -4,42 +4,11 @@ USER root COPY . /opt/signalgp-lite -RUN \ - apt-get install -qq \ - python-virtualenv \ - python3-virtualenv \ - python-pip \ - python3-pip \ - doxygen \ - && \ - echo "installed docs dependencies" - RUN \ pip3 install -r /opt/signalgp-lite/docs/requirements.txt \ && \ echo "installed documentation build requirements" -RUN \ - add-apt-repository -y ppa:ubuntu-toolchain-r/test \ - && \ - wget -O - https://apt.llvm.org/llvm-snapshot.gpg.key | apt-key add - \ - && \ - apt-add-repository "deb https://apt.llvm.org/xenial/ llvm-toolchain-xenial-7 main" \ - && \ - apt-get update -qq \ - && \ - apt-get install -y cmake build-essential python-virtualenv python-pip nodejs tar gzip libclang-7-dev llvm-7 clang-7 libstdc++-7-dev gdb \ - && \ - update-alternatives --install /usr/bin/clang++ clang++ /usr/bin/clang++-7 90 \ - && \ - update-alternatives --install /usr/bin/llvm-config llvm-config /usr/bin/llvm-config-7 90 \ - && \ - update-alternatives --install /usr/bin/llvm-profdata llvm-profdata /usr/bin/llvm-profdata-7 90 \ - && \ - update-alternatives --install /usr/bin/llvm-cov llvm-cov /usr/bin/llvm-cov-7 90 \ - && \ - echo "installed force cover build dependencies" - # make sure unprivileged user has access to new files in opt # adapted from https://stackoverflow.com/a/27703359 # and https://superuser.com/a/235398 From cf8ebafa9dace62d45d3ebfa889332217a9bb418 Mon Sep 17 00:00:00 2001 From: Matthew Andres Moreno Date: Wed, 5 May 2021 23:39:06 -0400 Subject: [PATCH 012/113] Pin to specific Conduit image --- Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Dockerfile b/Dockerfile index 61757782..749e99a6 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,4 +1,4 @@ -FROM mmore500/conduit +FROM mmore500/conduit@sha256:b1590176a947b3d3887470c3dee6d81243c7f05c9f81388bffd67b903586037f USER root From d204518de0070f5a7ce7c0cee8e0e8fe20752ca4 Mon Sep 17 00:00:00 2001 From: "mmore500.login+git@gmail.com" Date: Wed, 12 May 2021 21:36:05 -0400 Subject: [PATCH 013/113] Bump Emprical pin Fixes empty video output --- third-party/conduit | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/third-party/conduit b/third-party/conduit index 9dd5fa0f..85532cf9 160000 --- a/third-party/conduit +++ b/third-party/conduit @@ -1 +1 @@ -Subproject commit 9dd5fa0fdc2fba752dcee4f42558b7b0a25310da +Subproject commit 85532cf930b7f9c9f259b689ff141c21569a98bb From f99db5d8044a8a1c2dfa009fb903ac4f96fefe09 Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Tue, 18 May 2021 12:15:51 -0400 Subject: [PATCH 014/113] Cite Alex's paper --- joss/paper.bib | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/joss/paper.bib b/joss/paper.bib index b97bc1e8..5e285caa 100644 --- a/joss/paper.bib +++ b/joss/paper.bib @@ -57,3 +57,11 @@ @misc{fidgit journal = {GitHub repository}, url = {https://github.com/arfon/fidgit} } + +@inproceedings{lalejini2018evolving, + title={Evolving event-driven programs with SignalGP}, + author={Lalejini, Alexander and Ofria, Charles}, + booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, + pages={1135--1142}, + year={2018} +} From 88f4ca5cb59b79682c2dddc14cc607f5cda633b2 Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Tue, 18 May 2021 12:16:04 -0400 Subject: [PATCH 015/113] Cite dishtiny papers --- joss/paper.bib | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/joss/paper.bib b/joss/paper.bib index 5e285caa..0732afb2 100644 --- a/joss/paper.bib +++ b/joss/paper.bib @@ -65,3 +65,23 @@ @inproceedings{lalejini2018evolving pages={1135--1142}, year={2018} } + +@misc{moreno2021exploring, + title={Exploring Evolved Multicellular Life Histories in a Open-Ended Digital Evolution System}, + author={Matthew Andres Moreno and Charles Ofria}, + year={2021}, + eprint={2104.10081}, + archivePrefix={arXiv}, + primaryClass={q-bio.PE} +} + +@article{moreno2019toward, + title={Toward open-ended fraternal transitions in individuality}, + author={Moreno, Matthew Andres and Ofria, Charles}, + journal={Artificial life}, + volume={25}, + number={2}, + pages={117--133}, + year={2019}, + publisher={MIT Press} +} From c5221ad97799d8bfa7fea7d3109c3ccfdbe2b643 Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Tue, 18 May 2021 12:16:21 -0400 Subject: [PATCH 016/113] Add result titles to paper --- joss/paper.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/joss/paper.md b/joss/paper.md index 0009385f..86ac7943 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -81,6 +81,12 @@ You can also use plain \LaTeX for equations \end{equation} and refer to \autoref{eq:fourier} from text. +# Results + +## Replication Results + +## Benchmarking Results + # Citations Citations to entries in paper.bib should be in From d5049751f537f10fbd0cd24ee58f7a13851ab372 Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Tue, 18 May 2021 12:23:28 -0400 Subject: [PATCH 017/113] Add figures to joss folder --- ...gx-lineplot+x=update+y=max-fitness+ext=.png | Bin 0 -> 156468 bytes ...gx-lineplot+x=update+y=max-fitness+ext=.png | Bin 0 -> 143782 bytes .../viz=boxplot+x=k+y=solution-time+ext=.png | Bin 0 -> 42225 bytes ...gx-lineplot+x=update+y=max-fitness+ext=.png | Bin 0 -> 52708 bytes ...=tee-swarmplot+x=k+y=solution-time+ext=.png | Bin 0 -> 197718 bytes 5 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 joss/figures/ci=sd+hue=k+viz=logx-lineplot+x=update+y=max-fitness+ext=.png create mode 100644 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e1392010d2167df726b23dea1de2cb341d67773d Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Tue, 18 May 2021 12:44:45 -0400 Subject: [PATCH 019/113] Add two figures to paper --- joss/paper.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/joss/paper.md b/joss/paper.md index 86ac7943..d2f06590 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -110,6 +110,11 @@ and referenced from text using \autoref{fig:example}. Figure sizes can be customized by adding an optional second parameter: ![Caption for example figure.](figure.png){ width=20% } --> +![Maximum fitness wrt. updates, with standard deviation confidence intervals.\nThis is because, due to large number of datapoints, computing 95% CI takes a non-insignificant amount of time.\label{fig:max-fitness-sd}](figures/max-fitness-sd.png) + +![Filtered maximum fitness wrt. updates, with 95% confidence intervals.\nData has been filtered logarithmically,\label{fig:max-fitness-sd}](figures/max-fitness-log2.png) + + # Acknowledgements We acknowledge contributions from Brigitta Sipocz, Syrtis Major, and Semyeong From 4249581885a31a549f439208c41fb4f6393a6a10 Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Tue, 18 May 2021 14:21:20 -0400 Subject: [PATCH 020/113] Remove newlines in picture figures --- joss/paper.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index d2f06590..60a9ec83 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -110,9 +110,9 @@ and referenced from text using \autoref{fig:example}. Figure sizes can be customized by adding an optional second parameter: ![Caption for example figure.](figure.png){ width=20% } --> -![Maximum fitness wrt. updates, with standard deviation confidence intervals.\nThis is because, due to large number of datapoints, computing 95% CI takes a non-insignificant amount of time.\label{fig:max-fitness-sd}](figures/max-fitness-sd.png) +![Maximum fitness wrt. updates, with standard deviation confidence intervals. This is because, due to large number of datapoints, computing 95% CI takes a non-insignificant amount of time.\label{fig:max-fitness-sd}](figures/max-fitness-sd.png) -![Filtered maximum fitness wrt. updates, with 95% confidence intervals.\nData has been filtered logarithmically,\label{fig:max-fitness-sd}](figures/max-fitness-log2.png) +![Filtered maximum fitness wrt. updates, with 95% confidence intervals. Data has been filtered logarithmically,\label{fig:max-fitness-sd}](figures/max-fitness-log2.png) # Acknowledgements From 68adb43b41e9539a3fcc5c2215a25896fefcb975 Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Wed, 19 May 2021 14:35:50 -0400 Subject: [PATCH 021/113] Fix paper header --- joss/paper.md | 32 +++++++++----------------------- 1 file changed, 9 insertions(+), 23 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index 60a9ec83..bf9b881f 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -1,33 +1,19 @@ --- -title: 'Gala: A Python package for galactic dynamics' +title: 'signalgp-lite: A genetic programming implementation designed for large-scale artificial life applications' tags: - - Python - - astronomy - - dynamics - - galactic dynamics - - milky way + - C++ + - artificial life + - genetic programming + - event driven authors: - - name: Adrian M. Price-Whelan^[Custom footnotes for e.g. denoting who the corresponding author is can be included like this.] + - name: Matthew Andres Moreno orcid: 0000-0003-0872-7098 - affiliation: "1, 2" # (Multiple affiliations must be quoted) - - name: Author Without ORCID - affiliation: 2 - - name: Author with no affiliation - affiliation: 3 + affiliation: 1 affiliations: - - name: Lyman Spitzer, Jr. Fellow, Princeton University + - name: Michigan State University index: 1 - - name: Institution Name - index: 2 - - name: Independent Researcher - index: 3 -date: 13 August 2017 +date: 31 May 2021 bibliography: paper.bib - -# Optional fields if submitting to a AAS journal too, see this blog post: -# https://blog.joss.theoj.org/2018/12/a-new-collaboration-with-aas-publishing -aas-doi: 10.3847/xxxxx <- update this with the DOI from AAS once you know it. -aas-journal: Astrophysical Journal <- The name of the AAS journal. --- # Summary From 28ffcbddb02c523879ea81d2299a9b5dd63ab985 Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Wed, 19 May 2021 15:16:29 -0400 Subject: [PATCH 022/113] Add Matthew's ORCID --- joss/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss/paper.md b/joss/paper.md index bf9b881f..274c8282 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -7,7 +7,7 @@ tags: - event driven authors: - name: Matthew Andres Moreno - orcid: 0000-0003-0872-7098 + orcid: 0000-0003-4726-4479 affiliation: 1 affiliations: - name: Michigan State University From b419317303d04208acf6e32602523c8a8bc31beb Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Mon, 24 May 2021 11:42:03 -0400 Subject: [PATCH 023/113] More changes --- joss/paper.md | 18 +++++++----------- 1 file changed, 7 insertions(+), 11 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index 274c8282..c6660d6b 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -16,17 +16,9 @@ date: 31 May 2021 bibliography: paper.bib --- -# Summary - -The forces on stars, galaxies, and dark matter under external gravitational -fields lead to the dynamical evolution of structures in the universe. The orbits -of these bodies are therefore key to understanding the formation, history, and -future state of galaxies. The field of "galactic dynamics," which aims to model -the gravitating components of galaxies to study their structure and evolution, -is now well-established, commonly taught, and frequently used in astronomy. -Aside from toy problems and demonstrations, the majority of problems require -efficient numerical tools, many of which require the same base code (e.g., for -performing numerical orbit integration). +# Abstract + +Event-driven genetic programming has been shown to outperform traditional methods of # Statement of need @@ -71,6 +63,10 @@ and refer to \autoref{eq:fourier} from text. ## Replication Results +Three experiments from the original SignalGP paper were replicated [1]. +The first one (the "Changing Enviroment" problem) consisted of K number of enviromental signals (2, 4, 8, 16) that organisms had to learn to call a unique signaling instruction for. signalgp-lite managed 100% of the results, even suprassing SignalGP in signal reproduction in the K=16 case (100% vs 32% average). +This is hypothesised to be due to a difference in how mutation parameters are defined and not due to an implementation difference.zoo + ## Benchmarking Results # Citations From ac1632d4c54334ef320e24f8ea2fd2d9d20135b4 Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Mon, 24 May 2021 12:26:51 -0400 Subject: [PATCH 024/113] Finish replication results --- joss/paper.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/joss/paper.md b/joss/paper.md index c6660d6b..153a3fd2 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -9,6 +9,9 @@ authors: - name: Matthew Andres Moreno orcid: 0000-0003-4726-4479 affiliation: 1 + - name: Santiago Rodriguez Papa + orcid: 0000-0002-6028-2105 + affiliation: 1 affiliations: - name: Michigan State University index: 1 From 0c34930b463d2e719f46195d28d883734056d2f9 Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Mon, 24 May 2021 13:10:44 -0400 Subject: [PATCH 025/113] Outline abstract --- joss/paper.md | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index 153a3fd2..9ce65def 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -21,7 +21,12 @@ bibliography: paper.bib # Abstract -Event-driven genetic programming has been shown to outperform traditional methods of +* Event-driven genetic programming has been shown to outperform traditional traditional imperative representations on signal-heavy problems. +* SignalGP is an existing implementation that is focused on extending tag-based referencing by applying event-driven techiques. +* Our goal is to provide a performance benefit in large-scale experiments (such as those undertaken in Artificial Life). +* By removing some of the GP related overhead (call stack) and trading runtime configurability for compile-time optimization, we achieved a XXX% speedup. +* The software is unit- and integration-tested, achieving equivalent solution quality on benchmark problems originally developed for SignalGP. +* signalgp-lite is currently used in dishtiny, and our intention is for it to be useful in other Artifical Life projects. # Statement of need @@ -67,8 +72,12 @@ and refer to \autoref{eq:fourier} from text. ## Replication Results Three experiments from the original SignalGP paper were replicated [1]. -The first one (the "Changing Enviroment" problem) consisted of K number of enviromental signals (2, 4, 8, 16) that organisms had to learn to call a unique signaling instruction for. signalgp-lite managed 100% of the results, even suprassing SignalGP in signal reproduction in the K=16 case (100% vs 32% average). -This is hypothesised to be due to a difference in how mutation parameters are defined and not due to an implementation difference.zoo +The first one (the "Changing Enviroment problem") consisted of K number of enviromental signals (2, 4, 8, 16) that organisms had to learn to call a unique signaling instruction for. signalgp-lite managed 100% of the results, even suprassing SignalGP in signal reproduction in the K=16 case (100% vs 32% average). +This is hypothesised to be due to a difference in how mutation parameters are defined and not due to an implementation difference. +The second experiment (the "Contextual-Signal problem") involves evolving programs capable of responding to a signal depending on what signal they previously detected. +A total of 16 input signal pairs and 4 response signals were tested. +The input signal pairs were assigned +You can refer to table 2 in the original SignalGP problem for a visual representation of this sequence. ## Benchmarking Results From 350b7520ad8f523871ad9f4668169cd3a5becd9c Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Tue, 25 May 2021 11:34:48 -0400 Subject: [PATCH 026/113] Add summary and statement of need --- joss/paper.md | 49 +++++++++++++------------------------------------ 1 file changed, 13 insertions(+), 36 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index 9ce65def..951ba7ed 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -28,44 +28,21 @@ bibliography: paper.bib * The software is unit- and integration-tested, achieving equivalent solution quality on benchmark problems originally developed for SignalGP. * signalgp-lite is currently used in dishtiny, and our intention is for it to be useful in other Artifical Life projects. +# Summary + +* signalgp-lite allows is a lightweight genetic programming framework that allows for event-based development. +* event-based programming has been shown to outperform imperative programming on certain problems, such as those in which many interactions with the enviroment or other organisms occur. +* this allows for a better modeling of artificial life simulations, among other things. +* signalgp-lite offers a speedup over the original event-driven framework (SignalGP) by forgoing some runtime-customizability. + # Statement of need -`Gala` is an Astropy-affiliated Python package for galactic dynamics. Python -enables wrapping low-level languages (e.g., C) for speed without losing -flexibility or ease-of-use in the user-interface. The API for `Gala` was -designed to provide a class-based and user-friendly interface to fast (C or -Cython-optimized) implementations of common operations such as gravitational -potential and force evaluation, orbit integration, dynamical transformations, -and chaos indicators for nonlinear dynamics. `Gala` also relies heavily on and -interfaces well with the implementations of physical units and astronomical -coordinate systems in the `Astropy` package [@astropy] (`astropy.units` and -`astropy.coordinates`). - -`Gala` was designed to be used by both astronomical researchers and by -students in courses on gravitational dynamics or astronomy. It has already been -used in a number of scientific publications [@Pearson:2017] and has also been -used in graduate courses on Galactic dynamics to, e.g., provide interactive -visualizations of textbook material [@Binney:2008]. The combination of speed, -design, and support for Astropy functionality in `Gala` will enable exciting -scientific explorations of forthcoming data releases from the *Gaia* mission -[@gaia] by students and experts alike. - -# Mathematics - -Single dollars ($) are required for inline mathematics e.g. $f(x) = e^{\pi/x}$ - -Double dollars make self-standing equations: - -$$\Theta(x) = \left\{\begin{array}{l} -0\textrm{ if } x < 0\cr -1\textrm{ else} -\end{array}\right.$$ - -You can also use plain \LaTeX for equations -\begin{equation}\label{eq:fourier} -\hat f(\omega) = \int_{-\infty}^{\infty} f(x) e^{i\omega x} dx -\end{equation} -and refer to \autoref{eq:fourier} from text. +* signalgp-lite is a C++ library for event-driven genetic programming. C++ was chosen due to its low-level customizability as well as runtime performance. Most alife packages are written in C++. +* signalgp-lite fills a niche for event-driven applications that do not require a high level of customizability at runtime, but instead benefit from a considerable speed-up. +* Despite being able to simulate evolution at scales that greately exceed real-life experiements, the performance of artifical life simulations greatly suffers as population size increases. +* A large population size is required to replicate ecological effects of interest (transition to multicelularity, ??) +* signalgp-lite allows for a considerable speedup in performance, allowing users to replicate a greater variety of alife situations. +* signalgp-lite is the framework of choice in DISHTINY due to its combination of performance, flexibility, and ability to be compiled into javascript. # Results From 7e081f2f9f8b7bf2888f15f3394243e7c4e58617 Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Tue, 25 May 2021 12:24:16 -0400 Subject: [PATCH 027/113] Fix typos --- joss/paper.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index 951ba7ed..b8917801 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -30,7 +30,7 @@ bibliography: paper.bib # Summary -* signalgp-lite allows is a lightweight genetic programming framework that allows for event-based development. +* signalgp-lite is a lightweight genetic programming framework that allows for an event-based development paradigm. * event-based programming has been shown to outperform imperative programming on certain problems, such as those in which many interactions with the enviroment or other organisms occur. * this allows for a better modeling of artificial life simulations, among other things. * signalgp-lite offers a speedup over the original event-driven framework (SignalGP) by forgoing some runtime-customizability. @@ -40,7 +40,7 @@ bibliography: paper.bib * signalgp-lite is a C++ library for event-driven genetic programming. C++ was chosen due to its low-level customizability as well as runtime performance. Most alife packages are written in C++. * signalgp-lite fills a niche for event-driven applications that do not require a high level of customizability at runtime, but instead benefit from a considerable speed-up. * Despite being able to simulate evolution at scales that greately exceed real-life experiements, the performance of artifical life simulations greatly suffers as population size increases. -* A large population size is required to replicate ecological effects of interest (transition to multicelularity, ??) +* A large population size is required to replicate ecological effects of interest (transition to multicelularity, role of rare events in evolution) * signalgp-lite allows for a considerable speedup in performance, allowing users to replicate a greater variety of alife situations. * signalgp-lite is the framework of choice in DISHTINY due to its combination of performance, flexibility, and ability to be compiled into javascript. From f94e99ed5e58c460ae38c0e0cca39558605890fc Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Thu, 27 May 2021 15:40:47 -0400 Subject: [PATCH 028/113] Flesh out paper a bit more --- joss/paper.md | 44 ++++++++++++++++++++++++++------------------ 1 file changed, 26 insertions(+), 18 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index b8917801..a32f85b1 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -21,37 +21,45 @@ bibliography: paper.bib # Abstract -* Event-driven genetic programming has been shown to outperform traditional traditional imperative representations on signal-heavy problems. -* SignalGP is an existing implementation that is focused on extending tag-based referencing by applying event-driven techiques. -* Our goal is to provide a performance benefit in large-scale experiments (such as those undertaken in Artificial Life). -* By removing some of the GP related overhead (call stack) and trading runtime configurability for compile-time optimization, we achieved a XXX% speedup. -* The software is unit- and integration-tested, achieving equivalent solution quality on benchmark problems originally developed for SignalGP. -* signalgp-lite is currently used in dishtiny, and our intention is for it to be useful in other Artifical Life projects. +Event-driven genetic programming has been shown to outperform traditional traditional imperative representations on signal-heavy problems [cite]. +SignalGP is an existing implementation that is focused on extending tag-based referencing by applying event-driven techiques to genetic programming. +With signalgp-lite, our goal is to provide a performance increase in large-scale experiments, such as those undertaken in Artificial Life. +By removing some of the Genetic Programming related overhead (e.g. the call stack) and employing compile-time configuration in exchange of run-time flexibility, we achieved a XXX% speedup. +signalgp-lite is unit- and integration-tested, achieving equivalent solution quality on three benchmark problems originally developed for SignalGP. +The library is currently used in dishtiny, and our expectation is that it will be useful in future Artifical Life projects. # Summary -* signalgp-lite is a lightweight genetic programming framework that allows for an event-based development paradigm. -* event-based programming has been shown to outperform imperative programming on certain problems, such as those in which many interactions with the enviroment or other organisms occur. -* this allows for a better modeling of artificial life simulations, among other things. -* signalgp-lite offers a speedup over the original event-driven framework (SignalGP) by forgoing some runtime-customizability. +signalgp-lite is a lightweight genetic programming framework that allows for an event-based development paradigm. +This development style has been shown to outperform imperative programming on certain problems, such as those in which many interactions with the enviroment or other organisms occur. [cite] +One example of such situation is the case of a large-scale digital evolution experiment, as well as various other artifical life simulations. +signalgp-lite offers a speedup over the original event-driven framework (SignalGP) by forgoing some run-time customizability, such as the ability to xxxxx on the flu=y. # Statement of need -* signalgp-lite is a C++ library for event-driven genetic programming. C++ was chosen due to its low-level customizability as well as runtime performance. Most alife packages are written in C++. -* signalgp-lite fills a niche for event-driven applications that do not require a high level of customizability at runtime, but instead benefit from a considerable speed-up. -* Despite being able to simulate evolution at scales that greately exceed real-life experiements, the performance of artifical life simulations greatly suffers as population size increases. -* A large population size is required to replicate ecological effects of interest (transition to multicelularity, role of rare events in evolution) -* signalgp-lite allows for a considerable speedup in performance, allowing users to replicate a greater variety of alife situations. -* signalgp-lite is the framework of choice in DISHTINY due to its combination of performance, flexibility, and ability to be compiled into javascript. +signalgp-lite is a C++ library for event-driven genetic programming. +C++ was chosen due to its low-level customizability as well as runtime performance. +Due to these features, C++ is the language of choice for most artificial life and genetic programming utilities. +signalgp-lite fills a niche for event-driven applications that do not require a high level of customizability at runtime, but instead benefit from a considerable speed-up. +Despite being able to simulate evolution at scales that greately exceed real-life experiements, the performance of artifical life simulations greatly suffers as population size increases. +A large population size is required to replicate ecological effects of interest, such as the transition to multicelularity, or the role of rare events in evolution. +Our genetic programming libary allows for a considerable speedup in performance, allowing users to replicate a greater variety of artificial life situations. +In light of the above, signalgp-lite is the framework of choice in DISHTINY due to its combination of performance, flexibility, and ability to be compiled into javascript. # Results ## Replication Results Three experiments from the original SignalGP paper were replicated [1]. -The first one (the "Changing Enviroment problem") consisted of K number of enviromental signals (2, 4, 8, 16) that organisms had to learn to call a unique signaling instruction for. signalgp-lite managed 100% of the results, even suprassing SignalGP in signal reproduction in the K=16 case (100% vs 32% average). + +### Changing Enviroment Problem + +The Changing Enviroment Problem consisted of K number of enviromental signals (2, 4, 8, 16) that organisms had to learn to call a unique signaling instruction for. signalgp-lite managed 100% of the results, even suprassing SignalGP in signal reproduction in the K=16 case (100% vs 32% average). This is hypothesised to be due to a difference in how mutation parameters are defined and not due to an implementation difference. -The second experiment (the "Contextual-Signal problem") involves evolving programs capable of responding to a signal depending on what signal they previously detected. + +### Contextual Signal Problem + +The Contextual Signal Problem involves evolving programs capable of responding to a signal depending on what signal they previously detected. A total of 16 input signal pairs and 4 response signals were tested. The input signal pairs were assigned You can refer to table 2 in the original SignalGP problem for a visual representation of this sequence. From 1484dc4be8645a0689bcdcbade42489fe7e31072 Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Thu, 27 May 2021 15:43:23 -0400 Subject: [PATCH 029/113] Make library title stand out --- joss/paper.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index a32f85b1..2102c24b 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -30,21 +30,21 @@ The library is currently used in dishtiny, and our expectation is that it will b # Summary -signalgp-lite is a lightweight genetic programming framework that allows for an event-based development paradigm. +`signalgp-lite` is a lightweight genetic programming framework that allows for an event-based development paradigm. This development style has been shown to outperform imperative programming on certain problems, such as those in which many interactions with the enviroment or other organisms occur. [cite] One example of such situation is the case of a large-scale digital evolution experiment, as well as various other artifical life simulations. -signalgp-lite offers a speedup over the original event-driven framework (SignalGP) by forgoing some run-time customizability, such as the ability to xxxxx on the flu=y. +`signalgp-lite` offers a speedup over the original event-driven framework (SignalGP) by forgoing some run-time customizability, such as the ability to xxxxx on the flu=y. # Statement of need -signalgp-lite is a C++ library for event-driven genetic programming. +`signalgp-lite` is a C++ library for event-driven genetic programming. C++ was chosen due to its low-level customizability as well as runtime performance. Due to these features, C++ is the language of choice for most artificial life and genetic programming utilities. -signalgp-lite fills a niche for event-driven applications that do not require a high level of customizability at runtime, but instead benefit from a considerable speed-up. +`signalgp-lite` fills a niche for event-driven applications that do not require a high level of customizability at runtime, but instead benefit from a considerable speed-up. Despite being able to simulate evolution at scales that greately exceed real-life experiements, the performance of artifical life simulations greatly suffers as population size increases. A large population size is required to replicate ecological effects of interest, such as the transition to multicelularity, or the role of rare events in evolution. Our genetic programming libary allows for a considerable speedup in performance, allowing users to replicate a greater variety of artificial life situations. -In light of the above, signalgp-lite is the framework of choice in DISHTINY due to its combination of performance, flexibility, and ability to be compiled into javascript. +In light of the above, `signalgp-lite` is the framework of choice in DISHTINY due to its combination of performance, flexibility, and ability to be compiled into javascript. # Results @@ -54,7 +54,7 @@ Three experiments from the original SignalGP paper were replicated [1]. ### Changing Enviroment Problem -The Changing Enviroment Problem consisted of K number of enviromental signals (2, 4, 8, 16) that organisms had to learn to call a unique signaling instruction for. signalgp-lite managed 100% of the results, even suprassing SignalGP in signal reproduction in the K=16 case (100% vs 32% average). +The Changing Enviroment Problem consisted of K number of enviromental signals (2, 4, 8, 16) that organisms had to learn to call a unique signaling instruction for. `signalgp-lite` managed 100% of the results, even suprassing SignalGP in signal reproduction in the K=16 case (100% vs 32% average). This is hypothesised to be due to a difference in how mutation parameters are defined and not due to an implementation difference. ### Contextual Signal Problem From 1e7c2040d567e73e03a67545023fdeb079134f2c Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Fri, 28 May 2021 16:10:32 -0400 Subject: [PATCH 030/113] Flesh our more stuff --- joss/paper.md | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index 2102c24b..3a5fbad7 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -54,8 +54,8 @@ Three experiments from the original SignalGP paper were replicated [1]. ### Changing Enviroment Problem -The Changing Enviroment Problem consisted of K number of enviromental signals (2, 4, 8, 16) that organisms had to learn to call a unique signaling instruction for. `signalgp-lite` managed 100% of the results, even suprassing SignalGP in signal reproduction in the K=16 case (100% vs 32% average). -This is hypothesised to be due to a difference in how mutation parameters are defined and not due to an implementation difference. +The Changing Enviroment Problem consisted of K number of enviromental signals (2, 4, 8, 16) that organisms had to learn to call a unique signaling instruction for. `signalgp-lite` managed 100% of the results, even suprassing SignalGP in signal reproduction in the `K=16` case (100% vs 32% average). +This is hypothesised to be caused by differences in how mutation parameters are defined across both libraries, and not due to implementation discrepancies. ### Contextual Signal Problem @@ -91,8 +91,13 @@ Figure sizes can be customized by adding an optional second parameter: ![Maximum fitness wrt. updates, with standard deviation confidence intervals. This is because, due to large number of datapoints, computing 95% CI takes a non-insignificant amount of time.\label{fig:max-fitness-sd}](figures/max-fitness-sd.png) -![Filtered maximum fitness wrt. updates, with 95% confidence intervals. Data has been filtered logarithmically,\label{fig:max-fitness-sd}](figures/max-fitness-log2.png) +![Filtered maximum fitness wrt. updates, with 95% confidence intervals. Data has been filtered logarithmically.\label{fig:max-fitness-sd}](figures/max-fitness-log2.png) +![Time to solution for the Changing Enviroment problem.\label{fig:tts-changing}](figures/solution-time-swarmplot.png) + + + +![Benchmarking results of 20 replicates shown as a times-speedup of wall time.\label{fig:bench-wall}](figures/wall-time-speedup.jpeg) # Acknowledgements From 33b8959189ea0b8d254b3005835ec33a734805da Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Thu, 10 Jun 2021 14:01:11 -0400 Subject: [PATCH 031/113] Add benchmarking section --- joss/paper.md | 58 +++++++++++++++++++++++++++++++++++++++++++-------- 1 file changed, 49 insertions(+), 9 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index 3a5fbad7..29d2f1b3 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -24,16 +24,16 @@ bibliography: paper.bib Event-driven genetic programming has been shown to outperform traditional traditional imperative representations on signal-heavy problems [cite]. SignalGP is an existing implementation that is focused on extending tag-based referencing by applying event-driven techiques to genetic programming. With signalgp-lite, our goal is to provide a performance increase in large-scale experiments, such as those undertaken in Artificial Life. -By removing some of the Genetic Programming related overhead (e.g. the call stack) and employing compile-time configuration in exchange of run-time flexibility, we achieved a XXX% speedup. -signalgp-lite is unit- and integration-tested, achieving equivalent solution quality on three benchmark problems originally developed for SignalGP. -The library is currently used in dishtiny, and our expectation is that it will be useful in future Artifical Life projects. +By removing some of the Genetic Programming related overhead (e.g. the call stack) and employing compile-time configuration in exchange of run-time flexibility, we achieved an 8x to 30x speedup. +`signalgp-lite` is unit- and integration-tested, achieving equivalent solution quality on two benchmark problems originally developed for SignalGP. +The library is currently used in DISHTNY [CITE IT], and our expectation is that it will be useful in future Artifical Life and Genetic Programming projects. # Summary `signalgp-lite` is a lightweight genetic programming framework that allows for an event-based development paradigm. This development style has been shown to outperform imperative programming on certain problems, such as those in which many interactions with the enviroment or other organisms occur. [cite] One example of such situation is the case of a large-scale digital evolution experiment, as well as various other artifical life simulations. -`signalgp-lite` offers a speedup over the original event-driven framework (SignalGP) by forgoing some run-time customizability, such as the ability to xxxxx on the flu=y. +`signalgp-lite` offers a speedup over the original event-driven framework (SignalGP) by forgoing some run-time customizability, such as the ability to xxxxx on the fly. # Statement of need @@ -54,18 +54,56 @@ Three experiments from the original SignalGP paper were replicated [1]. ### Changing Enviroment Problem -The Changing Enviroment Problem consisted of K number of enviromental signals (2, 4, 8, 16) that organisms had to learn to call a unique signaling instruction for. `signalgp-lite` managed 100% of the results, even suprassing SignalGP in signal reproduction in the `K=16` case (100% vs 32% average). -This is hypothesised to be caused by differences in how mutation parameters are defined across both libraries, and not due to implementation discrepancies. +The Changing Enviroment Problem consisted of K number of enviromental signals (2, 4, 8, 16) that organisms had to learn to call a unique signaling instruction for. +Figure \autoref{fig:tts-context} shows the number of generations before a full solution was found. +`signalgp-lite` achieved a 100% replication rate, even suprassing SignalGP in signal reproduction in the `K=16` case [figure X cited in Alex's paper] (100% vs 32% average). +This is hypothesised to be caused by differences in how mutation parameters are defined across both libraries, and not due to implementation discrepancies in the simulation. ### Contextual Signal Problem -The Contextual Signal Problem involves evolving programs capable of responding to a signal depending on what signal they previously detected. +The Contextual Signal Problem involves evolving programs capable of responding to a signal depending on what signal they previously encountered. A total of 16 input signal pairs and 4 response signals were tested. -The input signal pairs were assigned -You can refer to table 2 in the original SignalGP problem for a visual representation of this sequence. +The unordered input signal pairs were assigned a response signal that then had to be replicated by the organism. +You can refer to table 2 in the original SignalGP problem for a visual representation of this sequence. [cite it] +TODO: explain how `signalgp-lite` compared in this problem. ## Benchmarking Results +The main goal of this library is achieving fast execution of genetic programming problems compared to previous solutions. +As such, benchmarking is essential to determining whether `signalgp-lite`'s optimizations work as intended. + +Memory access time and caching are important factors when it comes to performance. [cite?] +In order to determine how the libary performs are different caching levels, benchmarks were performed over a variety of memory usages, which were simulated via the different agent counts. \autoref{fig:bench-wall} +Wall times were measured using Google Benchmark version x.y.z. + +Each of the five treatments serve a different purpose. + +The control ensures our benchmarking process is valid. +While there is some uncertainty inherent to the benchmarking enviroment, the near-zero wall speedup means that our results are not directly affected by the chosen library, and that any resulting difference will stem from implementation choices. +As such, our analysis can be considered fair. + +Benchmarking the NOP instruction alone isolates the performance impact of executing `signalgp-lite`'s byte-code interpreter compared to SignalGP's lamda-based instructions. +We observe a 10 to 30 times speedup based on this change. + +The arithmetic benchmark adds to the prior improvements the performance effect of `signalgp-lite`'s registers being fixed-length arrays compared to SignalGP variable-length vectors. [TODO: FIX THIS SENTENCE] +This compares the effects of compile-time optimizations versus the runtime flexibility of being able to change the number of registers on the fly. +This trade-off results in a 20 to 50 times speed-up. + +* test complex control flow within modules and in between modules +* signalgp-lite doesnt have a function stack like signal-gp +* inner while loops are implemented in terms of jump instrunctions +* we wanted to test this both with and without regulation +* because with regulation, tag matching causes caching to not work as well which can affect performance + +The complete benchmark adds tag matching and complex control flow to the prior benchmarks' instruction set. +Tag-based referencing is used to toggle module activation. +This in turn allows for complex regulation mechanics and control flow. +Given the compile-time-friendly nature of `signalgp-lite`'s instructions, we can observe a 30 to 55 times speed-up compared to the vanilla SignalGP lambdas. + +Finally, sans_regulation measures the previous instruction set without regulation-specific instructions ([list them here]). +This results in a 35 to 47 times speedup. +We can conclude that `signalgp-lite` offers significant performance improvements even on simulations that do not heavily depend on regulation. + # Citations Citations to entries in paper.bib should be in @@ -95,6 +133,8 @@ Figure sizes can be customized by adding an optional second parameter: ![Time to solution for the Changing Enviroment problem.\label{fig:tts-changing}](figures/solution-time-swarmplot.png) +![Time to solution for the Contextual Signal problem.\label{fig:tts-context}](figures/solution-time-contextual-signal.png) + ![Benchmarking results of 20 replicates shown as a times-speedup of wall time.\label{fig:bench-wall}](figures/wall-time-speedup.jpeg) From d6d1be7a5a671213166be921d7b3b23d2764a28d Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Thu, 10 Jun 2021 14:01:34 -0400 Subject: [PATCH 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zAh`{0>8E1Dk}e?2*ec2qyXY!ZCtM)n$c$BI88Y!;YJVFSwo?#w`j-mWsTQR^4vpiD z88aI6wO5$}`aRmo2Ot8i9`;#|)Z3i#CAX@3pkB;oqiN?xwz82ZW|4Qn9B@SBr=a)l zvJV`+kxV4Fy3A);5nVNdgCehA#czr|t0UQ0S(@*O74bTV(C^uOIo1Dzi@9KD30t7q zyS-s90hBmj!6=w6ffwiABd*Qddw|NV;_* zdBUa%*NFaX_AgAgwdZsm4KUjx;l16l0x+|yfuldl^AIFe?j+>kIb&L(HeHv*P=sw} zAFb(02XGGeQo1HJ8+P947#Pa7zxN9N3`$|Mgaa*vDUqRG>b z6Y;yKT6t%r6TMqBvpf4QoL|6Cu*#g5+#Y1aHU;^F2GOJ~r2yO;S3tuJEg9+zo~lNS z#u$`(UVDjU1#Yv>yb9Xk2s?)J%9|rH1%2B0xTw6qw&KklcQ^Z^vDBrgseO(Cuq+_G-l@O`b#hzOCF+ErHZpmFN?QBn>EV9dmmq$J&;jt|9)bmP-wgM=F30@ zc0?QA#ZpJoy8ZxeV8-(NZkQ+}N~{IGeJ6pfysm(i04p-caDEmXPmZ2DRLHY<3hiD* z<831iJqZ{h@5RtR)X8hy-<{o1(IAhnOS$IX;B2?5=U#ms9P~3-cfycMu+!@W1aM#u8%5=Aua4@P+VsFK>di%$6u!4 WSJq=6|NJ)VdYk^=Y*Xr`PyYvj#n6KQ literal 0 HcmV?d00001 From d2cc06060f426d09468511e3ce991bb75380f4de Mon Sep 17 00:00:00 2001 From: "mmore500.login+git@gmail.com" Date: Mon, 24 May 2021 17:51:20 -0400 Subject: [PATCH 033/113] Bump conduit pin --- third-party/conduit | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/third-party/conduit b/third-party/conduit index 85532cf9..2eb5d77f 160000 --- a/third-party/conduit +++ b/third-party/conduit @@ -1 +1 @@ -Subproject commit 85532cf930b7f9c9f259b689ff141c21569a98bb +Subproject commit 2eb5d77f0feb9a0ef6d7cc3f9aafc402cd7e4006 From 6d073bb78cc1e80fe026f202e9915b948b98c555 Mon Sep 17 00:00:00 2001 From: "mmore500.login+git@gmail.com" Date: Tue, 25 May 2021 23:15:03 -0400 Subject: [PATCH 034/113] Bump conduit pin --- third-party/conduit | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/third-party/conduit b/third-party/conduit index 2eb5d77f..da777d63 160000 --- a/third-party/conduit +++ b/third-party/conduit @@ -1 +1 @@ -Subproject commit 2eb5d77f0feb9a0ef6d7cc3f9aafc402cd7e4006 +Subproject commit da777d63e49de6f18808ad70c2326e20dd0cc667 From 1dc95ae44bab099edf592693518833200cea28f5 Mon Sep 17 00:00:00 2001 From: "mmore500.login+git@gmail.com" Date: Tue, 25 May 2021 23:15:40 -0400 Subject: [PATCH 035/113] Mark script executable --- ci/grep_dotos.sh | 0 1 file changed, 0 insertions(+), 0 deletions(-) mode change 100644 => 100755 ci/grep_dotos.sh diff --git a/ci/grep_dotos.sh b/ci/grep_dotos.sh old mode 100644 new mode 100755 From 301359315d39cca20117baeedd0a91013e98cb48 Mon Sep 17 00:00:00 2001 From: Matthew Andres Moreno Date: Wed, 26 May 2021 11:09:47 -0400 Subject: [PATCH 036/113] Add codacy badge --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 1efbd7d0..b48f8f05 100644 --- a/README.md +++ b/README.md @@ -2,6 +2,7 @@ # signalgp-lite [![version](https://img.shields.io/endpoint?url=https%3A%2F%2Fmmore500.github.io%2Fsignalgp-lite%2Fversion-badge.json)](https://github.com/mmore500/signalgp-lite/releases) +[![Codacy Badge](https://app.codacy.com/project/badge/Grade/d4ec421f786a4d00bd850bf0d1719d5d)](https://www.codacy.com/gh/mmore500/signalgp-lite/dashboard?utm_source=github.com&utm_medium=referral&utm_content=mmore500/signalgp-lite&utm_campaign=Badge_Grade) [![continuous integration](https://github.com/mmore500/signalgp-lite/workflows/CI/badge.svg)](https://github.com/mmore500/signalgp-lite/actions?query=workflow%3ACI) [![Documentation Status](https://readthedocs.org/projects/signalgp-lite/badge/?version=latest)](https://signalgp-lite.readthedocs.io/en/latest/?badge=latest) [![documentation coverage](https://img.shields.io/endpoint?url=https%3A%2F%2Fmmore500.github.io%2Fsignalgp-lite%2Fdocumentation-coverage-badge.json)](https://signalgp-lite.readthedocs.io/en/latest/) From 1258004cea1d54c38a424d3302f3adda6dff3c6f Mon Sep 17 00:00:00 2001 From: "mmore500.login+git@gmail.com" Date: Fri, 28 May 2021 17:34:02 -0400 Subject: [PATCH 037/113] Mark scripts executable --- microbenchmarks/slurm/job.sh | 0 microbenchmarks/slurm/nnode=1+nproc=8+ext=.slurm.sh | 0 microbenchmarks/slurm/nnode=8+nproc=1+ext=.slurm.sh | 0 3 files changed, 0 insertions(+), 0 deletions(-) mode change 100644 => 100755 microbenchmarks/slurm/job.sh mode change 100644 => 100755 microbenchmarks/slurm/nnode=1+nproc=8+ext=.slurm.sh mode change 100644 => 100755 microbenchmarks/slurm/nnode=8+nproc=1+ext=.slurm.sh diff --git a/microbenchmarks/slurm/job.sh b/microbenchmarks/slurm/job.sh old mode 100644 new mode 100755 diff --git a/microbenchmarks/slurm/nnode=1+nproc=8+ext=.slurm.sh b/microbenchmarks/slurm/nnode=1+nproc=8+ext=.slurm.sh old mode 100644 new mode 100755 diff --git a/microbenchmarks/slurm/nnode=8+nproc=1+ext=.slurm.sh b/microbenchmarks/slurm/nnode=8+nproc=1+ext=.slurm.sh old mode 100644 new mode 100755 From 1a8d6776f7405b2cf352061c40e75cf6f4443b2c Mon Sep 17 00:00:00 2001 From: "mmore500.login+git@gmail.com" Date: Fri, 28 May 2021 17:34:12 -0400 Subject: [PATCH 038/113] Bump conduit/Empirical Fixes serialization issues --- third-party/conduit | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/third-party/conduit b/third-party/conduit index da777d63..3d654b3a 160000 --- a/third-party/conduit +++ b/third-party/conduit @@ -1 +1 @@ -Subproject commit da777d63e49de6f18808ad70c2326e20dd0cc667 +Subproject commit 3d654b3aa7f1f21f1f03119c41d28679ac696c2e From 2fc8b594a30ac03d8d47381539707cc7bb8863ce Mon Sep 17 00:00:00 2001 From: "mmore500.login+git@gmail.com" Date: Fri, 28 May 2021 17:58:57 -0400 Subject: [PATCH 039/113] Bump conduit --- third-party/conduit | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/third-party/conduit b/third-party/conduit index 3d654b3a..021b40cc 160000 --- a/third-party/conduit +++ b/third-party/conduit @@ -1 +1 @@ -Subproject commit 3d654b3aa7f1f21f1f03119c41d28679ac696c2e +Subproject commit 021b40ccd366eac2656d5514d5b3ad398ebc630f From d4f8bf74b9bc1d635a0d524402b28d3abac8eec2 Mon Sep 17 00:00:00 2001 From: "mmore500.login+git@gmail.com" Date: Fri, 28 May 2021 19:12:33 -0400 Subject: [PATCH 040/113] Patch Empirical --- third-party/conduit | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/third-party/conduit b/third-party/conduit index 021b40cc..bf982317 160000 --- a/third-party/conduit +++ b/third-party/conduit @@ -1 +1 @@ -Subproject commit 021b40ccd366eac2656d5514d5b3ad398ebc630f +Subproject commit bf9823170ddea563261970d8d52363f9ab798224 From f3d5021bb000891ccce956034ce0a34caf6cb9bf Mon Sep 17 00:00:00 2001 From: "mmore500.login+git@gmail.com" Date: Fri, 28 May 2021 22:07:49 -0400 Subject: [PATCH 041/113] Patch Empirical --- third-party/conduit | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/third-party/conduit b/third-party/conduit index bf982317..fde0e52f 160000 --- a/third-party/conduit +++ b/third-party/conduit @@ -1 +1 @@ -Subproject commit bf9823170ddea563261970d8d52363f9ab798224 +Subproject commit fde0e52fe0983d460056900253e3cd60230cce20 From 1d9b0116e88d6a52156459c4efa8a916220e1914 Mon Sep 17 00:00:00 2001 From: "mmore500.login+git@gmail.com" Date: Wed, 2 Jun 2021 22:57:41 -0400 Subject: [PATCH 042/113] Bump Empirical pin --- third-party/conduit | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/third-party/conduit b/third-party/conduit index fde0e52f..3ace7d37 160000 --- a/third-party/conduit +++ b/third-party/conduit @@ -1 +1 @@ -Subproject commit fde0e52fe0983d460056900253e3cd60230cce20 +Subproject commit 3ace7d379566fee9cacf6437ce57296123a82526 From d55a15a200803c8f7cefcbb6d4c213c714c70f3b Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Thu, 10 Jun 2021 16:10:38 -0400 Subject: [PATCH 043/113] Add control benchmark --- microbenchmarks/SignalGP/Makefile | 1 + microbenchmarks/SignalGP/_BenchHolder.hpp | 26 ++++++++++++--------- microbenchmarks/signalgp-lite/Makefile | 1 + microbenchmarks/signalgp-lite/benchmark.hpp | 3 ++- 4 files changed, 19 insertions(+), 12 deletions(-) diff --git a/microbenchmarks/SignalGP/Makefile b/microbenchmarks/SignalGP/Makefile index aa7d6bfe..6d203831 100644 --- a/microbenchmarks/SignalGP/Makefile +++ b/microbenchmarks/SignalGP/Makefile @@ -1,5 +1,6 @@ TARGET_NAMES += arithmetic TARGET_NAMES += complete +TARGET_NAMES += control TARGET_NAMES += nop TARGET_NAMES += sans_regulation diff --git a/microbenchmarks/SignalGP/_BenchHolder.hpp b/microbenchmarks/SignalGP/_BenchHolder.hpp index 860e782f..94c7c0aa 100644 --- a/microbenchmarks/SignalGP/_BenchHolder.hpp +++ b/microbenchmarks/SignalGP/_BenchHolder.hpp @@ -2,7 +2,7 @@ #include "_SignalGP.hpp" -template +template struct BenchHolder { std::function make_program; @@ -38,20 +38,24 @@ struct BenchHolder { for (auto _ : state) { // This code gets timed - auto& cpu = collection[agent]; + if constexpr (!Control) { - if constexpr ( fill_cores ) { + auto& cpu = collection[agent]; - while ( cpu.GetUnusedThreadIDs().size() ) { - cpu.SpawnThreadWithTag( tags[tag] ); - ++tag %= tags.size(); + if constexpr ( fill_cores ) { + + while ( cpu.GetUnusedThreadIDs().size() ) { + cpu.SpawnThreadWithTag( tags[tag] ); + ++tag %= tags.size(); + } + cpu.SingleProcess(); + } else { + for (size_t i{}; i < 16; ++i) cpu.SingleProcess(); } - cpu.SingleProcess(); - } else { - for (size_t i{}; i < 16; ++i) cpu.SingleProcess(); - } - ++agent %= NUM_AGENTS; + ++agent %= NUM_AGENTS; + + } } diff --git a/microbenchmarks/signalgp-lite/Makefile b/microbenchmarks/signalgp-lite/Makefile index aa7d6bfe..6d203831 100644 --- a/microbenchmarks/signalgp-lite/Makefile +++ b/microbenchmarks/signalgp-lite/Makefile @@ -1,5 +1,6 @@ TARGET_NAMES += arithmetic TARGET_NAMES += complete +TARGET_NAMES += control TARGET_NAMES += nop TARGET_NAMES += sans_regulation diff --git a/microbenchmarks/signalgp-lite/benchmark.hpp b/microbenchmarks/signalgp-lite/benchmark.hpp index bfa52060..3896b29d 100644 --- a/microbenchmarks/signalgp-lite/benchmark.hpp +++ b/microbenchmarks/signalgp-lite/benchmark.hpp @@ -43,7 +43,7 @@ static void DoBench(benchmark::State& state) { // Perform setup here for (auto _ : state) { - +#if OP_LIBRARY != Control auto& cpu = collection[agent]; // This code gets timed @@ -56,6 +56,7 @@ static void DoBench(benchmark::State& state) { } ++agent %= NUM_AGENTS; +#endif // OP_LIBRARY != Control } // prevent work from being optimized away From e045ea62df831c39b901c4cfa715667bba01f63e Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Tue, 15 Jun 2021 22:53:47 -0400 Subject: [PATCH 044/113] Add changes --- joss/paper.md | 90 ++++++++++++++++++++++++++++++--------------------- 1 file changed, 53 insertions(+), 37 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index 29d2f1b3..3e04a573 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -1,5 +1,5 @@ --- -title: 'signalgp-lite: A genetic programming implementation designed for large-scale artificial life applications' +title: 'signalgp-lite: Event Driven Genetic Programming Library for Large-Scale Artificial Life Applications' tags: - C++ - artificial life @@ -21,90 +21,106 @@ bibliography: paper.bib # Abstract -Event-driven genetic programming has been shown to outperform traditional traditional imperative representations on signal-heavy problems [cite]. +Event-driven genetic representations have been shown to outperform traditional traditional imperative representations on interaction-heavy problems. +Most existing work uses the SignalGP library. +This library caters to traditional program synthesis applications. +However, large-scale artificial life applications have a different set of implementation priorities. SignalGP is an existing implementation that is focused on extending tag-based referencing by applying event-driven techiques to genetic programming. With signalgp-lite, our goal is to provide a performance increase in large-scale experiments, such as those undertaken in Artificial Life. -By removing some of the Genetic Programming related overhead (e.g. the call stack) and employing compile-time configuration in exchange of run-time flexibility, we achieved an 8x to 30x speedup. +By removing control-flow overhead (e.g., the call stack) and trading run-time flexibility for better-performing compile-time configuration, we achieved an 8x to 30x speedup. `signalgp-lite` is unit- and integration-tested, achieving equivalent solution quality on two benchmark problems originally developed for SignalGP. -The library is currently used in DISHTNY [CITE IT], and our expectation is that it will be useful in future Artifical Life and Genetic Programming projects. +The library is currently used in DISHTNY, and our expectation is that it will be useful in future Artifical Life and Genetic Programming projects. # Summary -`signalgp-lite` is a lightweight genetic programming framework that allows for an event-based development paradigm. -This development style has been shown to outperform imperative programming on certain problems, such as those in which many interactions with the enviroment or other organisms occur. [cite] +`signalgp-lite` is a genetic programming framework that allows for an event-based development paradigm. +Compared to the traditional imperative paradigm, event-driven simulations trigger event-handlers (i.e., modules) in response to enviromental and inter-programmatic signals. +This representation has been shown to outperform imperative programming on certain problems, such as those in which many interactions with the enviroment or other organisms occur. [cite] One example of such situation is the case of a large-scale digital evolution experiment, as well as various other artifical life simulations. -`signalgp-lite` offers a speedup over the original event-driven framework (SignalGP) by forgoing some run-time customizability, such as the ability to xxxxx on the fly. +In light of the above, `signalgp-lite` is the framework of choice in DISHTINY. + +`signalgp-lite` is written in C++. +It was chosen due to its low-level control over runtime performance. +C++ is the language of choice for many artificial life and genetic programming utilities. # Statement of need -`signalgp-lite` is a C++ library for event-driven genetic programming. -C++ was chosen due to its low-level customizability as well as runtime performance. -Due to these features, C++ is the language of choice for most artificial life and genetic programming utilities. `signalgp-lite` fills a niche for event-driven applications that do not require a high level of customizability at runtime, but instead benefit from a considerable speed-up. -Despite being able to simulate evolution at scales that greately exceed real-life experiements, the performance of artifical life simulations greatly suffers as population size increases. -A large population size is required to replicate ecological effects of interest, such as the transition to multicelularity, or the role of rare events in evolution. -Our genetic programming libary allows for a considerable speedup in performance, allowing users to replicate a greater variety of artificial life situations. -In light of the above, `signalgp-lite` is the framework of choice in DISHTINY due to its combination of performance, flexibility, and ability to be compiled into javascript. +Despite being able to simulate evolution at generation rates that greately exceed real-life experiements, the scale of artifical life simulations is profoundly limited by available computational resources. +A large population size is required to study ecological effects, the transition to multicelularity, and the role of rare events in evolution. +Our genetic programming framework allows for a considerable speedup, allowing users to perform larger-scale artificial life situations. + +# Replication Results -# Results +Since `signalgp-lite` is intended to be a replacement for the original SignalGP in artifical life situations, it is important to quantitatively determine how accurately it can replicate its results. +Two experiments from the original SignalGP paper were replicated [1]. -## Replication Results +## Changing Enviroment Problem -Three experiments from the original SignalGP paper were replicated [1]. +The Changing Enviroment Problem consisted of K enviromental signals (2, 4, 8, 16) that organisms had to learn to respond to with a unique signaling instruction. -### Changing Enviroment Problem +\autoref{fig:tts-context} shows the number of generations before a full solution was found. +`signalgp-lite` evolved fill solutions to each problem within XX updates in all YY tested replicates. -The Changing Enviroment Problem consisted of K number of enviromental signals (2, 4, 8, 16) that organisms had to learn to call a unique signaling instruction for. -Figure \autoref{fig:tts-context} shows the number of generations before a full solution was found. -`signalgp-lite` achieved a 100% replication rate, even suprassing SignalGP in signal reproduction in the `K=16` case [figure X cited in Alex's paper] (100% vs 32% average). -This is hypothesised to be caused by differences in how mutation parameters are defined across both libraries, and not due to implementation discrepancies in the simulation. +In the `K=16` case, we achieved a 100% signal reproduction rate compared to an average of 32% on the vanilla implementation [figure X cited in Alex's paper]. +We suspect this occured due to differences in how default mutation parameters and program initialization are defined across both libraries. -### Contextual Signal Problem +## Contextual Signal Problem -The Contextual Signal Problem involves evolving programs capable of responding to a signal depending on what signal they previously encountered. +This problem tests regulation by evolving programs that must respond differently depending on what signal they previously encountered. A total of 16 input signal pairs and 4 response signals were tested. The unordered input signal pairs were assigned a response signal that then had to be replicated by the organism. You can refer to table 2 in the original SignalGP problem for a visual representation of this sequence. [cite it] + TODO: explain how `signalgp-lite` compared in this problem. ## Benchmarking Results -The main goal of this library is achieving fast execution of genetic programming problems compared to previous solutions. +The main goal of this library is to accelerate execution of event-driven genetic programs for more rapid evaluation. As such, benchmarking is essential to determining whether `signalgp-lite`'s optimizations work as intended. -Memory access time and caching are important factors when it comes to performance. [cite?] -In order to determine how the libary performs are different caching levels, benchmarks were performed over a variety of memory usages, which were simulated via the different agent counts. \autoref{fig:bench-wall} -Wall times were measured using Google Benchmark version x.y.z. +Cache size limitations profoundly affect memory access time [cite?], which is key to performance [cite?]. +In order to determine how the libary performs at different caching levels, benchmarks were performed over a variety of memory usages. +We altered memory usage via the different agent counts. +\autoref{fig:bench-wall} shows wall-clock times measured using Google Benchmark version x.y.z. Each of the five treatments serve a different purpose. +### `control` + The control ensures our benchmarking process is valid. While there is some uncertainty inherent to the benchmarking enviroment, the near-zero wall speedup means that our results are not directly affected by the chosen library, and that any resulting difference will stem from implementation choices. As such, our analysis can be considered fair. +### `NOP` + Benchmarking the NOP instruction alone isolates the performance impact of executing `signalgp-lite`'s byte-code interpreter compared to SignalGP's lamda-based instructions. We observe a 10 to 30 times speedup based on this change. +### `arithmetic` + The arithmetic benchmark adds to the prior improvements the performance effect of `signalgp-lite`'s registers being fixed-length arrays compared to SignalGP variable-length vectors. [TODO: FIX THIS SENTENCE] This compares the effects of compile-time optimizations versus the runtime flexibility of being able to change the number of registers on the fly. This trade-off results in a 20 to 50 times speed-up. -* test complex control flow within modules and in between modules -* signalgp-lite doesnt have a function stack like signal-gp -* inner while loops are implemented in terms of jump instrunctions -* we wanted to test this both with and without regulation -* because with regulation, tag matching causes caching to not work as well which can affect performance +### `complete` + +In order to improve performance compared to SignalGP, `signalgp-lite` lacks a function stack and implements inner while-loops in terms of `JUMP` instructions. +Since a paramount part of benchmarking `signalgp-lite` involves the complex control flow that occurs within and in-between modules, we wanted to test these changes both with and without regulation. +Performance can be affected by enabling regulation because tag-matching causes caching to underperform. The complete benchmark adds tag matching and complex control flow to the prior benchmarks' instruction set. Tag-based referencing is used to toggle module activation. This in turn allows for complex regulation mechanics and control flow. -Given the compile-time-friendly nature of `signalgp-lite`'s instructions, we can observe a 30 to 55 times speed-up compared to the vanilla SignalGP lambdas. +Given the striped-down control flow of `signalgp-lite`'s instructions, we can observe a 30 to 55 times speed-up compared to the vanilla SignalGP lambda-based operators. + +### `sans_regulation` Finally, sans_regulation measures the previous instruction set without regulation-specific instructions ([list them here]). -This results in a 35 to 47 times speedup. +This introduces a 35 to 47 times speedup. We can conclude that `signalgp-lite` offers significant performance improvements even on simulations that do not heavily depend on regulation. -# Citations +# Experimental Materials Citations to entries in paper.bib should be in [rMarkdown](http://rmarkdown.rstudio.com/authoring_bibliographies_and_citations.html) @@ -131,7 +147,7 @@ Figure sizes can be customized by adding an optional second parameter: ![Filtered maximum fitness wrt. updates, with 95% confidence intervals. Data has been filtered logarithmically.\label{fig:max-fitness-sd}](figures/max-fitness-log2.png) -![Time to solution for the Changing Enviroment problem.\label{fig:tts-changing}](figures/solution-time-swarmplot.png) +![Number of generations elapsed before a perfect solution was observed on the Changing Enviroment problem.\label{fig:tts-changing}](figures/solution-time-swarmplot.png) ![Time to solution for the Contextual Signal problem.\label{fig:tts-context}](figures/solution-time-contextual-signal.png) From 35f1b30411491db95234bd86d4a251c906594db0 Mon Sep 17 00:00:00 2001 From: "mmore500.login+git@gmail.com" Date: Wed, 23 Jun 2021 14:16:30 -0400 Subject: [PATCH 045/113] Exclude binary files from whitespace strip --- ci/fix_missing_newlines.sh | 4 +++- ci/replace_tabs.sh | 4 +++- ci/strip_trailing_whitespace.sh | 4 +++- 3 files changed, 9 insertions(+), 3 deletions(-) diff --git a/ci/fix_missing_newlines.sh b/ci/fix_missing_newlines.sh index 941a8818..8631e4e7 100755 --- a/ci/fix_missing_newlines.sh +++ b/ci/fix_missing_newlines.sh @@ -1,6 +1,8 @@ #!/bin/bash -TARGETS=$(find . -type f ! -path "./third-party/*" ! -path "./.git/*" ! -path "*/assets/*" ! -path "*.tar.gz" ! -path "*.jpg" ! -path "*.png" ! -path "*Makefile" ! -path "*Maketemplate*") +# greq -qI tests if file is binary +# adapted from https://stackoverflow.com/a/29689345 +TARGETS=$(find . -type f ! -path "./third-party/*" ! -path "./.git/*" ! -path "*/assets/*" ! -path "*.tar.gz" ! -path "*.jpg" ! -path "*.png" ! -path "*Makefile" ! -path "*Maketemplate*" -exec grep -qI . {} ';' -print) for filename in $TARGETS do diff --git a/ci/replace_tabs.sh b/ci/replace_tabs.sh index 0c1ff784..86a54b23 100755 --- a/ci/replace_tabs.sh +++ b/ci/replace_tabs.sh @@ -1,6 +1,8 @@ #!/bin/bash -TARGETS=$(find . -type f ! -path "./third-party/*" ! -path "./.git/*" ! -path "*/assets/*" ! -path "*Makefile" ! -path "*Maketemplate*") +# greq -qI tests if file is binary +# adapted from https://stackoverflow.com/a/29689345 +TARGETS=$(find . -type f ! -path "./third-party/*" ! -path "./.git/*" ! -path "*/assets/*" ! -path "*Makefile" ! -path "*Maketemplate*" -exec grep -qI . {} ';' -print) for filename in $TARGETS do diff --git a/ci/strip_trailing_whitespace.sh b/ci/strip_trailing_whitespace.sh index 1f4f0d79..8ca333b3 100755 --- a/ci/strip_trailing_whitespace.sh +++ b/ci/strip_trailing_whitespace.sh @@ -1,6 +1,8 @@ #!/bin/bash -TARGETS=$(find . -type f ! -path "./third-party/*" ! -path "./.git/*" ! -path "*/assets/*") +# greq -qI tests if file is binary +# adapted from https://stackoverflow.com/a/29689345 +TARGETS=$(find . -type f ! -path "./third-party/*" ! -path "./.git/*" ! -path "*/assets/*" -exec grep -qI . {} ';' -print) for filename in $TARGETS do From 33c156885e0cbc7574c3c342fa1545702043cdab Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Wed, 23 Jun 2021 16:21:59 -0400 Subject: [PATCH 046/113] Add figure --- joss/paper.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index 3e04a573..afea3584 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -149,11 +149,11 @@ Figure sizes can be customized by adding an optional second parameter: ![Number of generations elapsed before a perfect solution was observed on the Changing Enviroment problem.\label{fig:tts-changing}](figures/solution-time-swarmplot.png) -![Time to solution for the Contextual Signal problem.\label{fig:tts-context}](figures/solution-time-contextual-signal.png) +![Number of generations elapsed before a perfect solution was observed on the Contextual Signal problem.\label{fig:tts-context}](figures/solution-time-contextual-signal.png) -![Benchmarking results of 20 replicates shown as a times-speedup of wall time.\label{fig:bench-wall}](figures/wall-time-speedup.jpeg) +![Benchmarking results of 20 replicates shown as a times-speedup of wall time. "Library" refers to the set of instructions tested (see Benchmarking Results section).\label{fig:bench-wall}](figures/wall-time-speedup.jpeg) # Acknowledgements From 1c15e9d3dccc3f62d1d285abd755576cbb81ec9a Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Wed, 23 Jun 2021 16:39:57 -0400 Subject: [PATCH 047/113] Add Appendix --- joss/paper.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/joss/paper.md b/joss/paper.md index afea3584..eb9d0bd7 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -155,6 +155,9 @@ Figure sizes can be customized by adding an optional second parameter: ![Benchmarking results of 20 replicates shown as a times-speedup of wall time. "Library" refers to the set of instructions tested (see Benchmarking Results section).\label{fig:bench-wall}](figures/wall-time-speedup.jpeg) +\pagebreak +\appendix + # Acknowledgements We acknowledge contributions from Brigitta Sipocz, Syrtis Major, and Semyeong From 406388fd4e45521de37d790f6c6418c89b4d3dfe Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Wed, 23 Jun 2021 16:42:42 -0400 Subject: [PATCH 048/113] Add Supplementary Material section --- joss/paper.md | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index eb9d0bd7..ee1153af 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -143,10 +143,6 @@ and referenced from text using \autoref{fig:example}. Figure sizes can be customized by adding an optional second parameter: ![Caption for example figure.](figure.png){ width=20% } --> -![Maximum fitness wrt. updates, with standard deviation confidence intervals. This is because, due to large number of datapoints, computing 95% CI takes a non-insignificant amount of time.\label{fig:max-fitness-sd}](figures/max-fitness-sd.png) - -![Filtered maximum fitness wrt. updates, with 95% confidence intervals. Data has been filtered logarithmically.\label{fig:max-fitness-sd}](figures/max-fitness-log2.png) - ![Number of generations elapsed before a perfect solution was observed on the Changing Enviroment problem.\label{fig:tts-changing}](figures/solution-time-swarmplot.png) ![Number of generations elapsed before a perfect solution was observed on the Contextual Signal problem.\label{fig:tts-context}](figures/solution-time-contextual-signal.png) @@ -155,8 +151,6 @@ Figure sizes can be customized by adding an optional second parameter: ![Benchmarking results of 20 replicates shown as a times-speedup of wall time. "Library" refers to the set of instructions tested (see Benchmarking Results section).\label{fig:bench-wall}](figures/wall-time-speedup.jpeg) -\pagebreak -\appendix # Acknowledgements @@ -164,3 +158,12 @@ We acknowledge contributions from Brigitta Sipocz, Syrtis Major, and Semyeong Oh, and support from Kathryn Johnston during the genesis of this project. # References + +\pagebreak +\appendix + +# Supplementary Material + +![Maximum fitness wrt. updates, with standard deviation confidence intervals. This is because, due to large number of datapoints, computing 95% CI takes a non-insignificant amount of time.\label{fig:max-fitness-sd}](figures/max-fitness-sd.png) + +![Filtered maximum fitness wrt. updates, with 95% confidence intervals. Data has been filtered logarithmically.\label{fig:max-fitness-sd}](figures/max-fitness-log2.png) From 55d19334312dd406f9a3805e9891a45076e361cc Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Wed, 23 Jun 2021 16:46:34 -0400 Subject: [PATCH 049/113] Add references --- joss/paper.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/joss/paper.md b/joss/paper.md index ee1153af..f1bae2f4 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -159,6 +159,8 @@ Oh, and support from Kathryn Johnston during the genesis of this project. # References +
    + \pagebreak \appendix From ec7f89f52f5364e9ca727460f019d2da481d8eda Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Thu, 24 Jun 2021 12:37:40 -0400 Subject: [PATCH 050/113] Fix paper --- joss/paper.md | 107 ++++++++++++++++++++++++++------------------------ 1 file changed, 56 insertions(+), 51 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index f1bae2f4..5931b460 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -45,81 +45,85 @@ C++ is the language of choice for many artificial life and genetic programming u # Statement of need -`signalgp-lite` fills a niche for event-driven applications that do not require a high level of customizability at runtime, but instead benefit from a considerable speed-up. -Despite being able to simulate evolution at generation rates that greately exceed real-life experiements, the scale of artifical life simulations is profoundly limited by available computational resources. -A large population size is required to study ecological effects, the transition to multicelularity, and the role of rare events in evolution. +`signalgp-lite` fills a niche for interaction-heavy genetic programming applications that can tolerate trading a high level of customizability at runtime for a considerable speed-up. +Despite being able to simulate evolution at generation rates that greately exceed biological experiements, the scale of artifical life simulations is profoundly limited by available computational resources. +Large population sizes are essential to studying ecological effects, the transition to multicelularity, and the role of rare events in evolution. +In addition to leveraging parallel and distributed computing resources, Our genetic programming framework allows for a considerable speedup, allowing users to perform larger-scale artificial life situations. -# Replication Results - -Since `signalgp-lite` is intended to be a replacement for the original SignalGP in artifical life situations, it is important to quantitatively determine how accurately it can replicate its results. -Two experiments from the original SignalGP paper were replicated [1]. - -## Changing Enviroment Problem - -The Changing Enviroment Problem consisted of K enviromental signals (2, 4, 8, 16) that organisms had to learn to respond to with a unique signaling instruction. - -\autoref{fig:tts-context} shows the number of generations before a full solution was found. -`signalgp-lite` evolved fill solutions to each problem within XX updates in all YY tested replicates. - -In the `K=16` case, we achieved a 100% signal reproduction rate compared to an average of 32% on the vanilla implementation [figure X cited in Alex's paper]. -We suspect this occured due to differences in how default mutation parameters and program initialization are defined across both libraries. - -## Contextual Signal Problem - -This problem tests regulation by evolving programs that must respond differently depending on what signal they previously encountered. -A total of 16 input signal pairs and 4 response signals were tested. -The unordered input signal pairs were assigned a response signal that then had to be replicated by the organism. -You can refer to table 2 in the original SignalGP problem for a visual representation of this sequence. [cite it] - -TODO: explain how `signalgp-lite` compared in this problem. - ## Benchmarking Results -The main goal of this library is to accelerate execution of event-driven genetic programs for more rapid evaluation. -As such, benchmarking is essential to determining whether `signalgp-lite`'s optimizations work as intended. +The main goal of this library is to accelerate execution of event-driven genetic programs. +We performed a set of microbenchmarks to quantify the effectiveness of `signalgp-lite`'s optimizations. -Cache size limitations profoundly affect memory access time [cite?], which is key to performance [cite?]. -In order to determine how the libary performs at different caching levels, benchmarks were performed over a variety of memory usages. -We altered memory usage via the different agent counts. +Cache size limitations profoundly affect memory access time, which is key to performance [cite?]. +In order to determine how the libary performs at different caching levels, benchmarks were performed across different orders of magnitude of memory usages. +We altered memory usage through agent counts, ranging from 1 to 32768. \autoref{fig:bench-wall} shows wall-clock times measured using Google Benchmark version x.y.z. -Each of the five treatments serve a different purpose. +We performed five microbenchmark experiments, overviewed below. + +### control -### `control` +This experiemnt verifies the validity of our benchmarking process. +While there is some uncertainty inherent to the benchmarking enviroment, the 1x wall speedup means that our results are not directly affected by our experimental aparatus, so that any differences in other experiments stem solely from differences between SignalGP and `signalgp-lite`. -The control ensures our benchmarking process is valid. -While there is some uncertainty inherent to the benchmarking enviroment, the near-zero wall speedup means that our results are not directly affected by the chosen library, and that any resulting difference will stem from implementation choices. -As such, our analysis can be considered fair. +### nop -### `NOP` +Benchmarking the `nop` instruction alone isolates the performance impact of executing `signalgp-lite`'s byte-code interpreter compared to SignalGP's lamda-based instructions. -Benchmarking the NOP instruction alone isolates the performance impact of executing `signalgp-lite`'s byte-code interpreter compared to SignalGP's lamda-based instructions. We observe a 10 to 30 times speedup based on this change. -### `arithmetic` +### arithmetic The arithmetic benchmark adds to the prior improvements the performance effect of `signalgp-lite`'s registers being fixed-length arrays compared to SignalGP variable-length vectors. [TODO: FIX THIS SENTENCE] -This compares the effects of compile-time optimizations versus the runtime flexibility of being able to change the number of registers on the fly. +This compares the effects of compile-time optimizations that streamline register access at the cost of the ability to change the number of registers on the fly. + This trade-off results in a 20 to 50 times speed-up. ### `complete` -In order to improve performance compared to SignalGP, `signalgp-lite` lacks a function stack and implements inner while-loops in terms of `JUMP` instructions. -Since a paramount part of benchmarking `signalgp-lite` involves the complex control flow that occurs within and in-between modules, we wanted to test these changes both with and without regulation. -Performance can be affected by enabling regulation because tag-matching causes caching to underperform. - +In order to improve performance, `signalgp-lite` omits a function stack and implements inner while-loops in terms of `jump` instructions. The complete benchmark adds tag matching and complex control flow to the prior benchmarks' instruction set. Tag-based referencing is used to toggle module activation. This in turn allows for complex regulation mechanics and control flow. -Given the striped-down control flow of `signalgp-lite`'s instructions, we can observe a 30 to 55 times speed-up compared to the vanilla SignalGP lambda-based operators. + +Given the stripped-down control flow of `signalgp-lite`'s instructions, we can observe a 30 to 55 times speed-up compared to the vanilla SignalGP lambda-based operators. ### `sans_regulation` -Finally, sans_regulation measures the previous instruction set without regulation-specific instructions ([list them here]). +Since regulation hinders tag-match caching, we wanted to measure changes both with and without regulation, +This benchmark measures the complete instruction set without regulation-specific instructions ([list them here]). + + This introduces a 35 to 47 times speedup. We can conclude that `signalgp-lite` offers significant performance improvements even on simulations that do not heavily depend on regulation. +# Replication Results + +Since `signalgp-lite` is intended to be a replacement for the original SignalGP in artifical life situations, it is important to verify how accurately it can replicate its results. +Two experiments from the original SignalGP paper were replicated [1]. + +## Changing Enviroment Problem + +The Changing Enviroment Problem consisted of K enviromental signals (2, 4, 8, 16) that organisms had to learn to respond to with a unique signaling instruction. + +\autoref{fig:tts-context} shows the number of generations before a full solution was found. +`signalgp-lite` evolved fill solutions to each problem within XX updates in all YY tested replicates. + +In the `K=16` case, we achieved a 100% signal reproduction rate compared to an average of 32% on the vanilla implementation [figure X cited in Alex's paper]. +We suspect this occured due to differences in how default mutation parameters and program initialization are defined across both libraries. + +## Contextual Signal Problem + +This problem tests regulation by evolving programs that must respond differently depending on what signal they previously encountered. +A total of 16 input signal pairs and 4 response signals were tested. +The unordered input signal pairs were assigned a response signal that then had to be replicated by the organism. +You can refer to table 2 in the original SignalGP problem for a visual representation of this sequence. [cite it] + +TODO: explain how `signalgp-lite` compared in this problem. + + # Experimental Materials Citations to entries in paper.bib should be in @@ -143,9 +147,9 @@ and referenced from text using \autoref{fig:example}. Figure sizes can be customized by adding an optional second parameter: ![Caption for example figure.](figure.png){ width=20% } --> -![Number of generations elapsed before a perfect solution was observed on the Changing Enviroment problem.\label{fig:tts-changing}](figures/solution-time-swarmplot.png) +![Number of generations elapsed before a perfect solution was observed on the Changing Enviroment problem. All replicates found a perfect solution.\label{fig:tts-changing}](figures/solution-time-swarmplot.png) -![Number of generations elapsed before a perfect solution was observed on the Contextual Signal problem.\label{fig:tts-context}](figures/solution-time-contextual-signal.png) +![Number of generations elapsed before a perfect solution was observed on the Contextual Signal problem. Replicates that did not find a solution are on a dashed line at 10,000 generations.\label{fig:tts-context}](figures/solution-time-contextual-signal.png) @@ -154,8 +158,9 @@ Figure sizes can be customized by adding an optional second parameter: # Acknowledgements -We acknowledge contributions from Brigitta Sipocz, Syrtis Major, and Semyeong -Oh, and support from Kathryn Johnston during the genesis of this project. +This research was supported in part by NSF grants DEB-1655715 and DBI-0939454 as well as by Michigan State University through the computational resources provided by the Institute for Cyber-Enabled Research. +This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1424871, and by the Michigan State University BEACON Center Luminaries program. +Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. # References From 772880da31f534b34e762e1d11ac3eaa282c387a Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Thu, 24 Jun 2021 18:39:57 -0400 Subject: [PATCH 051/113] More changes --- joss/paper.bib | 11 +++++++++++ joss/paper.md | 40 +++++++++++++++++++--------------------- 2 files changed, 30 insertions(+), 21 deletions(-) diff --git a/joss/paper.bib b/joss/paper.bib index 0732afb2..2697b51c 100644 --- a/joss/paper.bib +++ b/joss/paper.bib @@ -66,6 +66,17 @@ @inproceedings{lalejini2018evolving year={2018} } +@article{skadron1999branch, + title={Branch prediction, instruction-window size, and cache size: Performance trade-offs and simulation techniques}, + author={Skadron, Kevin and Ahuja, Pritpal S and Martonosi, Margaret and Clark, Douglas W}, + journal={IEEE Transactions on Computers}, + volume={48}, + number={11}, + pages={1260--1281}, + year={1999}, + publisher={IEEE} +} + @misc{moreno2021exploring, title={Exploring Evolved Multicellular Life Histories in a Open-Ended Digital Evolution System}, author={Matthew Andres Moreno and Charles Ofria}, diff --git a/joss/paper.md b/joss/paper.md index 5931b460..787fe7a1 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -45,41 +45,40 @@ C++ is the language of choice for many artificial life and genetic programming u # Statement of need -`signalgp-lite` fills a niche for interaction-heavy genetic programming applications that can tolerate trading a high level of customizability at runtime for a considerable speed-up. -Despite being able to simulate evolution at generation rates that greately exceed biological experiements, the scale of artifical life simulations is profoundly limited by available computational resources. +Despite being able to simulate evolution at generation rates that greately exceed biological experiements, the scale of artifical life simulations is profoundly limited by available computational resources [cite blog]. Large population sizes are essential to studying ecological effects, the transition to multicelularity, and the role of rare events in evolution. -In addition to leveraging parallel and distributed computing resources, -Our genetic programming framework allows for a considerable speedup, allowing users to perform larger-scale artificial life situations. +In addition to leveraging parallel and distributed computing resources, computational efficiency is crucial to researchers to perform larger-scale artificial life situations. +`signalgp-lite` fills a niche for interaction-heavy genetic programming applications that can tolerate trading a high level of customizability at runtime for a considerable speed-up. + ## Benchmarking Results The main goal of this library is to accelerate execution of event-driven genetic programs. We performed a set of microbenchmarks to quantify the effectiveness of `signalgp-lite`'s optimizations. - -Cache size limitations profoundly affect memory access time, which is key to performance [cite?]. -In order to determine how the libary performs at different caching levels, benchmarks were performed across different orders of magnitude of memory usages. +Cache size limitations profoundly affect memory access time, which is key to performance @skadron1999branch. +In order to determine how the libary performs at different caching levels, benchmarks were performed across different orders of magnitude of memory loads. We altered memory usage through agent counts, ranging from 1 to 32768. -\autoref{fig:bench-wall} shows wall-clock times measured using Google Benchmark version x.y.z. +\autoref{fig:bench-wall} shows wall-clock times measured using Google Benchmark version 1.5.2-1. We performed five microbenchmark experiments, overviewed below. ### control This experiemnt verifies the validity of our benchmarking process. -While there is some uncertainty inherent to the benchmarking enviroment, the 1x wall speedup means that our results are not directly affected by our experimental aparatus, so that any differences in other experiments stem solely from differences between SignalGP and `signalgp-lite`. +While there is some uncertainty inherent to the benchmarking enviroment, the 1x wall speedup (\autoref{fig:bench-wall}) means our results are not directly affected by our experimental aparatus, so that any speedups in other experiments stem solely from differences between SignalGP and `signalgp-lite`. ### nop -Benchmarking the `nop` instruction alone isolates the performance impact of executing `signalgp-lite`'s byte-code interpreter compared to SignalGP's lamda-based instructions. +Benchmarking the `nop` instruction on its own isolates the performance impact of executing `signalgp-lite`'s byte-code interpreter compared to SignalGP's lamda-based instructions. -We observe a 10 to 30 times speedup based on this change. +We observe a 10 to 30 times speedup (\autoref{fig:bench-wall}) based on this change. ### arithmetic -The arithmetic benchmark adds to the prior improvements the performance effect of `signalgp-lite`'s registers being fixed-length arrays compared to SignalGP variable-length vectors. [TODO: FIX THIS SENTENCE] +The arithmetic benchmark adds to the prior improvements the performance effect of `signalgp-lite`'s fixed-length array registers compared to SignalGP variable-length vectors. This compares the effects of compile-time optimizations that streamline register access at the cost of the ability to change the number of registers on the fly. -This trade-off results in a 20 to 50 times speed-up. +\autoref{fig:bench-wall} shows this trade-off results in a 20 to 50 times speed-up. ### `complete` @@ -88,30 +87,29 @@ The complete benchmark adds tag matching and complex control flow to the prior b Tag-based referencing is used to toggle module activation. This in turn allows for complex regulation mechanics and control flow. -Given the stripped-down control flow of `signalgp-lite`'s instructions, we can observe a 30 to 55 times speed-up compared to the vanilla SignalGP lambda-based operators. +Given the stripped-down control flow of `signalgp-lite`'s instructions, \autoref{fig:bench-wall} shows a 30 to 55 times speed-up compared to the vanilla SignalGP lambda-based operators. ### `sans_regulation` Since regulation hinders tag-match caching, we wanted to measure changes both with and without regulation, This benchmark measures the complete instruction set without regulation-specific instructions ([list them here]). - -This introduces a 35 to 47 times speedup. +As shown on \autoref{fig:bench-wall}, this introduces a 35 to 47 times speed-up. We can conclude that `signalgp-lite` offers significant performance improvements even on simulations that do not heavily depend on regulation. # Replication Results Since `signalgp-lite` is intended to be a replacement for the original SignalGP in artifical life situations, it is important to verify how accurately it can replicate its results. -Two experiments from the original SignalGP paper were replicated [1]. +Two experiments from the original SignalGP paper were replicated @lalejini2018evolving. ## Changing Enviroment Problem The Changing Enviroment Problem consisted of K enviromental signals (2, 4, 8, 16) that organisms had to learn to respond to with a unique signaling instruction. -\autoref{fig:tts-context} shows the number of generations before a full solution was found. -`signalgp-lite` evolved fill solutions to each problem within XX updates in all YY tested replicates. +\autoref{fig:tts-changing} shows the number of generations before a full solution was found. +`signalgp-lite` evolved fill solutions to each problem within 3500 updates in all 100 tested replicates. -In the `K=16` case, we achieved a 100% signal reproduction rate compared to an average of 32% on the vanilla implementation [figure X cited in Alex's paper]. +In the `K=16` case, we achieved a 100% signal reproduction rate compared to an average of 32% on the vanilla implementation (Figure 2 in @lalejini2018evolving). We suspect this occured due to differences in how default mutation parameters and program initialization are defined across both libraries. ## Contextual Signal Problem @@ -119,7 +117,7 @@ We suspect this occured due to differences in how default mutation parameters an This problem tests regulation by evolving programs that must respond differently depending on what signal they previously encountered. A total of 16 input signal pairs and 4 response signals were tested. The unordered input signal pairs were assigned a response signal that then had to be replicated by the organism. -You can refer to table 2 in the original SignalGP problem for a visual representation of this sequence. [cite it] +You can refer to table 2 in the original SignalGP problem for a visual representation of this sequence @lalejini2018evolving. TODO: explain how `signalgp-lite` compared in this problem. From dbfecf14435d8aebbfa1ed8201b11cb37342e9b8 Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Wed, 30 Jun 2021 15:18:55 -0400 Subject: [PATCH 052/113] Implemented some review feedback --- joss/paper.md | 81 ++++++++++++++++++++++++++------------------------- 1 file changed, 41 insertions(+), 40 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index 787fe7a1..2b7d1797 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -21,23 +21,20 @@ bibliography: paper.bib # Abstract -Event-driven genetic representations have been shown to outperform traditional traditional imperative representations on interaction-heavy problems. -Most existing work uses the SignalGP library. -This library caters to traditional program synthesis applications. -However, large-scale artificial life applications have a different set of implementation priorities. +Event-driven genetic programming representations have been shown to outperform traditional traditional imperative representations on interaction-heavy problems. +Existing work uses the SignalGP library, which caters to traditional program synthesis applications. +However, large-scale artificial life applications require a different set of implementation priorities. SignalGP is an existing implementation that is focused on extending tag-based referencing by applying event-driven techiques to genetic programming. -With signalgp-lite, our goal is to provide a performance increase in large-scale experiments, such as those undertaken in Artificial Life. -By removing control-flow overhead (e.g., the call stack) and trading run-time flexibility for better-performing compile-time configuration, we achieved an 8x to 30x speedup. -`signalgp-lite` is unit- and integration-tested, achieving equivalent solution quality on two benchmark problems originally developed for SignalGP. -The library is currently used in DISHTNY, and our expectation is that it will be useful in future Artifical Life and Genetic Programming projects. +With signalgp-lite, our goal is to increase performance to enable large-scale experiments, such as those undertaken in Artificial Life, by removing control-flow overhead (e.g., the call stack) and trading run-time flexibility for better-performing compile-time configuration. +Here, we report benchmarking experiments that show an 8x to 30x speedup. +We also report solution quality equivalent to SignalGP on two benchmark problems originally developed for that implementation. +The library is currently used in Artificial Life experiments studying the evolution of multicelularity, and our expectation is that it will be useful in future Artifical Life and Genetic Programming projects. # Summary -`signalgp-lite` is a genetic programming framework that allows for an event-based development paradigm. -Compared to the traditional imperative paradigm, event-driven simulations trigger event-handlers (i.e., modules) in response to enviromental and inter-programmatic signals. -This representation has been shown to outperform imperative programming on certain problems, such as those in which many interactions with the enviroment or other organisms occur. [cite] -One example of such situation is the case of a large-scale digital evolution experiment, as well as various other artifical life simulations. -In light of the above, `signalgp-lite` is the framework of choice in DISHTINY. +`signalgp-lite` is a software framework for event-driven genetic programming. +Unlike the traditional imperative paradigm, event-driven simulations trigger event-handlers (i.e., program modules) in response to enviromental, self-generated, and cohabitant-generated signals. +This representation outperforms imperative programming on interaction intensive problems, where inputs from the enviroment or other organisms ocmust be handled [cite], as is the case in large-scale digital evolution experiments as well as various other artifical life simulations. `signalgp-lite` is written in C++. It was chosen due to its low-level control over runtime performance. @@ -45,83 +42,87 @@ C++ is the language of choice for many artificial life and genetic programming u # Statement of need -Despite being able to simulate evolution at generation rates that greately exceed biological experiements, the scale of artifical life simulations is profoundly limited by available computational resources [cite blog]. -Large population sizes are essential to studying ecological effects, the transition to multicelularity, and the role of rare events in evolution. -In addition to leveraging parallel and distributed computing resources, computational efficiency is crucial to researchers to perform larger-scale artificial life situations. -`signalgp-lite` fills a niche for interaction-heavy genetic programming applications that can tolerate trading a high level of customizability at runtime for a considerable speed-up. +Despite being able to simulate evolution with faster generations than possible in biological experiements [cite avida paper], the scale of artifical life populations is profoundly limited by available computational resources [cite blog]. +Large population sizes are essential to studying essential topics such as ecologies, the transition to multicelularity, and the role of rare events in evolution. +Also, parallel and distributed computing resources are ultimately key to increasing scale. [join these] Computational efficiency is crucial to enabling larger-scale artificial life situations.] + +[add transition] `signalgp-lite` fills a niche for interaction-heavy genetic programming applications that can tolerate trading a high level of customizability at runtime for a considerable speed-up. -## Benchmarking Results +## Execution Speed -The main goal of this library is to accelerate execution of event-driven genetic programs. +To accelerating execution of event-driven genetic programs. [????] We performed a set of microbenchmarks to quantify the effectiveness of `signalgp-lite`'s optimizations. Cache size limitations profoundly affect memory access time, which is key to performance @skadron1999branch. -In order to determine how the libary performs at different caching levels, benchmarks were performed across different orders of magnitude of memory loads. -We altered memory usage through agent counts, ranging from 1 to 32768. +In order to determine how the libary performs across cache levels, we benchmarked over different orders of magnitude of memory loads, by varying agent counts between from 1 and 32768. [addfig](raw timings) \autoref{fig:bench-wall} shows wall-clock times measured using Google Benchmark version 1.5.2-1. -We performed five microbenchmark experiments, overviewed below. +We performed five microbenchmark experiments, overviewed below, in order to understand how different aspects of the library influenced performance. ### control This experiemnt verifies the validity of our benchmarking process. -While there is some uncertainty inherent to the benchmarking enviroment, the 1x wall speedup (\autoref{fig:bench-wall}) means our results are not directly affected by our experimental aparatus, so that any speedups in other experiments stem solely from differences between SignalGP and `signalgp-lite`. +The 1x wall speedup (\autoref{fig:bench-wall}) confirms that our results are not directly affected by the experimental aparatus, so that any speedups in other experiments stem solely from differences between SignalGP and `signalgp-lite`. ### nop -Benchmarking the `nop` instruction on its own isolates the performance impact of executing `signalgp-lite`'s byte-code interpreter compared to SignalGP's lamda-based instructions. +Benchmarking the `nop` instruction in isolation denotes the relative performance impact of `signalgp-lite`'s byte-code interpreter compared to SignalGP's lamda-based instructions. -We observe a 10 to 30 times speedup (\autoref{fig:bench-wall}) based on this change. +We observe a 10x to 30x speedup under `signalgp-lite` (\autoref{fig:bench-wall}). ### arithmetic -The arithmetic benchmark adds to the prior improvements the performance effect of `signalgp-lite`'s fixed-length array registers compared to SignalGP variable-length vectors. +Benchmarking arithmetic operations aids in measuring the performance effect of `signalgp-lite`'s fixed-length array registers compared to SignalGP's variable-length vectors. This compares the effects of compile-time optimizations that streamline register access at the cost of the ability to change the number of registers on the fly. -\autoref{fig:bench-wall} shows this trade-off results in a 20 to 50 times speed-up. +\autoref{fig:bench-wall} shows this trade-off results in a 20x to 50x speed-up. ### `complete` In order to improve performance, `signalgp-lite` omits a function stack and implements inner while-loops in terms of `jump` instructions. -The complete benchmark adds tag matching and complex control flow to the prior benchmarks' instruction set. -Tag-based referencing is used to toggle module activation. -This in turn allows for complex regulation mechanics and control flow. +The complete benchmark adds control flow to the prior benchmarks' instruction set. -Given the stripped-down control flow of `signalgp-lite`'s instructions, \autoref{fig:bench-wall} shows a 30 to 55 times speed-up compared to the vanilla SignalGP lambda-based operators. + `signalgp-lite`'s stripped-down control flow modele nables a 30x to 55x speed-up compared to vanilla SignalGP. ### `sans_regulation` Since regulation hinders tag-match caching, we wanted to measure changes both with and without regulation, This benchmark measures the complete instruction set without regulation-specific instructions ([list them here]). -As shown on \autoref{fig:bench-wall}, this introduces a 35 to 47 times speed-up. -We can conclude that `signalgp-lite` offers significant performance improvements even on simulations that do not heavily depend on regulation. +As shown on \autoref{fig:bench-wall}, this yields a 35x to 47x speed-up. +Thus, `signalgp-lite` offers performance improvements even on simulations that do not heavily depend on regulation. -# Replication Results +# Test Problem Benchmarking -Since `signalgp-lite` is intended to be a replacement for the original SignalGP in artifical life situations, it is important to verify how accurately it can replicate its results. +Since `signalgp-lite` is intended to be a specialized alternative to the original SignalGP in artifical life situations, it is important to verify how accurately it can replicate its results. Two experiments from the original SignalGP paper were replicated @lalejini2018evolving. ## Changing Enviroment Problem -The Changing Enviroment Problem consisted of K enviromental signals (2, 4, 8, 16) that organisms had to learn to respond to with a unique signaling instruction. +The Changing Enviroment Problem consisted of K mutually exclusive enviromental signals (2, 4, 8, 16). Organisms were tasked to respond to each with a particular signaling instruction. -\autoref{fig:tts-changing} shows the number of generations before a full solution was found. +\autoref{fig:tts-changing} shows the number of generations elapsed before a full solution was found [add](population size and selection scheme). `signalgp-lite` evolved fill solutions to each problem within 3500 updates in all 100 tested replicates. In the `K=16` case, we achieved a 100% signal reproduction rate compared to an average of 32% on the vanilla implementation (Figure 2 in @lalejini2018evolving). -We suspect this occured due to differences in how default mutation parameters and program initialization are defined across both libraries. +We suspect this occured due to differences in how default mutation parameters and program initialization are performed. ## Contextual Signal Problem -This problem tests regulation by evolving programs that must respond differently depending on what signal they previously encountered. +This problem tests regulation by evolving programs that maintain memory of previously encountered signals. A total of 16 input signal pairs and 4 response signals were tested. The unordered input signal pairs were assigned a response signal that then had to be replicated by the organism. -You can refer to table 2 in the original SignalGP problem for a visual representation of this sequence @lalejini2018evolving. +Table 2 in @lalejini2018evolving provides a visual representation of this sequence. +TODO: expand more on the problem TODO: explain how `signalgp-lite` compared in this problem. +# Projects Using the Sofrware + +In light of the above, `signalgp-lite` is the framework of choice in DISHTINY. + + # Experimental Materials Citations to entries in paper.bib should be in From 8e0eac6ef02e6dbb103f52c054dd4bad5239764a Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Wed, 30 Jun 2021 15:59:56 -0400 Subject: [PATCH 053/113] Add raw timings --- joss/figures/raw-timings.png | Bin 0 -> 121480 bytes joss/paper.md | 1028 +++++++++++++++++++++++++++++++++- 2 files changed, 1012 insertions(+), 16 deletions(-) create mode 100644 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This representation outperforms imperative programming on interaction intensive problems, where inputs from the enviroment or other organisms ocmust be handled [cite], as is the case in large-scale digital evolution experiments as well as various other artifical life simulations. `signalgp-lite` is written in C++. @@ -53,7 +53,7 @@ Also, parallel and distributed computing resources are ultimately key to increas To accelerating execution of event-driven genetic programs. [????] We performed a set of microbenchmarks to quantify the effectiveness of `signalgp-lite`'s optimizations. -Cache size limitations profoundly affect memory access time, which is key to performance @skadron1999branch. +Cache size limitations profoundly affect memory access time, which is key to performance [@skadron1999branch]. In order to determine how the libary performs across cache levels, we benchmarked over different orders of magnitude of memory loads, by varying agent counts between from 1 and 32768. [addfig](raw timings) \autoref{fig:bench-wall} shows wall-clock times measured using Google Benchmark version 1.5.2-1. @@ -94,12 +94,12 @@ Thus, `signalgp-lite` offers performance improvements even on simulations that d # Test Problem Benchmarking -Since `signalgp-lite` is intended to be a specialized alternative to the original SignalGP in artifical life situations, it is important to verify how accurately it can replicate its results. -Two experiments from the original SignalGP paper were replicated @lalejini2018evolving. +Since `signalgp-lite` is intended to be a specialized alternative to the original SignalGP in artifical life situations, it must match SignalGP performance on benchmarks measuring responsitivy and plasticity. +Two experiments from the original SignalGP paper were replicated [@lalejini2018evolving]. ## Changing Enviroment Problem -The Changing Enviroment Problem consisted of K mutually exclusive enviromental signals (2, 4, 8, 16). Organisms were tasked to respond to each with a particular signaling instruction. +The Changing Enviroment Problem consisted of K mutually-exclusive enviromental signals (2, 4, 8, 16). Organisms were tasked to respond to each with a particular signaling instruction. \autoref{fig:tts-changing} shows the number of generations elapsed before a full solution was found [add](population size and selection scheme). `signalgp-lite` evolved fill solutions to each problem within 3500 updates in all 100 tested replicates. @@ -122,20 +122,11 @@ TODO: explain how `signalgp-lite` compared in this problem. In light of the above, `signalgp-lite` is the framework of choice in DISHTINY. +TODO: talk more about it -# Experimental Materials - -Citations to entries in paper.bib should be in -[rMarkdown](http://rmarkdown.rstudio.com/authoring_bibliographies_and_citations.html) -format. -If you want to cite a software repository URL (e.g. something on GitHub without a preferred -citation) then you can do it with the example BibTeX entry below for @fidgit. +# Experimental Materials - # Figures @@ -173,3 +164,1008 @@ Any opinions, findings, and conclusions or recommendations expressed in this mat ![Maximum fitness wrt. updates, with standard deviation confidence intervals. This is because, due to large number of datapoints, computing 95% CI takes a non-insignificant amount of time.\label{fig:max-fitness-sd}](figures/max-fitness-sd.png) ![Filtered maximum fitness wrt. updates, with 95% confidence intervals. Data has been filtered logarithmically.\label{fig:max-fitness-sd}](figures/max-fitness-log2.png) + +![Wall time benchmarking results of 20 replicates. The x-axis represents different agent counts.\label{fig:raw-timings}](figures/raw-timings.png) + +| Library | Implementation | Statistic | Wall Nanoseconds | CPU Nanoseconds | num agents | +|-----------------|----------------|-------------|------------------------|------------------------|------------| +| arithmetic | vanilla | measurement | 760.1855802385494 | 749.882302240635 | 1.0 | +| arithmetic | vanilla | measurement | 768.353517653824 | 754.1809000481397 | 1.0 | +| arithmetic | vanilla | measurement | 768.8718047121922 | 755.1567654938049 | 1.0 | +| arithmetic | vanilla | measurement | 757.2702403360975 | 748.9733345468779 | 1.0 | +| arithmetic | vanilla | measurement | 759.5047761394123 | 746.0654853612508 | 1.0 | +| arithmetic | vanilla | measurement | 759.3515514911907 | 747.1096697220942 | 1.0 | +| arithmetic | vanilla | measurement | 759.5045195402103 | 746.0732469304083 | 1.0 | +| arithmetic | vanilla | measurement | 752.6935430278776 | 751.918087921319 | 1.0 | +| arithmetic | vanilla | measurement | 741.5234705767284 | 741.5241296197398 | 1.0 | +| arithmetic | vanilla | measurement | 745.2377202598302 | 745.2209766330488 | 1.0 | +| arithmetic | vanilla | measurement | 740.7536749030298 | 740.7368281960548 | 1.0 | +| arithmetic | vanilla | measurement | 747.5374552170535 | 747.5379925410867 | 1.0 | +| arithmetic | vanilla | measurement | 745.7338597629983 | 745.7354300608938 | 1.0 | +| arithmetic | vanilla | measurement | 742.2138850732599 | 742.2163315345814 | 1.0 | +| arithmetic | vanilla | measurement | 741.1792310592932 | 741.1651477253165 | 1.0 | +| arithmetic | vanilla | measurement | 742.0711053168758 | 742.0724475260682 | 1.0 | +| arithmetic | vanilla | measurement | 746.5643268273411 | 746.5564139313476 | 1.0 | +| arithmetic | vanilla | measurement | 738.833539672256 | 738.8223315630922 | 1.0 | +| arithmetic | vanilla | measurement | 776.1955053522951 | 776.1755684927219 | 1.0 | +| arithmetic | vanilla | measurement | 764.0617339731097 | 764.0406687362724 | 1.0 | +| arithmetic | vanilla | measurement | 769.9571962292429 | 769.9325901468208 | 1.0 | +| arithmetic | vanilla | measurement | 760.7416849325331 | 760.7424887244514 | 1.0 | +| arithmetic | vanilla | measurement | 761.0623009033172 | 761.0563015322481 | 1.0 | +| arithmetic | vanilla | measurement | 773.9119953423642 | 773.9125589821139 | 1.0 | +| arithmetic | vanilla | measurement | 779.1800480068675 | 779.1820470672611 | 1.0 | +| arithmetic | vanilla | measurement | 743.9381925566772 | 743.9399547552478 | 1.0 | +| arithmetic | vanilla | measurement | 748.3893746113941 | 748.3899437346524 | 1.0 | +| arithmetic | vanilla | measurement | 763.5274785646566 | 763.523753493966 | 1.0 | +| arithmetic | vanilla | measurement | 750.3185730481417 | 750.3022735322593 | 1.0 | +| arithmetic | vanilla | measurement | 741.5182399068215 | 741.5068662143655 | 1.0 | +| arithmetic | vanilla | measurement | 773.1227453010433 | 773.1236203695677 | 1.0 | +| arithmetic | vanilla | measurement | 747.6102426825483 | 747.6112778516745 | 1.0 | +| arithmetic | vanilla | measurement | 750.5999107387825 | 750.6012375963494 | 1.0 | +| arithmetic | vanilla | measurement | 758.2117610031188 | 758.2017689973678 | 1.0 | +| arithmetic | vanilla | measurement | 756.0790708479337 | 756.0722051826405 | 1.0 | +| arithmetic | vanilla | measurement | 760.3699476599177 | 760.3515153043711 | 1.0 | +| arithmetic | vanilla | measurement | 765.7969293666088 | 765.7808447806805 | 1.0 | +| arithmetic | vanilla | measurement | 760.4979126665603 | 760.4993886585489 | 1.0 | +| arithmetic | vanilla | measurement | 760.7685170652778 | 760.4262535789492 | 1.0 | +| arithmetic | vanilla | measurement | 758.7680247035157 | 758.7600964548777 | 1.0 | +| arithmetic | vanilla | measurement | 765.4752255933706 | 765.4792708642905 | 1.0 | +| arithmetic | vanilla | measurement | 784.8301249344532 | 784.8319978858005 | 1.0 | +| arithmetic | vanilla | measurement | 765.4722911535139 | 765.4755096065613 | 1.0 | +| arithmetic | vanilla | measurement | 755.4673269524541 | 755.4674870521714 | 1.0 | +| arithmetic | vanilla | measurement | 781.8975523300039 | 781.886854130093 | 1.0 | +| arithmetic | vanilla | measurement | 753.2713204639175 | 753.2394451761055 | 1.0 | +| arithmetic | vanilla | measurement | 751.0369381816973 | 751.026774014209 | 1.0 | +| arithmetic | vanilla | measurement | 751.3253104141113 | 751.2594818236958 | 1.0 | +| arithmetic | vanilla | measurement | 749.1344885407492 | 749.1345642040146 | 1.0 | +| arithmetic | vanilla | measurement | 751.6837944615038 | 751.6842572173961 | 1.0 | +| arithmetic | vanilla | measurement | 799.4231393315299 | 799.3434118691126 | 32.0 | +| arithmetic | vanilla | measurement | 792.839467521623 | 792.8303008788721 | 32.0 | +| arithmetic | vanilla | measurement | 807.1811857668533 | 807.1731012567636 | 32.0 | +| arithmetic | vanilla | measurement | 780.1608004359199 | 780.1511997938858 | 32.0 | +| arithmetic | vanilla | measurement | 777.250704531349 | 777.2520672182343 | 32.0 | +| arithmetic | vanilla | measurement | 757.2426704074553 | 757.2318158655639 | 32.0 | +| arithmetic | vanilla | measurement | 801.7762365789041 | 801.7179330680436 | 32.0 | +| arithmetic | vanilla | measurement | 801.9850001436089 | 801.985477655951 | 32.0 | +| arithmetic | vanilla | measurement | 771.2267922474311 | 771.2272044888467 | 32.0 | +| arithmetic | vanilla | measurement | 789.3089645295903 | 789.2082516962048 | 32.0 | +| arithmetic | vanilla | measurement | 813.7674455355893 | 810.3502859923835 | 32.0 | +| arithmetic | vanilla | measurement | 790.6304726446336 | 787.3940282270731 | 32.0 | +| arithmetic | vanilla | measurement | 773.4529077311772 | 772.5679997709748 | 32.0 | +| arithmetic | vanilla | measurement | 775.7915421823997 | 775.7950462340046 | 32.0 | +| arithmetic | vanilla | measurement | 769.401249320479 | 769.4007683719343 | 32.0 | +| arithmetic | vanilla | measurement | 797.9947817136758 | 791.5737848902127 | 32.0 | +| arithmetic | vanilla | measurement | 779.2191967005299 | 779.2209441470369 | 32.0 | +| arithmetic | vanilla | measurement | 789.4209143743326 | 789.3751853654388 | 32.0 | +| arithmetic | vanilla | measurement | 851.1407254302585 | 839.1518662506093 | 32.0 | +| arithmetic | vanilla | measurement | 822.5891683596035 | 807.2459981105593 | 32.0 | +| arithmetic | vanilla | measurement | 827.1450803025248 | 812.934027654514 | 32.0 | +| arithmetic | vanilla | measurement | 815.0800263374626 | 798.6080249634934 | 32.0 | +| arithmetic | vanilla | measurement | 827.4736846922378 | 811.9264412699342 | 32.0 | +| arithmetic | vanilla | measurement | 810.1951034897797 | 799.0324330823588 | 32.0 | +| arithmetic | vanilla | measurement | 799.6908774436223 | 797.1943122155124 | 32.0 | +| arithmetic | vanilla | measurement | 790.4080593180399 | 790.3724222037764 | 32.0 | +| arithmetic | vanilla | measurement | 797.1540728853762 | 797.1136892731413 | 32.0 | +| arithmetic | vanilla | measurement | 813.771385873542 | 813.634881337497 | 32.0 | +| arithmetic | vanilla | measurement | 841.2466909050114 | 841.2061435401266 | 32.0 | +| arithmetic | vanilla | measurement | 835.9091477497803 | 832.5378237095982 | 32.0 | +| arithmetic | vanilla | measurement | 834.3413918865364 | 828.5528510492131 | 32.0 | +| arithmetic | vanilla | measurement | 822.8340133400028 | 818.0432234977552 | 32.0 | +| arithmetic | vanilla | measurement | 817.6634496585389 | 813.088863187426 | 32.0 | +| arithmetic | vanilla | measurement | 815.647351634595 | 810.5290131974489 | 32.0 | +| arithmetic | vanilla | measurement | 823.8009481559195 | 815.0996146689167 | 32.0 | +| arithmetic | vanilla | measurement | 787.8816375144411 | 787.6498147777023 | 32.0 | +| arithmetic | vanilla | measurement | 782.1995465344049 | 782.1950531046899 | 32.0 | +| arithmetic | vanilla | measurement | 781.2749614952904 | 781.2768303226345 | 32.0 | +| arithmetic | vanilla | measurement | 788.7166803128617 | 788.7152374681474 | 32.0 | +| arithmetic | vanilla | measurement | 810.9040829061861 | 810.9080541639254 | 32.0 | +| arithmetic | vanilla | measurement | 818.318222780378 | 818.2977927915075 | 32.0 | +| arithmetic | vanilla | measurement | 787.8710097047249 | 787.8635080587464 | 32.0 | +| arithmetic | vanilla | measurement | 802.6580573130539 | 802.6032372391292 | 32.0 | +| arithmetic | vanilla | measurement | 809.534759383135 | 809.5353433912584 | 32.0 | +| arithmetic | vanilla | measurement | 815.6922916614728 | 815.6963236094028 | 32.0 | +| arithmetic | vanilla | measurement | 802.4399679374104 | 802.4393381237206 | 32.0 | +| arithmetic | vanilla | measurement | 831.9165635119534 | 831.8968091380185 | 32.0 | +| arithmetic | vanilla | measurement | 815.7628410282749 | 815.2359978242791 | 32.0 | +| arithmetic | vanilla | measurement | 798.047159256046 | 798.0437296384335 | 32.0 | +| arithmetic | vanilla | measurement | 801.6123981563063 | 801.6133566173393 | 32.0 | +| arithmetic | vanilla | measurement | 1609.1744390348708 | 1609.1209913676068 | 1024.0 | +| arithmetic | vanilla | measurement | 1690.6170982860706 | 1690.0549676457904 | 1024.0 | +| arithmetic | vanilla | measurement | 1611.3780428336256 | 1609.005088830346 | 1024.0 | +| arithmetic | vanilla | measurement | 1671.336695223913 | 1670.9555043529904 | 1024.0 | +| arithmetic | vanilla | measurement | 1737.047181480137 | 1730.468951926303 | 1024.0 | +| arithmetic | vanilla | measurement | 1766.9685127158384 | 1766.9127586381348 | 1024.0 | +| arithmetic | vanilla | measurement | 1588.2588864818172 | 1588.2404972474692 | 1024.0 | +| arithmetic | vanilla | measurement | 1682.208947878868 | 1680.912917304788 | 1024.0 | +| arithmetic | vanilla | measurement | 1643.2361833526359 | 1643.233623992224 | 1024.0 | +| arithmetic | vanilla | measurement | 1703.6614306680335 | 1703.643846263515 | 1024.0 | +| arithmetic | vanilla | measurement | 1665.9098911407928 | 1665.9103119524304 | 1024.0 | +| arithmetic | vanilla | measurement | 1658.9306304812817 | 1653.5303421220683 | 1024.0 | +| arithmetic | vanilla | measurement | 1623.6143043754373 | 1616.7326489971922 | 1024.0 | +| arithmetic | vanilla | measurement | 1653.1023284924381 | 1653.0537397039213 | 1024.0 | +| arithmetic | vanilla | measurement | 1644.4696624769304 | 1644.47319913354 | 1024.0 | +| arithmetic | vanilla | measurement | 1682.1525798285445 | 1682.1547206776938 | 1024.0 | +| arithmetic | vanilla | measurement | 1719.6431058163246 | 1719.63633144312 | 1024.0 | +| arithmetic | vanilla | measurement | 1681.7786761234966 | 1681.5671205912386 | 1024.0 | +| arithmetic | vanilla | measurement | 1613.6447706685321 | 1613.568440513792 | 1024.0 | +| arithmetic | vanilla | measurement | 1609.356724018054 | 1608.9844805621817 | 1024.0 | +| arithmetic | vanilla | measurement | 1656.7508243764112 | 1656.651666919625 | 1024.0 | +| arithmetic | vanilla | measurement | 1681.5887590426819 | 1681.5870045116744 | 1024.0 | +| arithmetic | vanilla | measurement | 1710.993740713979 | 1710.9646472311397 | 1024.0 | +| arithmetic | vanilla | measurement | 1669.0191296773257 | 1669.0168508579507 | 1024.0 | +| arithmetic | vanilla | measurement | 1638.7054135233195 | 1638.7027138895298 | 1024.0 | +| arithmetic | vanilla | measurement | 1691.1376421663008 | 1691.1413236937697 | 1024.0 | +| arithmetic | vanilla | measurement | 1728.321746068102 | 1728.3204583396564 | 1024.0 | +| arithmetic | vanilla | measurement | 1661.1459756154145 | 1661.1429379544231 | 1024.0 | +| arithmetic | vanilla | measurement | 1622.395788205591 | 1622.3089147661033 | 1024.0 | +| arithmetic | vanilla | measurement | 1648.8280306477307 | 1648.8306658940362 | 1024.0 | +| arithmetic | vanilla | measurement | 1627.8422669541694 | 1625.5742605904209 | 1024.0 | +| arithmetic | vanilla | measurement | 1707.3267544145972 | 1707.3028624383253 | 1024.0 | +| arithmetic | vanilla | measurement | 1660.418263681009 | 1658.6419123700236 | 1024.0 | +| arithmetic | vanilla | measurement | 1616.956143619305 | 1616.9054898660293 | 1024.0 | +| arithmetic | vanilla | measurement | 1633.5055142408967 | 1633.4924161941335 | 1024.0 | +| arithmetic | vanilla | measurement | 1594.5922865009634 | 1594.5720001655804 | 1024.0 | +| arithmetic | vanilla | measurement | 1615.6975031844017 | 1615.6972709336512 | 1024.0 | +| arithmetic | vanilla | measurement | 1668.2646467709535 | 1668.257573458061 | 1024.0 | +| arithmetic | vanilla | measurement | 1659.4387638707308 | 1659.434063659797 | 1024.0 | +| arithmetic | vanilla | measurement | 1609.2287329201135 | 1609.2060274930234 | 1024.0 | +| arithmetic | vanilla | measurement | 1671.2142965540447 | 1670.363877812898 | 1024.0 | +| arithmetic | vanilla | measurement | 1625.1001140557923 | 1625.0922750037869 | 1024.0 | +| arithmetic | vanilla | measurement | 1631.8991248047864 | 1631.8727815413004 | 1024.0 | +| arithmetic | vanilla | measurement | 1608.6925431268037 | 1608.6919981419924 | 1024.0 | +| arithmetic | vanilla | measurement | 1622.3218564467677 | 1617.4035421754522 | 1024.0 | +| arithmetic | vanilla | measurement | 1632.6106067487883 | 1627.0667940598878 | 1024.0 | +| arithmetic | vanilla | measurement | 1580.6848351475755 | 1580.0031135455265 | 1024.0 | +| arithmetic | vanilla | measurement | 1686.2803685659949 | 1686.2807870785648 | 1024.0 | +| arithmetic | vanilla | measurement | 1673.447288178401 | 1646.5058223761448 | 1024.0 | +| arithmetic | vanilla | measurement | 1650.3715880905345 | 1613.3599341418496 | 1024.0 | +| arithmetic | vanilla | measurement | 1787.2793145253897 | 1778.4245653288035 | 32768.0 | +| arithmetic | vanilla | measurement | 1745.0570822329707 | 1712.712382660661 | 32768.0 | +| arithmetic | vanilla | measurement | 1814.9603577393025 | 1782.0925309050706 | 32768.0 | +| arithmetic | vanilla | measurement | 1940.5818000591187 | 1939.8476400885236 | 32768.0 | +| arithmetic | vanilla | measurement | 1669.2724210337183 | 1669.2291320325485 | 32768.0 | +| arithmetic | vanilla | measurement | 1701.1096896379554 | 1700.7119108033453 | 32768.0 | +| arithmetic | vanilla | measurement | 1619.6671437333748 | 1619.6632188792996 | 32768.0 | +| arithmetic | vanilla | measurement | 1601.335684283475 | 1601.337874829675 | 32768.0 | +| arithmetic | vanilla | measurement | 1615.9230591361013 | 1615.9217841830962 | 32768.0 | +| arithmetic | vanilla | measurement | 1704.9848536940378 | 1704.8378810794857 | 32768.0 | +| arithmetic | vanilla | measurement | 1679.2324944110528 | 1679.1568972413347 | 32768.0 | +| arithmetic | vanilla | measurement | 1642.933718111633 | 1642.894922690394 | 32768.0 | +| arithmetic | vanilla | measurement | 1614.5185243070478 | 1614.5167931202475 | 32768.0 | +| arithmetic | vanilla | measurement | 1627.8727985228265 | 1627.8275814656797 | 32768.0 | +| arithmetic | vanilla | measurement | 1679.478613302856 | 1679.4693667738027 | 32768.0 | +| arithmetic | vanilla | measurement | 1626.587274849193 | 1626.5849718135971 | 32768.0 | +| arithmetic | vanilla | measurement | 1687.0231772566156 | 1686.8952820518728 | 32768.0 | +| arithmetic | vanilla | measurement | 1679.4308338468277 | 1679.4282901891843 | 32768.0 | +| arithmetic | vanilla | measurement | 1640.8502806099373 | 1640.8143350874363 | 32768.0 | +| arithmetic | vanilla | measurement | 1729.6812244540447 | 1728.7213573240754 | 32768.0 | +| arithmetic | vanilla | measurement | 1714.4259558981457 | 1714.3981350699535 | 32768.0 | +| arithmetic | vanilla | measurement | 1621.6680155762497 | 1621.660528355202 | 32768.0 | +| arithmetic | vanilla | measurement | 1595.2502093629935 | 1595.2490844093493 | 32768.0 | +| arithmetic | vanilla | measurement | 1631.1609127152346 | 1631.1593658987897 | 32768.0 | +| arithmetic | vanilla | measurement | 1652.0163993877622 | 1651.9869192405397 | 32768.0 | +| arithmetic | vanilla | measurement | 1617.5367017440776 | 1617.5013843231304 | 32768.0 | +| arithmetic | vanilla | measurement | 1704.846455760332 | 1702.741350324387 | 32768.0 | +| arithmetic | vanilla | measurement | 1703.7565966283332 | 1693.1664937564954 | 32768.0 | +| arithmetic | vanilla | measurement | 1754.7727460212732 | 1754.340221616732 | 32768.0 | +| arithmetic | vanilla | measurement | 1678.2588277887191 | 1639.8599583765981 | 32768.0 | +| arithmetic | vanilla | measurement | 1662.2700086246714 | 1634.3638488556742 | 32768.0 | +| arithmetic | vanilla | measurement | 1655.2223979110843 | 1629.148719422987 | 32768.0 | +| arithmetic | vanilla | measurement | 1665.7835237417528 | 1636.264721322923 | 32768.0 | +| arithmetic | vanilla | measurement | 1680.616017528572 | 1648.3814950689712 | 32768.0 | +| arithmetic | vanilla | measurement | 1752.3959164059343 | 1728.7069766133836 | 32768.0 | +| arithmetic | vanilla | measurement | 1669.390797846056 | 1669.337740459704 | 32768.0 | +| arithmetic | vanilla | measurement | 1676.2403847351165 | 1672.0014311963585 | 32768.0 | +| arithmetic | vanilla | measurement | 1634.836287388197 | 1634.8142569654237 | 32768.0 | +| arithmetic | vanilla | measurement | 1687.7195636385075 | 1687.6651531818138 | 32768.0 | +| arithmetic | vanilla | measurement | 1650.510874592383 | 1650.4191561566627 | 32768.0 | +| arithmetic | vanilla | measurement | 1668.145453919917 | 1668.1490850342504 | 32768.0 | +| arithmetic | vanilla | measurement | 1658.71565128626 | 1658.603914853222 | 32768.0 | +| arithmetic | vanilla | measurement | 1647.3613270727265 | 1647.3702048673756 | 32768.0 | +| arithmetic | vanilla | measurement | 1638.665290674642 | 1638.5891935302482 | 32768.0 | +| arithmetic | vanilla | measurement | 1688.7960326514014 | 1688.7964263839822 | 32768.0 | +| arithmetic | vanilla | measurement | 1634.5530136375812 | 1634.5584759321757 | 32768.0 | +| arithmetic | vanilla | measurement | 1727.8173349751407 | 1727.6869367398874 | 32768.0 | +| arithmetic | vanilla | measurement | 1636.126942113468 | 1636.127345224506 | 32768.0 | +| arithmetic | vanilla | measurement | 1661.6555785402636 | 1661.567997450135 | 32768.0 | +| arithmetic | vanilla | measurement | 1712.8865292537746 | 1691.9483925602754 | 32768.0 | +| complete | vanilla | measurement | 22792.3851184786 | 22759.790567306973 | 1.0 | +| complete | vanilla | measurement | 23203.624486148277 | 23118.266948202403 | 1.0 | +| complete | vanilla | measurement | 24437.947081252125 | 24188.152814111672 | 1.0 | +| complete | vanilla | measurement | 24410.703341079585 | 24330.484901711654 | 1.0 | +| complete | vanilla | measurement | 24124.182898589417 | 24020.804843411315 | 1.0 | +| complete | vanilla | measurement | 24155.325024276965 | 23968.298153823165 | 1.0 | +| complete | vanilla | measurement | 24780.18181475385 | 24444.825809103822 | 1.0 | +| complete | vanilla | measurement | 25548.30387179554 | 25401.63136258316 | 1.0 | +| complete | vanilla | measurement | 23486.370020200186 | 23340.78458778683 | 1.0 | +| complete | vanilla | measurement | 23760.35264220735 | 23582.124037670983 | 1.0 | +| complete | vanilla | measurement | 24136.772666182125 | 23936.80488078331 | 1.0 | +| complete | vanilla | measurement | 25552.983817874494 | 25415.716234397176 | 1.0 | +| complete | vanilla | measurement | 25193.66936991574 | 24936.470737723328 | 1.0 | +| complete | vanilla | measurement | 24901.95989979383 | 24608.717467673174 | 1.0 | +| complete | vanilla | measurement | 23956.943979406493 | 23732.903767097672 | 1.0 | +| complete | vanilla | measurement | 25029.20595705341 | 24858.605650646477 | 1.0 | +| complete | vanilla | measurement | 24957.038530553516 | 24856.431272890353 | 1.0 | +| complete | vanilla | measurement | 24399.096868255547 | 24123.833844083973 | 1.0 | +| complete | vanilla | measurement | 24196.049106820537 | 23899.28892293889 | 1.0 | +| complete | vanilla | measurement | 25542.33190076546 | 25512.69702518877 | 1.0 | +| complete | vanilla | measurement | 35583.70039496416 | 35111.39183354764 | 32.0 | +| complete | vanilla | measurement | 31072.11955721301 | 30814.789454345024 | 32.0 | +| complete | vanilla | measurement | 34756.03968404421 | 34397.73566966745 | 32.0 | +| complete | vanilla | measurement | 32993.72836674032 | 32660.276961234576 | 32.0 | +| complete | vanilla | measurement | 29274.58722211107 | 29038.10343560535 | 32.0 | +| complete | vanilla | measurement | 30905.559480022068 | 30728.66489068537 | 32.0 | +| complete | vanilla | measurement | 31758.99889765729 | 31520.895278339216 | 32.0 | +| complete | vanilla | measurement | 33703.312098068505 | 33402.315956274 | 32.0 | +| complete | vanilla | measurement | 30585.89821788914 | 30258.01566231861 | 32.0 | +| complete | vanilla | measurement | 34651.7127503011 | 34358.01405474912 | 32.0 | +| complete | vanilla | measurement | 33578.90327025094 | 33394.26988792953 | 32.0 | +| complete | vanilla | measurement | 32262.492283684605 | 32103.09815359187 | 32.0 | +| complete | vanilla | measurement | 31068.125620092163 | 30884.79579276128 | 32.0 | +| complete | vanilla | measurement | 35098.88471429041 | 34734.11937350737 | 32.0 | +| complete | vanilla | measurement | 31326.039224726457 | 31265.810169024448 | 32.0 | +| complete | vanilla | measurement | 29679.551350382844 | 29667.7608855411 | 32.0 | +| complete | vanilla | measurement | 34160.38177474951 | 33780.28026823446 | 32.0 | +| complete | vanilla | measurement | 32381.71918055487 | 32345.97354400146 | 32.0 | +| complete | vanilla | measurement | 31777.50275584732 | 31541.13577071475 | 32.0 | +| complete | vanilla | measurement | 31529.369465399574 | 31490.999035458277 | 32.0 | +| complete | vanilla | measurement | 30595.669673736404 | 30589.463958908727 | 1024.0 | +| complete | vanilla | measurement | 29877.091289660122 | 29468.467843577375 | 1024.0 | +| complete | vanilla | measurement | 34749.25064746224 | 34318.37802140886 | 1024.0 | +| complete | vanilla | measurement | 32068.20139848096 | 31617.809133287432 | 1024.0 | +| complete | vanilla | measurement | 30824.032846989292 | 30821.112353245888 | 1024.0 | +| complete | vanilla | measurement | 30170.31060083666 | 30089.372453384152 | 1024.0 | +| complete | vanilla | measurement | 29264.817506870048 | 29198.94220476518 | 1024.0 | +| complete | vanilla | measurement | 30249.132208209077 | 30214.929816988766 | 1024.0 | +| complete | vanilla | measurement | 30166.86157630598 | 30166.832743439252 | 1024.0 | +| complete | vanilla | measurement | 31835.437068406645 | 31834.656379488897 | 1024.0 | +| complete | vanilla | measurement | 33134.298687868526 | 33134.2911774864 | 1024.0 | +| complete | vanilla | measurement | 32381.86023820969 | 32343.311723066076 | 1024.0 | +| complete | vanilla | measurement | 30340.40689748664 | 30079.613777624414 | 1024.0 | +| complete | vanilla | measurement | 32355.056500382514 | 32126.012258287417 | 1024.0 | +| complete | vanilla | measurement | 34694.4364640674 | 34191.56176622917 | 1024.0 | +| complete | vanilla | measurement | 36885.53431457356 | 36467.54881733455 | 1024.0 | +| complete | vanilla | measurement | 35027.89938707058 | 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complete | vanilla | measurement | 36349.79934101258 | 36349.751696870415 | 32768.0 | +| complete | vanilla | measurement | 37353.11288310155 | 37353.031301483 | 32768.0 | +| complete | vanilla | measurement | 37442.02181216463 | 36854.76112026329 | 32768.0 | +| complete | vanilla | measurement | 37787.89074140884 | 37014.779505765764 | 32768.0 | +| complete | vanilla | measurement | 38519.35018124013 | 37777.27670510716 | 32768.0 | +| complete | vanilla | measurement | 37983.14266889149 | 37299.05871499188 | 32768.0 | +| complete | vanilla | measurement | 39508.65311369559 | 38880.46464579838 | 32768.0 | +| complete | vanilla | measurement | 38163.34425035981 | 37575.58892915995 | 32768.0 | +| complete | vanilla | measurement | 37972.42741358061 | 37299.12718286648 | 32768.0 | +| complete | vanilla | measurement | 41130.08507421065 | 40588.81344316304 | 32768.0 | +| complete | vanilla | measurement | 38845.02991766798 | 38391.072751235704 | 32768.0 | +| complete | vanilla | measurement | 37005.04230637997 | 37005.051927512555 | 32768.0 | +| nop | vanilla | measurement | 303.518526960116 | 301.9014828206564 | 1.0 | +| nop | vanilla | measurement | 328.00512935117064 | 327.2114961197941 | 1.0 | +| nop | vanilla | measurement | 314.50027815324444 | 312.62834862771047 | 1.0 | +| nop | vanilla | measurement | 313.9945984695294 | 309.9417493344898 | 1.0 | +| nop | vanilla | measurement | 310.23631881672645 | 306.25389315105474 | 1.0 | +| nop | vanilla | measurement | 312.31619684500987 | 308.06040862208977 | 1.0 | +| nop | vanilla | measurement | 309.919321367975 | 305.78170782170776 | 1.0 | +| nop | vanilla | measurement | 311.39033433714485 | 307.72232285703535 | 1.0 | +| nop | vanilla | measurement | 311.690600098201 | 307.99051534711623 | 1.0 | +| nop | vanilla | measurement | 310.4323547440936 | 305.7173917757776 | 1.0 | +| nop | vanilla | measurement | 310.9938115865003 | 305.7588417137076 | 1.0 | +| nop | vanilla | measurement | 310.44213503193436 | 306.1118189724036 | 1.0 | +| nop | vanilla | measurement | 308.91487843817936 | 304.42753607474054 | 1.0 | +| nop | vanilla | measurement | 318.68316187544 | 314.0424687271111 | 1.0 | +| nop | vanilla | measurement | 312.2969739481846 | 307.2380921822759 | 1.0 | +| nop | vanilla | measurement | 310.80569495218964 | 307.1913491433016 | 1.0 | +| nop | vanilla | measurement | 312.33587337241556 | 307.7371705365466 | 1.0 | +| nop | vanilla | measurement | 313.6466500999225 | 308.0794974209413 | 1.0 | +| nop | vanilla | measurement | 310.85350280727914 | 305.635719580712 | 1.0 | +| nop | vanilla | measurement | 309.22869471452634 | 305.2533948206384 | 1.0 | +| nop | vanilla | measurement | 330.75083138598546 | 326.6084585543005 | 32.0 | +| nop | vanilla | measurement | 332.6648078724064 | 328.22081538818026 | 32.0 | +| nop | vanilla | measurement | 332.8981989037112 | 327.83205694516977 | 32.0 | +| nop | vanilla | measurement | 333.40105479491143 | 329.0613293779696 | 32.0 | +| nop | vanilla | measurement | 333.4572772050267 | 327.4372041084082 | 32.0 | +| nop | vanilla | measurement | 334.44057824586474 | 328.4662437672578 | 32.0 | +| nop | vanilla | measurement | 332.09816842648036 | 329.8143280999303 | 32.0 | +| nop | vanilla | measurement | 326.88920780885803 | 326.88965174960214 | 32.0 | +| nop | vanilla | measurement | 325.0502761532662 | 325.04652412446626 | 32.0 | +| nop | vanilla | measurement | 327.6612077535455 | 327.65661313431985 | 32.0 | +| nop | vanilla | measurement | 328.5344554039851 | 327.30260613411804 | 32.0 | +| nop | vanilla | measurement | 328.2994650536226 | 328.30002929724753 | 32.0 | +| nop | vanilla | measurement | 328.44165059127045 | 328.44223464878553 | 32.0 | +| nop | vanilla | measurement | 329.50903850731527 | 329.5101353853671 | 32.0 | +| nop | vanilla | measurement | 324.3136216160352 | 324.29934852544204 | 32.0 | +| nop | vanilla | measurement | 330.27945894944486 | 330.2732900149226 | 32.0 | +| nop | vanilla | measurement | 330.1975177063575 | 330.19785644159754 | 32.0 | +| nop | vanilla | measurement | 326.89945995897074 | 326.8948860969212 | 32.0 | +| nop | vanilla | measurement | 336.33790153435376 | 336.33913145217554 | 32.0 | +| nop | vanilla | measurement | 348.2359947591225 | 348.21233617011865 | 32.0 | +| nop | vanilla | measurement | 961.1765567078551 | 961.1280707357316 | 1024.0 | +| nop | vanilla | measurement | 932.1480651417346 | 932.1315413878079 | 1024.0 | +| nop | vanilla | measurement | 985.2599102956906 | 985.2589267545453 | 1024.0 | +| nop | vanilla | measurement | 957.8535952908071 | 957.8449539884227 | 1024.0 | +| nop | vanilla | measurement | 942.4053879888493 | 928.0729218481512 | 1024.0 | +| nop | vanilla | measurement | 906.3546198105088 | 890.0827515940136 | 1024.0 | +| nop | vanilla | measurement | 888.757977130982 | 884.2308619539616 | 1024.0 | +| nop | vanilla | measurement | 940.8074438125454 | 940.792631364743 | 1024.0 | +| nop | vanilla | measurement | 859.1868655037314 | 859.1914539432047 | 1024.0 | +| nop | vanilla | measurement | 837.65754160489 | 837.6589504951687 | 1024.0 | +| nop | vanilla | measurement | 848.949561081648 | 848.9513232567613 | 1024.0 | +| nop | vanilla | measurement | 844.9993386551943 | 844.9798218888498 | 1024.0 | +| nop | vanilla | measurement | 895.5930293040003 | 895.5679476236763 | 1024.0 | +| nop | vanilla | measurement | 851.0907414406681 | 851.0694653273921 | 1024.0 | +| nop | vanilla | measurement | 858.0790859299923 | 858.0763472800115 | 1024.0 | +| nop | vanilla | measurement | 849.775235710253 | 849.7764651352037 | 1024.0 | +| nop | vanilla | measurement | 867.2048306498549 | 867.203547526458 | 1024.0 | +| nop | vanilla | measurement | 904.9285562882774 | 904.8959991408119 | 1024.0 | +| nop | vanilla | measurement | 807.0520823923956 | 806.0381686940358 | 1024.0 | +| nop | vanilla | measurement | 971.0577589417946 | 967.017455005877 | 1024.0 | +| nop | vanilla | measurement | 1112.1677915303087 | 1111.9716139390378 | 32768.0 | +| nop | vanilla | measurement | 1082.2873931202844 | 1082.2714093954808 | 32768.0 | +| nop | vanilla | measurement | 1107.7751017067394 | 1107.6478165725491 | 32768.0 | +| nop | vanilla | measurement | 1056.0737090092593 | 1056.0732841275699 | 32768.0 | +| nop | vanilla | measurement | 1070.6960046342008 | 1070.6936226772682 | 32768.0 | +| nop | vanilla | measurement | 1070.9719109786386 | 1070.949320945413 | 32768.0 | +| nop | vanilla | measurement | 1034.7757954273766 | 1034.7422149361953 | 32768.0 | +| nop | vanilla | measurement | 1062.3453308408978 | 1062.3478349965105 | 32768.0 | +| nop | vanilla | measurement | 1001.2520040737043 | 997.6316091262539 | 32768.0 | +| nop | vanilla | measurement | 1004.3829740037672 | 1004.3612092886864 | 32768.0 | +| nop | vanilla | measurement | 1042.7626298112193 | 1042.7631862925791 | 32768.0 | +| nop | vanilla | measurement | 973.3301912326397 | 973.1977962685884 | 32768.0 | +| nop | vanilla | measurement | 1021.5056493898867 | 1021.5057095481235 | 32768.0 | +| nop | vanilla | measurement | 1004.2440686094927 | 1004.2421152964064 | 32768.0 | +| nop | vanilla | measurement | 1129.3200524140589 | 1129.2717590747477 | 32768.0 | +| nop | vanilla | measurement | 1071.8831394442268 | 1071.8806916881674 | 32768.0 | +| nop | vanilla | measurement | 1044.9765056917904 | 1044.9798464419187 | 32768.0 | +| nop | vanilla | measurement | 1075.3501737126123 | 1075.3331860669682 | 32768.0 | +| nop | vanilla | measurement | 998.7915798751891 | 998.7579354634811 | 32768.0 | +| nop | vanilla | measurement | 970.1559155966636 | 970.1556918761821 | 32768.0 | +| sans_regulation | vanilla | measurement | 23738.825068529306 | 23687.111459186217 | 1.0 | +| sans_regulation | vanilla | measurement | 29138.335481110378 | 28867.22409312591 | 1.0 | +| sans_regulation | vanilla | measurement | 23012.358299848787 | 23011.809155957417 | 1.0 | +| sans_regulation | vanilla | measurement | 22879.23470146041 | 22879.278024990213 | 1.0 | +| sans_regulation | vanilla | measurement | 21606.437150686063 | 21606.055284610735 | 1.0 | +| sans_regulation | vanilla | measurement | 24315.654444480944 | 24189.47043537076 | 1.0 | +| sans_regulation | vanilla | measurement | 24290.38660074855 | 24256.591363781998 | 1.0 | +| sans_regulation | vanilla | measurement | 22075.367413065887 | 22074.203623936493 | 1.0 | +| sans_regulation | vanilla | measurement | 24206.385247900253 | 24205.96742728989 | 1.0 | +| sans_regulation | vanilla | measurement | 23650.88106511718 | 23650.93748887542 | 1.0 | +| sans_regulation | vanilla | measurement | 21503.51151611538 | 21503.290128510896 | 1.0 | +| sans_regulation | vanilla | measurement | 22603.92969280309 | 22556.04364387171 | 1.0 | +| sans_regulation | vanilla | measurement | 22832.249652884573 | 22832.306575059636 | 1.0 | +| sans_regulation | vanilla | measurement | 21128.389199355846 | 21128.38321882456 | 1.0 | +| sans_regulation | vanilla | measurement | 21393.670748656168 | 21393.717098002944 | 1.0 | +| sans_regulation | vanilla | measurement | 23769.544658429677 | 23769.575878395255 | 1.0 | +| sans_regulation | vanilla | measurement | 21918.967534143452 | 21918.465487166657 | 1.0 | +| sans_regulation | vanilla | measurement | 21961.279840536732 | 21960.961304332315 | 1.0 | +| sans_regulation | vanilla | measurement | 24999.865544092972 | 24999.538998255666 | 1.0 | +| sans_regulation | vanilla | measurement | 22841.799829083673 | 22807.612865330524 | 1.0 | +| sans_regulation | vanilla | measurement | 30745.112784305216 | 30744.647329614105 | 32.0 | +| sans_regulation | vanilla | measurement | 30997.847618278956 | 30997.52120313653 | 32.0 | +| sans_regulation | vanilla | measurement | 26773.265556124945 | 26772.853001989643 | 32.0 | +| sans_regulation | vanilla | measurement | 26852.465376690587 | 26780.96020754502 | 32.0 | +| sans_regulation | vanilla | measurement | 28659.167791518234 | 28658.562478055628 | 32.0 | +| sans_regulation | vanilla | measurement | 28455.672726569195 | 28433.84110326529 | 32.0 | +| sans_regulation | vanilla | measurement | 30140.9071119436 | 30125.235165606835 | 32.0 | +| sans_regulation | vanilla | measurement | 31165.901025973508 | 31114.383021885893 | 32.0 | +| sans_regulation | vanilla | measurement | 31729.553622241394 | 31530.9841220302 | 32.0 | +| sans_regulation | vanilla | measurement | 27850.56922713811 | 27517.928919751797 | 32.0 | +| sans_regulation | vanilla | measurement | 27863.727421728825 | 27490.690008972866 | 32.0 | +| sans_regulation | vanilla | measurement | 29210.20563332853 | 28824.136932859972 | 32.0 | +| sans_regulation | vanilla | measurement | 31009.491631888774 | 30626.252018881896 | 32.0 | +| sans_regulation | vanilla | measurement | 31467.93746339954 | 30930.420824718043 | 32.0 | +| sans_regulation | vanilla | measurement | 31278.665470298427 | 31062.188155892687 | 32.0 | +| sans_regulation | vanilla | measurement | 27853.74961958565 | 27853.73222018489 | 32.0 | +| sans_regulation | vanilla | measurement | 27778.196270441887 | 27775.59665275228 | 32.0 | +| sans_regulation | vanilla | measurement | 32296.026138179943 | 32295.63316818164 | 32.0 | +| sans_regulation | vanilla | measurement | 31422.34525805611 | 31360.819607537156 | 32.0 | +| sans_regulation | vanilla | measurement | 28538.44423207017 | 28536.98146919987 | 32.0 | +| sans_regulation | vanilla | measurement | 30988.458258912495 | 30987.942876424135 | 1024.0 | +| sans_regulation | vanilla | measurement | 30206.436782936536 | 30206.389504187995 | 1024.0 | +| sans_regulation | vanilla | measurement | 31261.62300030161 | 31258.946528522098 | 1024.0 | +| sans_regulation | vanilla | measurement | 30122.97102140553 | 30122.031836767354 | 1024.0 | +| sans_regulation | vanilla | measurement | 35259.54166168986 | 35259.20963836303 | 1024.0 | +| sans_regulation | vanilla | measurement | 30799.627525710945 | 30796.55345162975 | 1024.0 | +| sans_regulation | vanilla | measurement | 29496.76960026235 | 29495.437537215574 | 1024.0 | +| sans_regulation | vanilla | measurement | 31268.153030877256 | 31266.79321980052 | 1024.0 | +| sans_regulation | vanilla | measurement | 32659.24810447719 | 32658.48084633393 | 1024.0 | +| sans_regulation | vanilla | measurement | 29186.145885495564 | 29185.87265293163 | 1024.0 | +| sans_regulation | vanilla | measurement | 31275.829462934722 | 31275.838077091026 | 1024.0 | +| sans_regulation | vanilla | measurement | 27903.06482474803 | 27902.45206621421 | 1024.0 | +| sans_regulation | vanilla | measurement | 32888.622166635985 | 32887.919653844634 | 1024.0 | +| sans_regulation | vanilla | measurement | 29790.59291010803 | 29788.202572347443 | 1024.0 | +| sans_regulation | vanilla | measurement | 33735.681354419474 | 33735.25894168561 | 1024.0 | +| sans_regulation | vanilla | measurement | 35563.131277063156 | 35560.49581199635 | 1024.0 | +| sans_regulation | vanilla | measurement | 30951.492159888523 | 30758.95990631588 | 1024.0 | +| sans_regulation | vanilla | measurement | 34846.644714379516 | 34189.19364058605 | 1024.0 | +| sans_regulation | vanilla | measurement | 33492.68897625004 | 32931.72216267731 | 1024.0 | +| sans_regulation | vanilla | measurement | 32136.118613754817 | 31705.88761859398 | 1024.0 | +| sans_regulation | vanilla | measurement | 36815.19566032653 | 36814.18047117172 | 32768.0 | +| sans_regulation | vanilla | measurement | 37040.27954127459 | 37040.17309361454 | 32768.0 | +| sans_regulation | vanilla | measurement | 36282.9871668029 | 36282.962492250495 | 32768.0 | +| sans_regulation | vanilla | measurement | 38338.96193431918 | 38337.865034098155 | 32768.0 | +| sans_regulation | vanilla | measurement | 35984.64990697935 | 35983.68301301959 | 32768.0 | +| sans_regulation | vanilla | measurement | 35774.72926232416 | 35774.66379417282 | 32768.0 | +| sans_regulation | vanilla | measurement | 36623.65306876741 | 36623.80991940504 | 32768.0 | +| sans_regulation | vanilla | measurement | 34905.897954092594 | 34904.353316800945 | 32768.0 | +| sans_regulation | vanilla | measurement | 33155.0525727931 | 33155.12901425897 | 32768.0 | +| sans_regulation | vanilla | measurement | 36185.56261625121 | 36184.490638561576 | 32768.0 | +| sans_regulation | vanilla | measurement | 35980.14513325999 | 35968.70334779924 | 32768.0 | +| sans_regulation | vanilla | measurement | 35395.238127783734 | 35395.27495350279 | 32768.0 | +| sans_regulation | vanilla | measurement | 35854.08121512595 | 35669.64730316179 | 32768.0 | +| sans_regulation | vanilla | measurement | 37071.68704287151 | 36384.45740855602 | 32768.0 | +| sans_regulation | vanilla | measurement | 37510.0232486373 | 36879.26769993876 | 32768.0 | +| sans_regulation | vanilla | measurement | 36601.62641038003 | 36601.41816490973 | 32768.0 | +| sans_regulation | vanilla | measurement | 36582.208369545406 | 36580.7269063855 | 32768.0 | +| sans_regulation | vanilla | measurement | 36695.067141943335 | 36695.07352758869 | 32768.0 | +| sans_regulation | vanilla | measurement | 35868.58921264258 | 35867.27334159941 | 32768.0 | +| sans_regulation | vanilla | measurement | 35831.20601363138 | 35830.7473031621 | 32768.0 | +| control | vanilla | measurement | 2.4450000637443736e-06 | 1.8740000000002331e-06 | 1.0 | +| control | vanilla | measurement | 1.9560002328944393e-06 | 1.3719999999999184e-06 | 1.0 | +| control | vanilla | measurement | 1.3970002328278497e-06 | 1.341999999999663e-06 | 1.0 | +| control | vanilla | measurement | 1.3970002328278497e-06 | 1.4130000000000219e-06 | 1.0 | +| control | vanilla | measurement | 1.396999323333148e-06 | 1.3729999999998257e-06 | 1.0 | +| control | vanilla | measurement | 1.4659999578725547e-06 | 1.3219999999997817e-06 | 1.0 | +| control | vanilla | measurement | 2.373999450355768e-06 | 1.6729999999997788e-06 | 1.0 | +| control | vanilla | measurement | 1.886000063677784e-06 | 1.453000000000218e-06 | 1.0 | +| control | vanilla | measurement | 9.769992175279185e-07 | 1.3220000000002154e-06 | 1.0 | +| control | vanilla | measurement | 1.4659999578725547e-06 | 1.2319999999998825e-06 | 1.0 | +| control | vanilla | measurement | 1.4669994925498031e-06 | 1.642999999999957e-06 | 1.0 | +| control | vanilla | measurement | 1.886000063677784e-06 | 1.7529999999997374e-06 | 1.0 | +| control | vanilla | measurement | 1.9560002328944393e-06 | 1.7139999999998823e-06 | 1.0 | +| control | vanilla | measurement | 1.4670004020445049e-06 | 1.311999999999841e-06 | 1.0 | +| control | vanilla | measurement | 1.4670004020445049e-06 | 1.3930000000001407e-06 | 1.0 | +| control | vanilla | measurement | 1.4659999578725547e-06 | 1.3629999999998851e-06 | 1.0 | +| control | vanilla | measurement | 9.080004019779153e-07 | 1.1730000000001461e-06 | 1.0 | +| control | vanilla | measurement | 1.4670004020445049e-06 | 1.432999999999903e-06 | 1.0 | +| control | vanilla | measurement | 1.3959997886558995e-06 | 1.2319999999998825e-06 | 1.0 | +| control | vanilla | measurement | 1.4669994925498031e-06 | 1.453000000000218e-06 | 1.0 | +| control | vanilla | measurement | 5.168999450688716e-06 | 2.61500000000095e-06 | 32.0 | +| control | vanilla | measurement | 3.3520000215503387e-06 | 2.2450000000014125e-06 | 32.0 | +| control | vanilla | measurement | 3.2819998523336835e-06 | 2.4139999999991946e-06 | 32.0 | +| control | vanilla | measurement | 1.886000063677784e-06 | 1.5429999999996835e-06 | 32.0 | +| control | vanilla | measurement | 5.658000191033352e-06 | 3.837999999999064e-06 | 32.0 | +| control | vanilla | measurement | 2.9340008040890098e-06 | 2.164000000002414e-06 | 32.0 | +| control | vanilla | measurement | 3.840999852400273e-06 | 2.505000000003338e-06 | 32.0 | +| control | vanilla | measurement | 5.169000360183418e-06 | 2.3639999999977568e-06 | 32.0 | +| control | vanilla | measurement | 1.3970002328278497e-06 | 1.3319999999997223e-06 | 32.0 | +| control | vanilla | measurement | 1.8159998944611289e-06 | 1.4129999999987208e-06 | 32.0 | +| control | vanilla | measurement | 5.308999789122026e-06 | 2.5549999999978368e-06 | 32.0 | +| control | vanilla | measurement | 9.080004019779153e-07 | 1.3430000000008713e-06 | 32.0 | +| control | vanilla | measurement | 2.3749998945277184e-06 | 1.6840000000017952e-06 | 32.0 | +| control | vanilla | measurement | 1.8159998944611289e-06 | 1.594000000000595e-06 | 32.0 | +| control | vanilla | measurement | 1.886000063677784e-06 | 1.7240000000015574e-06 | 32.0 | +| control | vanilla | measurement | 4.400000761961564e-06 | 2.8759999999988795e-06 | 32.0 | +| control | vanilla | measurement | 3.772000127355568e-06 | 2.835999999999117e-06 | 32.0 | +| control | vanilla | measurement | 3.3520000215503387e-06 | 2.0039999999998948e-06 | 32.0 | +| control | vanilla | measurement | 8.451000212517101e-06 | 3.6870000000022163e-06 | 32.0 | +| control | vanilla | measurement | 5.1679999160114676e-06 | 2.7860000000011487e-06 | 32.0 | +| control | vanilla | measurement | 3.422000190766994e-06 | 2.644999999967812e-06 | 1024.0 | +| control | vanilla | measurement | 6.565999683516566e-06 | 3.778000000009829e-06 | 1024.0 | +| control | vanilla | measurement | 4.260000423528254e-06 | 2.2839999999635374e-06 | 1024.0 | +| control | vanilla | measurement | 3.840999852400273e-06 | 2.4350000000228356e-06 | 1024.0 | +| control | vanilla | measurement | 3.282999387010932e-06 | 2.143999999981716e-06 | 1024.0 | +| control | vanilla | measurement | 3.7719992178608663e-06 | 2.3749999999989058e-06 | 1024.0 | +| control | vanilla | measurement | 3.772000127355568e-06 | 2.6049999999888662e-06 | 1024.0 | +| control | vanilla | measurement | 3.9120004657888785e-06 | 2.4149999999778515e-06 | 1024.0 | +| control | vanilla | measurement | 5.308999789122026e-06 | 3.1060000000304733e-06 | 1024.0 | +| control | vanilla | measurement | 3.352999556227587e-06 | 2.3650000000041693e-06 | 1024.0 | +| control | vanilla | measurement | 3.840999852400273e-06 | 2.3239999999979948e-06 | 1024.0 | +| control | vanilla | measurement | 6.356000085361302e-06 | 4.107999999947154e-06 | 1024.0 | +| control | vanilla | measurement | 3.422000190766994e-06 | 2.1049999999744884e-06 | 1024.0 | +| control | vanilla | measurement | 3.3520000215503387e-06 | 2.1040000000027703e-06 | 1024.0 | +| control | vanilla | measurement | 2.863999725377653e-06 | 1.8039999999386325e-06 | 1024.0 | +| control | vanilla | measurement | 3.772000127355568e-06 | 3.206000000033349e-06 | 1024.0 | +| control | vanilla | measurement | 2.8630001907004043e-06 | 1.5529999999319697e-06 | 1024.0 | +| control | vanilla | measurement | 2.3749998945277184e-06 | 1.5729999999214428e-06 | 1024.0 | +| control | vanilla | measurement | 3.282999387010932e-06 | 1.8739999999572987e-06 | 1024.0 | +| control | vanilla | measurement | 4.260000423528254e-06 | 2.5649999999544093e-06 | 1024.0 | +| control | vanilla | measurement | 3.352999556227587e-06 | 2.5450000000759587e-06 | 32768.0 | +| control | vanilla | measurement | 3.353000465722289e-06 | 2.073999999296916e-06 | 32768.0 | +| control | vanilla | measurement | 4.2609999582055025e-06 | 2.75499999879969e-06 | 32768.0 | +| control | vanilla | measurement | 3.770999683183618e-06 | 2.555000000015184e-06 | 32768.0 | +| control | vanilla | measurement | 3.770999683183618e-06 | 2.7249999998701924e-06 | 32768.0 | +| control | vanilla | measurement | 3.772000127355568e-06 | 2.3850000001601757e-06 | 32768.0 | +| control | vanilla | measurement | 3.2830002965056337e-06 | 2.0639999984695123e-06 | 32768.0 | +| control | vanilla | measurement | 3.772000127355568e-06 | 2.304000000563633e-06 | 32768.0 | +| control | vanilla | measurement | 3.3520000215503387e-06 | 1.984000000732067e-06 | 32768.0 | +| control | vanilla | measurement | 2.8640006348723546e-06 | 1.794000000998608e-06 | 32768.0 | +| control | vanilla | measurement | 3.282999387010932e-06 | 2.024000000488968e-06 | 32768.0 | +| control | vanilla | measurement | 4.260000423528254e-06 | 2.5440000008813968e-06 | 32768.0 | +| control | vanilla | measurement | 3.770999683183618e-06 | 2.645000002132747e-06 | 32768.0 | +| control | vanilla | measurement | 3.352999556227587e-06 | 2.443999999712787e-06 | 32768.0 | +| control | vanilla | measurement | 6.104299973230809e-05 | 1.1472000000622984e-05 | 32768.0 | +| control | vanilla | measurement | 3.772000127355568e-06 | 2.644999998580033e-06 | 32768.0 | +| control | vanilla | measurement | 3.840999852400273e-06 | 2.4950000003798323e-06 | 32768.0 | +| control | vanilla | measurement | 4.2609999582055025e-06 | 2.666000000317581e-06 | 32768.0 | +| control | vanilla | measurement | 3.422000190766994e-06 | 2.0940000027280803e-06 | 32768.0 | +| control | vanilla | measurement | 4.819000423594844e-06 | 2.5150000020346397e-06 | 32768.0 | +| arithmetic | lite | measurement | 37.25939968715988 | 36.94151981585546 | 1.0 | +| arithmetic | lite | measurement | 36.402936588815344 | 36.40106426639122 | 1.0 | +| arithmetic | lite | measurement | 36.074829256523444 | 36.07387516536972 | 1.0 | +| arithmetic | lite | measurement | 35.99263434335295 | 35.9921233925219 | 1.0 | +| arithmetic | lite | measurement | 36.25945405040838 | 36.25919192936358 | 1.0 | +| arithmetic | lite | measurement | 36.7614544884002 | 36.733730960786744 | 1.0 | +| arithmetic | lite | measurement | 36.76952561471063 | 36.76862854492684 | 1.0 | +| arithmetic | lite | measurement | 36.33801424364706 | 36.33771970287603 | 1.0 | +| arithmetic | lite | measurement | 36.1752151773578 | 36.17528032975224 | 1.0 | +| arithmetic | lite | measurement | 36.05462551001532 | 36.05392071781429 | 1.0 | +| arithmetic | lite | measurement | 36.25260976895211 | 36.251134563180486 | 1.0 | +| arithmetic | lite | measurement | 36.48383429242734 | 36.483939213787174 | 1.0 | +| arithmetic | lite | measurement | 35.837375072928275 | 35.8374131740572 | 1.0 | +| arithmetic | lite | measurement | 36.44457045775965 | 36.444735527810664 | 1.0 | +| arithmetic | lite | measurement | 36.24640893223913 | 36.246412841415335 | 1.0 | +| arithmetic | lite | measurement | 36.149649183153045 | 36.1489214574239 | 1.0 | +| arithmetic | lite | measurement | 36.11878307064271 | 36.118863807489205 | 1.0 | +| arithmetic | lite | measurement | 36.31701496077276 | 36.31642452401514 | 1.0 | +| arithmetic | lite | measurement | 36.29804582541118 | 36.29771792656216 | 1.0 | +| arithmetic | lite | measurement | 36.05173321435791 | 36.051387594094365 | 1.0 | +| arithmetic | lite | measurement | 35.997805616483355 | 35.996932939667225 | 1.0 | +| arithmetic | lite | measurement | 35.949528083309474 | 35.94892810817672 | 1.0 | +| arithmetic | lite | measurement | 35.980973843194505 | 35.98105494491295 | 1.0 | +| arithmetic | lite | measurement | 36.10124400357101 | 36.100690886039686 | 1.0 | +| arithmetic | lite | measurement | 36.287499378492825 | 36.286801257888825 | 1.0 | +| arithmetic | lite | measurement | 36.18101608286411 | 36.181079723658144 | 1.0 | +| arithmetic | lite | measurement | 36.033695392793895 | 36.03277935064259 | 1.0 | +| arithmetic | lite | measurement | 35.97146060849447 | 35.96110138342966 | 1.0 | +| arithmetic | lite | measurement | 36.28039917857458 | 36.280527919662084 | 1.0 | +| arithmetic | lite | measurement | 36.18431268792042 | 36.18437231539695 | 1.0 | +| arithmetic | lite | measurement | 35.90008593848849 | 35.90014832846675 | 1.0 | +| arithmetic | lite | measurement | 36.09280651238392 | 36.09192189973027 | 1.0 | +| arithmetic | lite | measurement | 36.354496852242356 | 36.35466510175903 | 1.0 | +| arithmetic | lite | measurement | 35.984361453193834 | 35.98346969982697 | 1.0 | +| arithmetic | lite | measurement | 35.883309779494766 | 35.88216455734594 | 1.0 | +| arithmetic | lite | measurement | 35.89901832599877 | 35.899071386032524 | 1.0 | +| arithmetic | lite | measurement | 36.32183404621953 | 36.321962735144034 | 1.0 | +| arithmetic | lite | measurement | 36.974672619832575 | 36.618242566715125 | 1.0 | +| arithmetic | lite | measurement | 37.053031518433095 | 36.62524952050457 | 1.0 | +| arithmetic | lite | measurement | 37.25437227052867 | 36.57855232292133 | 1.0 | +| arithmetic | lite | measurement | 36.59231036416791 | 36.10443141327766 | 1.0 | +| arithmetic | lite | measurement | 36.74839086708554 | 36.23262618526465 | 1.0 | +| arithmetic | lite | measurement | 36.32271099701327 | 36.18125751141542 | 1.0 | +| arithmetic | lite | measurement | 35.979543253833484 | 35.978978878022104 | 1.0 | +| arithmetic | lite | measurement | 36.01893984284767 | 36.018661668386144 | 1.0 | +| arithmetic | lite | measurement | 36.058405285023746 | 36.05848258175133 | 1.0 | +| arithmetic | lite | measurement | 35.943607349548 | 35.94334439464011 | 1.0 | +| arithmetic | lite | measurement | 36.59782204532298 | 36.59782360890047 | 1.0 | +| arithmetic | lite | measurement | 35.73477636942875 | 35.73492293622595 | 1.0 | +| arithmetic | lite | measurement | 36.412216213269645 | 36.41120541350432 | 1.0 | +| arithmetic | lite | measurement | 32.42259154614784 | 32.4221331587587 | 32.0 | +| arithmetic | lite | measurement | 32.56184321686113 | 32.5609011067292 | 32.0 | +| arithmetic | lite | measurement | 33.25640111083587 | 33.25643682933786 | 32.0 | +| arithmetic | lite | measurement | 32.7417871674457 | 32.74188583166995 | 32.0 | +| arithmetic | lite | measurement | 32.566543891583464 | 32.566534015965885 | 32.0 | +| arithmetic | lite | measurement | 32.49130429850818 | 32.49103396386513 | 32.0 | +| arithmetic | lite | measurement | 32.512245216077744 | 32.512313745780766 | 32.0 | +| arithmetic | lite | measurement | 32.734614347759496 | 32.73463862152054 | 32.0 | +| arithmetic | lite | measurement | 32.675238753262484 | 32.67530755983445 | 32.0 | +| arithmetic | lite | measurement | 32.69375925784872 | 32.69345555829265 | 32.0 | +| arithmetic | lite | measurement | 32.73311384759461 | 32.730725415079895 | 32.0 | +| arithmetic | lite | measurement | 32.690746443772525 | 32.69012819988608 | 32.0 | +| arithmetic | lite | measurement | 32.64667751940818 | 32.6451820494501 | 32.0 | +| arithmetic | lite | measurement | 32.77765541902101 | 32.77771988775722 | 32.0 | +| arithmetic | lite | measurement | 33.57988442896753 | 33.579967864423786 | 32.0 | +| arithmetic | lite | measurement | 33.830958729880535 | 33.83095988350769 | 32.0 | +| arithmetic | lite | measurement | 33.18071314429759 | 33.17958205845819 | 32.0 | +| arithmetic | lite | measurement | 32.95961474231308 | 32.95843584712516 | 32.0 | +| arithmetic | lite | measurement | 34.01271126409156 | 34.0124340995952 | 32.0 | +| arithmetic | lite | measurement | 32.99232389093645 | 32.99133341781485 | 32.0 | +| arithmetic | lite | measurement | 32.741985372807825 | 32.74109153341396 | 32.0 | +| arithmetic | lite | measurement | 33.46988962615244 | 33.46995741745466 | 32.0 | +| arithmetic | lite | measurement | 33.21912571628318 | 33.218326618655006 | 32.0 | +| arithmetic | lite | measurement | 32.38594284055586 | 32.38547914616344 | 32.0 | +| arithmetic | lite | measurement | 32.52318966034744 | 32.52282421485519 | 32.0 | +| arithmetic | lite | measurement | 32.571978272434094 | 32.572019113366956 | 32.0 | +| arithmetic | lite | measurement | 32.56263188499742 | 32.56157223614725 | 32.0 | +| arithmetic | lite | measurement | 32.480104517623566 | 32.47934612252592 | 32.0 | +| arithmetic | lite | measurement | 32.73881108707303 | 32.73900352394854 | 32.0 | +| arithmetic | lite | measurement | 32.641615460104646 | 32.640656598356365 | 32.0 | +| arithmetic | lite | measurement | 32.689688686956195 | 32.68912152883289 | 32.0 | +| arithmetic | lite | measurement | 33.213547538476966 | 33.21359040994242 | 32.0 | +| arithmetic | lite | measurement | 32.98226724069425 | 32.982336462563396 | 32.0 | +| arithmetic | lite | measurement | 32.6031560480617 | 32.59927237628023 | 32.0 | +| arithmetic | lite | measurement | 32.63885881325545 | 32.63847103215657 | 32.0 | +| arithmetic | lite | measurement | 32.48984837723871 | 32.48869790924928 | 32.0 | +| arithmetic | lite | measurement | 32.533522044585965 | 32.532904585175956 | 32.0 | +| arithmetic | lite | measurement | 32.526015852133646 | 32.52605406267515 | 32.0 | +| arithmetic | lite | measurement | 32.77923344773616 | 32.77929320930392 | 32.0 | +| arithmetic | lite | measurement | 32.922462424323435 | 32.91530700236769 | 32.0 | +| arithmetic | lite | measurement | 32.49278057090668 | 32.49247871726456 | 32.0 | +| arithmetic | lite | measurement | 32.56692212008771 | 32.56640997031727 | 32.0 | +| arithmetic | lite | measurement | 32.586886455102466 | 32.58686988802015 | 32.0 | +| arithmetic | lite | measurement | 32.66826090857516 | 32.66652551551633 | 32.0 | +| arithmetic | lite | measurement | 32.77596801989131 | 32.774667386451064 | 32.0 | +| arithmetic | lite | measurement | 32.46760696467977 | 32.467677986354765 | 32.0 | +| arithmetic | lite | measurement | 32.58184733143681 | 32.573311209397374 | 32.0 | +| arithmetic | lite | measurement | 32.43970550786237 | 32.43975340943299 | 32.0 | +| arithmetic | lite | measurement | 32.57845107408995 | 32.57796850514252 | 32.0 | +| arithmetic | lite | measurement | 32.606537630226654 | 32.6042002835141 | 32.0 | +| arithmetic | lite | measurement | 32.84358767327927 | 32.84312007385357 | 1024.0 | +| arithmetic | lite | measurement | 32.5195875672589 | 32.51770211676165 | 1024.0 | +| arithmetic | lite | measurement | 33.347582953724455 | 33.347216649951505 | 1024.0 | +| arithmetic | lite | measurement | 32.616582941585804 | 32.616630784942316 | 1024.0 | +| arithmetic | lite | measurement | 32.90814654090218 | 32.90810665541945 | 1024.0 | +| arithmetic | lite | measurement | 32.91461574997655 | 32.913572544082726 | 1024.0 | +| arithmetic | lite | measurement | 33.27580884643901 | 33.27530035493703 | 1024.0 | +| arithmetic | lite | measurement | 33.31779595298476 | 33.316916891138476 | 1024.0 | +| arithmetic | lite | measurement | 33.12952267804216 | 33.1288716152079 | 1024.0 | +| arithmetic | lite | measurement | 34.21239141220364 | 34.212130622739906 | 1024.0 | +| arithmetic | lite | measurement | 33.92403964628505 | 33.92299337235164 | 1024.0 | +| arithmetic | lite | measurement | 34.175856627538984 | 34.17554252961088 | 1024.0 | +| arithmetic | lite | measurement | 34.91898814447669 | 34.91699511839519 | 1024.0 | +| arithmetic | lite | measurement | 34.45236030534325 | 34.450848781075535 | 1024.0 | +| arithmetic | lite | measurement | 33.670909321642526 | 33.67100922700995 | 1024.0 | +| arithmetic | lite | measurement | 33.54052335364208 | 33.53988106369734 | 1024.0 | +| arithmetic | lite | measurement | 33.3892812263121 | 33.38936372975349 | 1024.0 | +| arithmetic | lite | measurement | 33.65128663893902 | 33.649686954345974 | 1024.0 | +| arithmetic | lite | measurement | 33.993609459247054 | 33.993305620296326 | 1024.0 | +| arithmetic | lite | measurement | 33.50559309543118 | 33.505011975942345 | 1024.0 | +| arithmetic | lite | measurement | 33.94137126472459 | 33.94138655727061 | 1024.0 | +| arithmetic | lite | measurement | 34.0418954466852 | 34.041919943669505 | 1024.0 | +| arithmetic | lite | measurement | 32.58865708616578 | 32.587089808466864 | 1024.0 | +| arithmetic | lite | measurement | 33.37752192178321 | 33.37773836745836 | 1024.0 | +| arithmetic | lite | measurement | 32.872406253301975 | 32.87029299554492 | 1024.0 | +| arithmetic | lite | measurement | 33.953902630640684 | 33.95346465777551 | 1024.0 | +| arithmetic | lite | measurement | 33.12852937696859 | 33.12820128077356 | 1024.0 | +| arithmetic | lite | measurement | 33.81159052965333 | 33.81124191546676 | 1024.0 | +| arithmetic | lite | measurement | 33.734719330313645 | 33.73389717233693 | 1024.0 | +| arithmetic | lite | measurement | 33.93367773530096 | 33.93366330553433 | 1024.0 | +| arithmetic | lite | measurement | 33.83948258789389 | 33.83954716207635 | 1024.0 | +| arithmetic | lite | measurement | 33.928780166315065 | 33.92798629244465 | 1024.0 | +| arithmetic | lite | measurement | 34.364029517678446 | 34.36369705907616 | 1024.0 | +| arithmetic | lite | measurement | 33.30465561478728 | 33.304181543612465 | 1024.0 | +| arithmetic | lite | measurement | 33.1803566363734 | 33.179483183321075 | 1024.0 | +| arithmetic | lite | measurement | 33.13907351751217 | 33.13833769812138 | 1024.0 | +| arithmetic | lite | measurement | 33.636020434974974 | 33.636067415423334 | 1024.0 | +| arithmetic | lite | measurement | 33.21797844327787 | 33.21803232706158 | 1024.0 | +| arithmetic | lite | measurement | 33.10575329886226 | 33.10536839462608 | 1024.0 | +| arithmetic | lite | measurement | 32.796806450629894 | 32.79418853650992 | 1024.0 | +| arithmetic | lite | measurement | 32.72269722288073 | 32.722398417583115 | 1024.0 | +| arithmetic | lite | measurement | 33.31511221837056 | 33.31351133525802 | 1024.0 | +| arithmetic | lite | measurement | 33.224653695350284 | 33.22482014035019 | 1024.0 | +| arithmetic | lite | measurement | 32.57245031426095 | 32.571229733146026 | 1024.0 | +| arithmetic | lite | measurement | 32.886534187885246 | 32.886585818411426 | 1024.0 | +| arithmetic | lite | measurement | 33.37990531705548 | 33.37915981395145 | 1024.0 | +| arithmetic | lite | measurement | 33.71926482098905 | 33.71929626917182 | 1024.0 | +| arithmetic | lite | measurement | 33.44526342774309 | 33.444371949655874 | 1024.0 | +| arithmetic | lite | measurement | 33.23388056139813 | 33.233973755277795 | 1024.0 | +| arithmetic | lite | measurement | 33.09498988405509 | 33.094931733798816 | 1024.0 | +| arithmetic | lite | measurement | 74.95881326439178 | 74.9573986066382 | 32768.0 | +| arithmetic | lite | measurement | 77.05715202958437 | 77.05050884664948 | 32768.0 | +| arithmetic | lite | measurement | 71.69948144859538 | 71.69750615694373 | 32768.0 | +| arithmetic | lite | measurement | 74.27900210342798 | 74.27669989189465 | 32768.0 | +| arithmetic | lite | measurement | 79.41953061827385 | 79.41637917295803 | 32768.0 | +| arithmetic | lite | measurement | 89.80389308266373 | 89.80092465680538 | 32768.0 | +| arithmetic | lite | measurement | 83.1189896135092 | 83.11556663295939 | 32768.0 | +| arithmetic | lite | measurement | 82.23206862059013 | 82.23115162772883 | 32768.0 | +| arithmetic | lite | measurement | 84.07943198937822 | 84.07534019991743 | 32768.0 | +| arithmetic | lite | measurement | 76.35435357997045 | 76.3504427141991 | 32768.0 | +| arithmetic | lite | measurement | 82.43267737418405 | 82.43088430373678 | 32768.0 | +| arithmetic | lite | measurement | 92.88165813917071 | 92.65235456773455 | 32768.0 | +| arithmetic | lite | measurement | 80.74974280999989 | 80.23062951367402 | 32768.0 | +| arithmetic | lite | measurement | 83.52577094910016 | 83.24374912619243 | 32768.0 | +| arithmetic | lite | measurement | 87.40113328278292 | 87.34686964582342 | 32768.0 | +| arithmetic | lite | measurement | 84.97534787399657 | 84.97536783244543 | 32768.0 | +| arithmetic | lite | measurement | 85.25275449602613 | 85.2341828231458 | 32768.0 | +| arithmetic | lite | measurement | 80.17747187638598 | 80.17548161578672 | 32768.0 | +| arithmetic | lite | measurement | 74.47007578942032 | 74.46848719348638 | 32768.0 | +| arithmetic | lite | measurement | 74.63523347793408 | 74.51141021788662 | 32768.0 | +| arithmetic | lite | measurement | 73.88286462983048 | 73.83420004732149 | 32768.0 | +| arithmetic | lite | measurement | 80.98822578916577 | 80.98848365284937 | 32768.0 | +| arithmetic | lite | measurement | 80.535628446285 | 80.53350636012135 | 32768.0 | +| arithmetic | lite | measurement | 71.48732940357675 | 71.48650102651455 | 32768.0 | +| arithmetic | lite | measurement | 74.66568983173994 | 74.66162338951253 | 32768.0 | +| arithmetic | lite | measurement | 72.99665815068008 | 72.79788537820255 | 32768.0 | +| arithmetic | lite | measurement | 78.21235761742764 | 76.66590318795954 | 32768.0 | +| arithmetic | lite | measurement | 80.95975400767146 | 80.55143018036692 | 32768.0 | +| arithmetic | lite | measurement | 78.40822533208343 | 77.9470821638076 | 32768.0 | +| arithmetic | lite | measurement | 81.09219664027053 | 80.90049466115508 | 32768.0 | +| arithmetic | lite | measurement | 84.23715833433411 | 84.23594356098508 | 32768.0 | +| arithmetic | lite | measurement | 80.57434901136305 | 80.57309541883856 | 32768.0 | +| arithmetic | lite | measurement | 82.49305609268713 | 82.49114556633869 | 32768.0 | +| arithmetic | lite | measurement | 87.69112961951502 | 87.55319011986896 | 32768.0 | +| arithmetic | lite | measurement | 78.8061530822119 | 78.47188313027954 | 32768.0 | +| arithmetic | lite | measurement | 74.2127686624977 | 74.1765582764428 | 32768.0 | +| arithmetic | lite | measurement | 73.3364341506627 | 73.31743806057743 | 32768.0 | +| arithmetic | lite | measurement | 76.16675057540763 | 76.16272624568272 | 32768.0 | +| arithmetic | lite | measurement | 79.13710503516391 | 78.89499060903125 | 32768.0 | +| arithmetic | lite | measurement | 70.45980714707156 | 70.45688173168787 | 32768.0 | +| arithmetic | lite | measurement | 70.56714309895766 | 70.54434671326891 | 32768.0 | +| arithmetic | lite | measurement | 72.08031126061118 | 72.0795632164687 | 32768.0 | +| arithmetic | lite | measurement | 78.3183929745159 | 78.31737718718799 | 32768.0 | +| arithmetic | lite | measurement | 70.07208018046157 | 70.06832588898499 | 32768.0 | +| arithmetic | lite | measurement | 68.56598091642026 | 68.56537337990834 | 32768.0 | +| arithmetic | lite | measurement | 76.20033931430359 | 76.19502556632536 | 32768.0 | +| arithmetic | lite | measurement | 79.13725951404521 | 79.13033351931209 | 32768.0 | +| arithmetic | lite | measurement | 86.78484719638791 | 86.59293864690743 | 32768.0 | +| arithmetic | lite | measurement | 73.83544994764978 | 73.82324293701517 | 32768.0 | +| arithmetic | lite | measurement | 80.12618215372174 | 80.10648711744321 | 32768.0 | +| complete | lite | measurement | 723.5279090006092 | 722.5609159376743 | 1.0 | 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measurement | 601.5960902772182 | 601.5977776743886 | 1.0 | +| complete | lite | measurement | 545.330795618239 | 544.7967815768486 | 1.0 | +| complete | lite | measurement | 674.8158376997293 | 674.7654438807175 | 1.0 | +| complete | lite | measurement | 170.12929296211638 | 170.1298524009958 | 1.0 | +| complete | lite | measurement | 587.2471940088383 | 587.2103107102614 | 1.0 | +| complete | lite | measurement | 322.8478778162661 | 322.84949258941117 | 1.0 | +| complete | lite | measurement | 563.8738514749195 | 563.6268605906007 | 1.0 | +| complete | lite | measurement | 609.04551319188 | 609.0485407477425 | 1.0 | +| complete | lite | measurement | 306.3110416138241 | 306.3045620994985 | 1.0 | +| complete | lite | measurement | 218.02243318822838 | 218.02484456543633 | 1.0 | +| complete | lite | measurement | 674.5809559039634 | 674.364465258575 | 1.0 | +| complete | lite | measurement | 295.649423141528 | 294.62779790726546 | 1.0 | +| complete | lite | measurement | 314.86336169814024 | 314.86643124055956 | 1.0 | +| complete | lite | measurement | 662.8352875888231 | 662.8362112888786 | 1.0 | +| complete | lite | measurement | 517.3836462062272 | 517.0420442127489 | 1.0 | +| complete | lite | measurement | 632.4348264209331 | 632.4040379511121 | 1.0 | +| complete | lite | measurement | 754.6268560515682 | 752.629038206435 | 1.0 | +| complete | lite | measurement | 800.1500194617701 | 794.33572050261 | 1.0 | +| complete | lite | measurement | 681.6686721753787 | 679.886796884842 | 1.0 | +| complete | lite | measurement | 201.37702711633662 | 201.37070647122894 | 1.0 | +| complete | lite | measurement | 627.2485228175797 | 627.251651368133 | 1.0 | +| complete | lite | measurement | 554.924210116796 | 554.9053888899592 | 1.0 | +| complete | lite | measurement | 669.5785626833974 | 669.5726630346591 | 1.0 | +| complete | lite | measurement | 470.0901946019736 | 470.0582032796977 | 1.0 | +| complete | lite | measurement | 640.2799887428051 | 640.2810849270171 | 1.0 | +| complete | lite | measurement | 165.8120282920551 | 165.7990726692745 | 1.0 | +| complete | lite | measurement | 581.4286544275313 | 581.4181408221815 | 1.0 | +| complete | lite | measurement | 649.630423756873 | 649.618785596189 | 1.0 | +| complete | lite | measurement | 588.2940246635737 | 588.2872728056749 | 1.0 | +| complete | lite | measurement | 639.9167853451828 | 639.9053332864304 | 1.0 | +| complete | lite | measurement | 574.9246424637286 | 574.9282453454751 | 1.0 | +| complete | lite | measurement | 260.29852854859837 | 260.29892344755626 | 1.0 | +| complete | lite | measurement | 600.6002196910182 | 600.5923421455467 | 1.0 | +| complete | lite | measurement | 683.8223592021812 | 683.7702541535267 | 1.0 | +| complete | lite | measurement | 287.4957349836851 | 287.4844406568465 | 1.0 | +| complete | lite | measurement | 755.5579008648407 | 755.5587008745622 | 1.0 | +| complete | lite | measurement | 747.1659243417268 | 747.1626562147138 | 1.0 | +| complete | lite | measurement | 776.3168000001315 | 776.2680910000022 | 32.0 | +| complete | lite | measurement | 543.0815469990193 | 543.075224999999 | 32.0 | +| complete | lite | measurement | 412.9729790001875 | 412.9646849999986 | 32.0 | +| complete | lite | measurement | 674.3139720001636 | 674.2880940000014 | 32.0 | +| complete | lite | measurement | 634.8671240011754 | 634.8521429999997 | 32.0 | +| complete | lite | measurement | 512.6864540015958 | 512.6809889999997 | 32.0 | +| complete | lite | measurement | 691.3717829993402 | 691.374003 | 32.0 | +| complete | lite | measurement | 633.4266260000732 | 633.4187910000005 | 32.0 | +| complete | lite | measurement | 489.41193700011354 | 489.3602950000009 | 32.0 | +| complete | lite | measurement | 513.7209170006827 | 513.7216349999995 | 32.0 | +| complete | lite | measurement | 309.69600600110425 | 309.6972660000006 | 32.0 | +| complete | lite | measurement | 495.7537029986269 | 495.7237939999999 | 32.0 | +| complete | lite | measurement | 569.4776290001755 | 569.4802719999999 | 32.0 | +| complete | lite | measurement | 599.9039649996121 | 599.9052739999975 | 32.0 | +| complete | lite | measurement | 607.7107620003517 | 607.7117190000009 | 32.0 | +| complete | lite | measurement | 734.9686329998804 | 734.9156200000024 | 32.0 | +| complete | lite | measurement | 593.8594770013879 | 593.8521859999994 | 32.0 | +| complete | lite | measurement | 310.89634899944934 | 310.89884400000045 | 32.0 | +| complete | lite | measurement | 370.71848300001875 | 370.710944999999 | 32.0 | +| complete | lite | measurement | 661.2054079996597 | 661.1997110000019 | 32.0 | +| complete | lite | measurement | 483.797777998916 | 483.78999399999856 | 32.0 | +| complete | lite | measurement | 620.9343409991561 | 620.9356069999999 | 32.0 | +| complete | lite | measurement | 395.30866500172124 | 395.3105550000018 | 32.0 | +| complete | lite | measurement | 634.598955999536 | 634.5919449999968 | 32.0 | +| complete | lite | measurement | 658.0641679993278 | 658.0550870000011 | 32.0 | +| complete | lite | measurement | 644.5295499997883 | 644.5327519999964 | 32.0 | +| complete | lite | measurement | 585.7931290011038 | 585.793308999996 | 32.0 | +| complete | lite | measurement | 431.48241299968504 | 431.41328699999804 | 32.0 | +| complete | lite | measurement | 778.6283740006184 | 778.4097549999984 | 32.0 | +| complete | lite | measurement | 504.6634600003017 | 504.62722900000045 | 32.0 | +| complete | lite | measurement | 342.38503400047193 | 342.3669459999985 | 32.0 | +| complete | lite | measurement | 568.0772220002837 | 567.9875729999964 | 32.0 | +| complete | lite | measurement | 817.7993829995103 | 817.7901270000021 | 32.0 | +| complete | lite | measurement | 390.50928899996507 | 390.51038299999874 | 32.0 | +| complete | lite | measurement | 587.0823339992057 | 587.0631909999986 | 32.0 | +| complete | lite | measurement | 416.9056129994715 | 416.87725900000316 | 32.0 | +| complete | lite | measurement | 841.1729830004333 | 841.1669579999952 | 32.0 | +| complete | lite | measurement | 626.7516659991088 | 626.7257439999979 | 32.0 | +| complete | lite | measurement | 765.5477930002235 | 765.549417999999 | 32.0 | +| complete | lite | measurement | 475.855500999387 | 475.85810100000003 | 32.0 | +| complete | lite | measurement | 480.6617640006152 | 480.66167500000034 | 32.0 | +| complete | lite | measurement | 395.58328200109827 | 395.5847309999996 | 32.0 | +| complete | lite | measurement | 703.3904649997567 | 703.3916070000004 | 32.0 | +| complete | lite | measurement | 623.9100550010335 | 623.9122310000056 | 32.0 | +| complete | lite | measurement | 814.8501989999204 | 814.844903000001 | 32.0 | +| complete | lite | measurement | 438.45897699975467 | 438.46025199999866 | 32.0 | +| complete | lite | measurement | 500.5332190012268 | 500.5350469999996 | 32.0 | +| complete | lite | measurement | 723.3550299988565 | 723.3563179999933 | 32.0 | +| complete | lite | measurement | 448.06437700026436 | 448.06628900000334 | 32.0 | +| complete | lite | measurement | 592.5250799991773 | 592.5249860000008 | 32.0 | +| complete | lite | measurement | 1106.8748360259938 | 1106.8841259606756 | 1024.0 | +| complete | lite | measurement | 1086.087296470808 | 1085.9942637347988 | 1024.0 | +| complete | lite | measurement | 759.2298063443624 | 759.226185976529 | 1024.0 | +| complete | lite | measurement | 1110.697751070937 | 1110.6550588819525 | 1024.0 | +| complete | lite | measurement | 1206.4877604082665 | 1206.4489238944236 | 1024.0 | +| complete | lite | measurement | 1148.6865323754814 | 1148.688340707468 | 1024.0 | +| complete | lite | measurement | 1135.7528644291735 | 1135.7561105305708 | 1024.0 | +| complete | lite | measurement | 1105.1430731269438 | 1105.119603723379 | 1024.0 | +| complete | lite | measurement | 1236.0028347806197 | 1235.9706646360069 | 1024.0 | +| complete | lite | measurement | 1275.0093807182702 | 1274.936756564472 | 1024.0 | +| complete | lite | measurement | 1039.724544393963 | 1039.7241608674087 | 1024.0 | +| complete | lite | measurement | 1237.1267536750613 | 1237.1284360154393 | 1024.0 | +| complete | lite | measurement | 1218.6751154279739 | 1218.6302461999048 | 1024.0 | +| complete | lite | measurement | 1374.4634127814647 | 1374.2130403687854 | 1024.0 | +| complete | lite | measurement | 1310.888763514825 | 1310.890290222405 | 1024.0 | +| complete | lite | measurement | 1154.8662668336758 | 1154.8706635243193 | 1024.0 | +| complete | lite | measurement | 1085.1086240372922 | 1084.8112312959868 | 1024.0 | +| complete | lite | measurement | 834.3854451523633 | 834.3825251424829 | 1024.0 | +| complete | lite | measurement | 1010.9688025720333 | 1010.96792249373 | 1024.0 | +| complete | lite | measurement | 1093.9132575165775 | 1093.9132241664195 | 1024.0 | +| complete | lite | measurement | 1382.0368188176062 | 1382.0236176267883 | 1024.0 | +| complete | lite | measurement | 1127.8321141895935 | 1127.8298130155504 | 1024.0 | +| complete | lite | measurement | 664.2453513290922 | 664.2463407223185 | 1024.0 | +| complete | lite | measurement | 1327.7695359109885 | 1327.7718741430915 | 1024.0 | +| complete | lite | measurement | 1113.7395038962864 | 1113.7402376029227 | 1024.0 | +| complete | lite | measurement | 1075.1306464054423 | 1075.1308150091695 | 1024.0 | +| complete | lite | measurement | 1121.9231218175569 | 1121.9227901668337 | 1024.0 | +| complete | lite | measurement | 1310.8038886531228 | 1310.789505376825 | 1024.0 | +| complete | lite | measurement | 962.5158896021986 | 962.5145574404677 | 1024.0 | +| complete | lite | measurement | 1052.4198998022484 | 1052.4209651599765 | 1024.0 | +| complete | lite | measurement | 728.2507300027688 | 728.2480675308225 | 1024.0 | +| complete | lite | measurement | 835.0209199512572 | 835.0186187755288 | 1024.0 | +| complete | lite | measurement | 1325.9237962374355 | 1325.9236850686534 | 1024.0 | +| complete | lite | measurement | 1077.9636517953254 | 1077.962308513233 | 1024.0 | +| complete | lite | measurement | 1241.2847937069932 | 1240.9641742816546 | 1024.0 | +| complete | lite | measurement | 1305.9336753604957 | 1305.9093147608598 | 1024.0 | +| complete | lite | measurement | 1049.9126034791852 | 1049.8834756282652 | 1024.0 | +| complete | lite | measurement | 1219.7487938279448 | 1219.711576657695 | 1024.0 | +| complete | lite | measurement | 623.4981342320508 | 623.499740608164 | 1024.0 | +| complete | lite | measurement | 989.6119109046139 | 989.6134691064394 | 1024.0 | +| complete | lite | measurement | 1023.7802487936325 | 1023.7787813771474 | 1024.0 | +| complete | lite | measurement | 1184.9257027689446 | 1184.8060879264333 | 1024.0 | +| complete | lite | measurement | 1061.8913240838033 | 1061.8955781102873 | 1024.0 | +| complete | lite | measurement | 1068.9951679024118 | 1068.969065670592 | 1024.0 | +| complete | lite | measurement | 1267.6781669887555 | 1267.5211107899431 | 1024.0 | +| complete | lite | measurement | 1270.249720227031 | 1270.2490513670098 | 1024.0 | +| complete | lite | measurement | 1210.1837494722904 | 1209.9341682044974 | 1024.0 | +| complete | lite | measurement | 1068.3397736623865 | 1062.8797366802273 | 1024.0 | +| complete | lite | measurement | 1166.1845072678482 | 1159.5487860461888 | 1024.0 | +| complete | lite | measurement | 1092.607443803562 | 1087.2167144688642 | 1024.0 | +| complete | lite | measurement | 1177.9611134609809 | 1176.7434786759688 | 32768.0 | +| complete | lite | measurement | 1176.3125202894216 | 1176.2004268899861 | 32768.0 | +| complete | lite | measurement | 1026.7121697740579 | 1026.4845228515806 | 32768.0 | +| complete | lite | measurement | 1088.5411560128925 | 1086.1502830021482 | 32768.0 | +| complete | lite | measurement | 1155.7913039929804 | 1148.5600755581706 | 32768.0 | +| complete | lite | measurement | 1105.223596241675 | 1103.7487800220897 | 32768.0 | +| complete | lite | measurement | 1090.184566494866 | 1089.993673026842 | 32768.0 | +| complete | lite | measurement | 1220.3665784702587 | 1220.177052958906 | 32768.0 | +| complete | lite | measurement | 1020.2505980555331 | 1011.7139155221487 | 32768.0 | +| complete | lite | measurement | 1200.437177616643 | 1192.9223605788357 | 32768.0 | +| complete | lite | measurement | 1144.1962602807405 | 1144.1672235785038 | 32768.0 | +| complete | lite | measurement | 1343.6937780440192 | 1343.6825275160002 | 32768.0 | +| complete | lite | measurement | 995.6473479622433 | 995.6188499884095 | 32768.0 | +| complete | lite | measurement | 1126.9057705138587 | 1126.8913612744022 | 32768.0 | +| complete | lite | measurement | 1011.3190177431773 | 1011.2922532425869 | 32768.0 | +| complete | lite | measurement | 1090.829484051278 | 1090.7728713465478 | 32768.0 | +| complete | lite | measurement | 1093.6149015970873 | 1093.5960052372561 | 32768.0 | +| complete | lite | measurement | 1089.1282238365632 | 1089.115074808051 | 32768.0 | +| complete | lite | measurement | 1246.9551997377691 | 1246.9105290435216 | 32768.0 | +| complete | lite | measurement | 1078.4352968457056 | 1078.4350049781121 | 32768.0 | +| complete | lite | measurement | 1261.8833035557673 | 1261.8526493091995 | 32768.0 | +| complete | lite | measurement | 1209.6609784365103 | 1209.6467928697164 | 32768.0 | +| complete | lite | measurement | 1236.4352013735588 | 1236.3985311165945 | 32768.0 | +| complete | lite | measurement | 953.6447661653634 | 953.6460659292751 | 32768.0 | +| complete | lite | measurement | 1086.4710342197236 | 1086.4600755581737 | 32768.0 | +| complete | lite | measurement | 1039.5666030193054 | 1039.5016639162068 | 32768.0 | +| complete | lite | measurement | 939.0857489668688 | 939.0763164713984 | 32768.0 | +| complete | lite | measurement | 970.1934861770001 | 970.1444524760846 | 32768.0 | +| complete | lite | measurement | 1154.792115492083 | 1154.7634743115932 | 32768.0 | +| complete | lite | measurement | 1159.8957024606573 | 1159.8476971126865 | 32768.0 | +| complete | lite | measurement | 903.6874156109146 | 903.6914608366012 | 32768.0 | +| complete | lite | measurement | 958.4452216968043 | 958.0679532466879 | 32768.0 | +| complete | lite | measurement | 1047.5559675953236 | 1047.5075339943564 | 32768.0 | +| complete | lite | measurement | 959.0539340703652 | 959.0388210744615 | 32768.0 | +| complete | lite | measurement | 969.6542177534521 | 969.354573723754 | 32768.0 | +| complete | lite | measurement | 1064.8366320706366 | 1064.819507371692 | 32768.0 | +| complete | lite | measurement | 1057.6566631680837 | 1057.5784400103664 | 32768.0 | +| complete | lite | measurement | 1295.7334338052856 | 1295.7341061905943 | 32768.0 | +| complete | lite | measurement | 1119.6907789042928 | 1119.6672249423816 | 32768.0 | +| complete | lite | measurement | 1024.9618035760132 | 1024.9259216322814 | 32768.0 | +| complete | lite | measurement | 1029.080908608122 | 1029.041412419364 | 32768.0 | +| complete | lite | measurement | 898.9702159000116 | 898.9226292603647 | 32768.0 | +| complete | lite | measurement | 1310.3794329058114 | 1310.3604942649422 | 32768.0 | +| complete | lite | measurement | 1023.9365052325968 | 1023.9197501398014 | 32768.0 | +| complete | lite | measurement | 1211.947838955993 | 1211.9327791492244 | 32768.0 | +| complete | lite | measurement | 1064.8364711349407 | 1064.8139973540879 | 32768.0 | +| complete | lite | measurement | 901.597452297904 | 901.5964512213461 | 32768.0 | +| complete | lite | measurement | 958.2952701133279 | 958.2867895964466 | 32768.0 | +| complete | lite | measurement | 1207.8354646014614 | 1207.819079117858 | 32768.0 | +| complete | lite | measurement | 1329.5655937588165 | 1329.535876488357 | 32768.0 | +| nop | lite | measurement | 38.110806619504665 | 38.1103878926192 | 1.0 | +| nop | lite | measurement | 38.11982587211032 | 38.119949977731416 | 1.0 | +| nop | lite | measurement | 35.60234371277777 | 35.60241076147078 | 1.0 | +| nop | lite | measurement | 35.52463371233377 | 35.52400712354319 | 1.0 | +| nop | lite | measurement | 35.60890345713732 | 35.598908831390666 | 1.0 | +| nop | lite | measurement | 35.56469826472105 | 35.56482291633165 | 1.0 | +| nop | lite | measurement | 35.587525729533816 | 35.58658169680518 | 1.0 | +| nop | lite | measurement | 35.59162558347666 | 35.591650153373905 | 1.0 | +| nop | lite | measurement | 35.60446562969847 | 35.604553426382886 | 1.0 | +| nop | lite | measurement | 35.54877823649765 | 35.548899666793055 | 1.0 | +| nop | lite | measurement | 35.55656571477951 | 35.55600808581229 | 1.0 | +| nop | lite | measurement | 35.55250075029243 | 35.551779212311956 | 1.0 | +| nop | lite | measurement | 35.589655344987555 | 35.5886050608781 | 1.0 | +| nop | lite | measurement | 35.64580476279574 | 35.64589305086534 | 1.0 | +| nop | lite | measurement | 35.553413005948464 | 35.55355906071306 | 1.0 | +| nop | lite | measurement | 35.58651017091633 | 35.58656859282283 | 1.0 | +| nop | lite | measurement | 35.599918493220784 | 35.599511614578184 | 1.0 | +| nop | lite | measurement | 35.703484015827875 | 35.702958709952256 | 1.0 | +| nop | lite | measurement | 35.893405000517724 | 35.793778425745394 | 1.0 | +| nop | lite | measurement | 35.81522980865446 | 35.81531323732079 | 1.0 | +| nop | lite | measurement | 35.84754892460622 | 35.84723661309451 | 1.0 | +| nop | lite | measurement | 35.844696788295025 | 35.84365141812669 | 1.0 | +| nop | lite | measurement | 36.199621371308744 | 35.909685824925454 | 1.0 | +| nop | lite | measurement | 35.80679553968121 | 35.79414806724712 | 1.0 | +| nop | lite | measurement | 35.74602500250775 | 35.74449800636577 | 1.0 | +| nop | lite | measurement | 35.66693449752423 | 35.667047901552834 | 1.0 | +| nop | lite | measurement | 35.598739025619565 | 35.59882054330985 | 1.0 | +| nop | lite | measurement | 35.5881571230598 | 35.58826626833473 | 1.0 | +| nop | lite | measurement | 35.61087014644528 | 35.610933809983365 | 1.0 | +| nop | lite | measurement | 35.57156906487411 | 35.571136633422135 | 1.0 | +| nop | lite | measurement | 35.57613039728185 | 35.5761895290117 | 1.0 | +| nop | lite | measurement | 35.60136408087952 | 35.60148944231259 | 1.0 | +| nop | lite | measurement | 35.56451884932358 | 35.5646368943824 | 1.0 | +| nop | lite | measurement | 35.56032060641034 | 35.56036854513535 | 1.0 | +| nop | lite | measurement | 35.592723587856014 | 35.59245162569375 | 1.0 | +| nop | lite | measurement | 35.618258663137574 | 35.575754312998356 | 1.0 | +| nop | lite | measurement | 35.58992517790013 | 35.589981361223046 | 1.0 | +| nop | lite | measurement | 35.594627323593166 | 35.594772613965524 | 1.0 | +| nop | lite | measurement | 35.560348670724025 | 35.56044809722792 | 1.0 | +| nop | lite | measurement | 35.522294651493006 | 35.522377861739045 | 1.0 | +| nop | lite | measurement | 35.56033387412664 | 35.560444056833425 | 1.0 | +| nop | lite | measurement | 35.52965892577639 | 35.52885406821049 | 1.0 | +| nop | lite | measurement | 35.56817109298652 | 35.56825353890782 | 1.0 | +| nop | lite | measurement | 35.65557148848413 | 35.65312721351518 | 1.0 | +| nop | lite | measurement | 35.796585189589536 | 35.796566843987144 | 1.0 | +| nop | lite | measurement | 35.72708166725118 | 35.72728647156318 | 1.0 | +| nop | lite | measurement | 35.83322894731306 | 35.83339340235235 | 1.0 | +| nop | lite | measurement | 35.55572160000488 | 35.55532662413073 | 1.0 | +| nop | lite | measurement | 35.591038088224536 | 35.591208549168925 | 1.0 | +| nop | lite | measurement | 35.59113746013219 | 35.590686901472296 | 1.0 | +| nop | lite | measurement | 32.15815746538057 | 32.157640515155286 | 32.0 | +| nop | lite | measurement | 32.01621556424229 | 32.01571680258658 | 32.0 | +| nop | lite | measurement | 32.0283739304795 | 32.02792233960789 | 32.0 | +| nop | lite | measurement | 32.05420458513627 | 32.05432570445549 | 32.0 | +| nop | lite | measurement | 32.03679845075908 | 32.03689164420103 | 32.0 | +| nop | lite | measurement | 32.03965387165498 | 32.03981214887703 | 32.0 | +| nop | lite | measurement | 32.04951533454708 | 32.04947606371195 | 32.0 | +| nop | lite | measurement | 32.03388639736518 | 32.033269961191245 | 32.0 | +| nop | lite | measurement | 32.0627294182118 | 32.061882455761264 | 32.0 | +| nop | lite | measurement | 32.02344184410205 | 32.02230037384856 | 32.0 | +| nop | lite | measurement | 32.05258952328848 | 32.05262236379488 | 32.0 | +| nop | lite | measurement | 32.04441596838553 | 32.04444476698029 | 32.0 | +| nop | lite | measurement | 32.038449430424116 | 32.038554887345335 | 32.0 | +| nop | lite | measurement | 32.071815570519604 | 32.07191202503869 | 32.0 | +| nop | lite | measurement | 32.176388653263515 | 32.17604669904645 | 32.0 | +| nop | lite | measurement | 32.02859536240377 | 32.02869875245737 | 32.0 | +| nop | lite | measurement | 32.0670492940753 | 32.06713086699465 | 32.0 | +| nop | lite | measurement | 32.077792948182825 | 32.07795834469721 | 32.0 | +| nop | lite | measurement | 32.07947745720951 | 32.078769848615195 | 32.0 | +| nop | lite | measurement | 32.095405127710414 | 32.094862318540414 | 32.0 | +| nop | lite | measurement | 32.064849626054404 | 32.0645349087693 | 32.0 | +| nop | lite | measurement | 32.01090179358638 | 32.010945749300255 | 32.0 | +| nop | lite | measurement | 32.092971121265684 | 32.09303243875307 | 32.0 | +| nop | lite | measurement | 32.00502523393077 | 32.0050775948947 | 32.0 | +| nop | lite | measurement | 32.04682517233799 | 32.046436047308774 | 32.0 | +| nop | lite | measurement | 32.048628320469334 | 32.04833771680788 | 32.0 | +| nop | lite | measurement | 32.028052691054484 | 32.02821946541969 | 32.0 | +| nop | lite | measurement | 32.045869905309154 | 32.045007601993106 | 32.0 | +| nop | lite | measurement | 32.01345062694742 | 32.013556818731786 | 32.0 | +| nop | lite | measurement | 32.04937671556183 | 32.049362477047346 | 32.0 | From cd40d65c22ada06cbd7a3223ca2ee8b3b9bed560 Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Wed, 30 Jun 2021 16:09:23 -0400 Subject: [PATCH 054/113] Minify data --- joss/paper.md | 2005 +++++++++++++++++++++++++------------------------ 1 file changed, 1003 insertions(+), 1002 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index 81a289fd..a726fb69 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -167,1005 +167,1006 @@ Any opinions, findings, and conclusions or recommendations expressed in this mat ![Wall time benchmarking results of 20 replicates. The x-axis represents different agent counts.\label{fig:raw-timings}](figures/raw-timings.png) -| Library | Implementation | Statistic | Wall Nanoseconds | CPU Nanoseconds | num agents | -|-----------------|----------------|-------------|------------------------|------------------------|------------| -| arithmetic | vanilla | measurement | 760.1855802385494 | 749.882302240635 | 1.0 | -| arithmetic | vanilla | measurement | 768.353517653824 | 754.1809000481397 | 1.0 | -| arithmetic | vanilla | measurement | 768.8718047121922 | 755.1567654938049 | 1.0 | -| arithmetic | vanilla | measurement | 757.2702403360975 | 748.9733345468779 | 1.0 | -| arithmetic | vanilla | measurement | 759.5047761394123 | 746.0654853612508 | 1.0 | -| arithmetic | vanilla | measurement | 759.3515514911907 | 747.1096697220942 | 1.0 | -| arithmetic | vanilla | measurement | 759.5045195402103 | 746.0732469304083 | 1.0 | -| arithmetic | vanilla | measurement | 752.6935430278776 | 751.918087921319 | 1.0 | -| arithmetic | vanilla | measurement | 741.5234705767284 | 741.5241296197398 | 1.0 | -| arithmetic | vanilla | measurement | 745.2377202598302 | 745.2209766330488 | 1.0 | -| arithmetic | vanilla | measurement | 740.7536749030298 | 740.7368281960548 | 1.0 | -| arithmetic | vanilla | measurement | 747.5374552170535 | 747.5379925410867 | 1.0 | -| arithmetic | vanilla | measurement | 745.7338597629983 | 745.7354300608938 | 1.0 | -| arithmetic | vanilla | measurement | 742.2138850732599 | 742.2163315345814 | 1.0 | -| arithmetic | vanilla | measurement | 741.1792310592932 | 741.1651477253165 | 1.0 | -| arithmetic | vanilla | measurement | 742.0711053168758 | 742.0724475260682 | 1.0 | -| arithmetic | vanilla | measurement | 746.5643268273411 | 746.5564139313476 | 1.0 | -| arithmetic | vanilla | measurement | 738.833539672256 | 738.8223315630922 | 1.0 | -| arithmetic | vanilla | measurement | 776.1955053522951 | 776.1755684927219 | 1.0 | -| arithmetic | vanilla | measurement | 764.0617339731097 | 764.0406687362724 | 1.0 | -| arithmetic | vanilla | measurement | 769.9571962292429 | 769.9325901468208 | 1.0 | -| arithmetic | vanilla | measurement | 760.7416849325331 | 760.7424887244514 | 1.0 | -| arithmetic | vanilla | measurement | 761.0623009033172 | 761.0563015322481 | 1.0 | -| arithmetic | vanilla | measurement | 773.9119953423642 | 773.9125589821139 | 1.0 | -| arithmetic | vanilla | measurement | 779.1800480068675 | 779.1820470672611 | 1.0 | -| arithmetic | vanilla | measurement | 743.9381925566772 | 743.9399547552478 | 1.0 | -| arithmetic | vanilla | measurement | 748.3893746113941 | 748.3899437346524 | 1.0 | -| arithmetic | vanilla | measurement | 763.5274785646566 | 763.523753493966 | 1.0 | -| arithmetic | vanilla | measurement | 750.3185730481417 | 750.3022735322593 | 1.0 | -| arithmetic | vanilla | measurement | 741.5182399068215 | 741.5068662143655 | 1.0 | -| arithmetic | vanilla | measurement | 773.1227453010433 | 773.1236203695677 | 1.0 | -| arithmetic | vanilla | measurement | 747.6102426825483 | 747.6112778516745 | 1.0 | -| arithmetic | vanilla | measurement | 750.5999107387825 | 750.6012375963494 | 1.0 | -| arithmetic | vanilla | measurement | 758.2117610031188 | 758.2017689973678 | 1.0 | -| arithmetic | vanilla | measurement | 756.0790708479337 | 756.0722051826405 | 1.0 | -| arithmetic | vanilla | measurement | 760.3699476599177 | 760.3515153043711 | 1.0 | -| arithmetic | vanilla | measurement | 765.7969293666088 | 765.7808447806805 | 1.0 | -| arithmetic | vanilla | measurement | 760.4979126665603 | 760.4993886585489 | 1.0 | -| arithmetic | vanilla | measurement | 760.7685170652778 | 760.4262535789492 | 1.0 | -| arithmetic | vanilla | measurement | 758.7680247035157 | 758.7600964548777 | 1.0 | -| arithmetic | vanilla | measurement | 765.4752255933706 | 765.4792708642905 | 1.0 | -| arithmetic | vanilla | measurement | 784.8301249344532 | 784.8319978858005 | 1.0 | -| arithmetic | vanilla | measurement | 765.4722911535139 | 765.4755096065613 | 1.0 | -| arithmetic | vanilla | measurement | 755.4673269524541 | 755.4674870521714 | 1.0 | -| arithmetic | vanilla | measurement | 781.8975523300039 | 781.886854130093 | 1.0 | -| arithmetic | vanilla | measurement | 753.2713204639175 | 753.2394451761055 | 1.0 | -| arithmetic | vanilla | measurement | 751.0369381816973 | 751.026774014209 | 1.0 | -| arithmetic | vanilla | measurement | 751.3253104141113 | 751.2594818236958 | 1.0 | -| arithmetic | vanilla | measurement | 749.1344885407492 | 749.1345642040146 | 1.0 | -| arithmetic | vanilla | measurement | 751.6837944615038 | 751.6842572173961 | 1.0 | -| arithmetic | vanilla | measurement | 799.4231393315299 | 799.3434118691126 | 32.0 | -| arithmetic | vanilla | measurement | 792.839467521623 | 792.8303008788721 | 32.0 | -| arithmetic | vanilla | measurement | 807.1811857668533 | 807.1731012567636 | 32.0 | -| arithmetic | vanilla | measurement | 780.1608004359199 | 780.1511997938858 | 32.0 | -| arithmetic | vanilla | measurement | 777.250704531349 | 777.2520672182343 | 32.0 | -| arithmetic | vanilla | measurement | 757.2426704074553 | 757.2318158655639 | 32.0 | -| arithmetic | vanilla | measurement | 801.7762365789041 | 801.7179330680436 | 32.0 | -| arithmetic | vanilla | measurement | 801.9850001436089 | 801.985477655951 | 32.0 | -| arithmetic | vanilla | measurement | 771.2267922474311 | 771.2272044888467 | 32.0 | -| arithmetic | vanilla | measurement | 789.3089645295903 | 789.2082516962048 | 32.0 | -| arithmetic | vanilla | measurement | 813.7674455355893 | 810.3502859923835 | 32.0 | -| arithmetic | vanilla | measurement | 790.6304726446336 | 787.3940282270731 | 32.0 | -| arithmetic | vanilla | measurement | 773.4529077311772 | 772.5679997709748 | 32.0 | -| arithmetic | vanilla | measurement | 775.7915421823997 | 775.7950462340046 | 32.0 | -| arithmetic | vanilla | measurement | 769.401249320479 | 769.4007683719343 | 32.0 | -| arithmetic | vanilla | measurement | 797.9947817136758 | 791.5737848902127 | 32.0 | -| arithmetic | vanilla | measurement | 779.2191967005299 | 779.2209441470369 | 32.0 | -| arithmetic | vanilla | measurement | 789.4209143743326 | 789.3751853654388 | 32.0 | -| arithmetic | vanilla | measurement | 851.1407254302585 | 839.1518662506093 | 32.0 | -| arithmetic | vanilla | measurement | 822.5891683596035 | 807.2459981105593 | 32.0 | -| arithmetic | vanilla | measurement | 827.1450803025248 | 812.934027654514 | 32.0 | -| arithmetic | vanilla | measurement | 815.0800263374626 | 798.6080249634934 | 32.0 | -| arithmetic | vanilla | measurement | 827.4736846922378 | 811.9264412699342 | 32.0 | -| arithmetic | vanilla | measurement | 810.1951034897797 | 799.0324330823588 | 32.0 | -| arithmetic | vanilla | measurement | 799.6908774436223 | 797.1943122155124 | 32.0 | -| arithmetic | vanilla | measurement | 790.4080593180399 | 790.3724222037764 | 32.0 | -| arithmetic | vanilla | measurement | 797.1540728853762 | 797.1136892731413 | 32.0 | -| arithmetic | vanilla | measurement | 813.771385873542 | 813.634881337497 | 32.0 | -| arithmetic | vanilla | measurement | 841.2466909050114 | 841.2061435401266 | 32.0 | -| arithmetic | vanilla | measurement | 835.9091477497803 | 832.5378237095982 | 32.0 | -| arithmetic | vanilla | measurement | 834.3413918865364 | 828.5528510492131 | 32.0 | -| arithmetic | vanilla | measurement | 822.8340133400028 | 818.0432234977552 | 32.0 | -| arithmetic | vanilla | measurement | 817.6634496585389 | 813.088863187426 | 32.0 | -| arithmetic | vanilla | measurement | 815.647351634595 | 810.5290131974489 | 32.0 | -| arithmetic | vanilla | measurement | 823.8009481559195 | 815.0996146689167 | 32.0 | -| arithmetic | vanilla | measurement | 787.8816375144411 | 787.6498147777023 | 32.0 | -| arithmetic | vanilla | measurement | 782.1995465344049 | 782.1950531046899 | 32.0 | -| arithmetic | vanilla | measurement | 781.2749614952904 | 781.2768303226345 | 32.0 | -| arithmetic | vanilla | measurement | 788.7166803128617 | 788.7152374681474 | 32.0 | -| arithmetic | vanilla | measurement | 810.9040829061861 | 810.9080541639254 | 32.0 | -| arithmetic | vanilla | measurement | 818.318222780378 | 818.2977927915075 | 32.0 | -| arithmetic | vanilla | measurement | 787.8710097047249 | 787.8635080587464 | 32.0 | -| arithmetic | vanilla | measurement | 802.6580573130539 | 802.6032372391292 | 32.0 | -| arithmetic | vanilla | measurement | 809.534759383135 | 809.5353433912584 | 32.0 | -| arithmetic | vanilla | measurement | 815.6922916614728 | 815.6963236094028 | 32.0 | -| arithmetic | vanilla | measurement | 802.4399679374104 | 802.4393381237206 | 32.0 | -| arithmetic | vanilla | measurement | 831.9165635119534 | 831.8968091380185 | 32.0 | -| arithmetic | vanilla | measurement | 815.7628410282749 | 815.2359978242791 | 32.0 | -| arithmetic | vanilla | measurement | 798.047159256046 | 798.0437296384335 | 32.0 | -| arithmetic | vanilla | measurement | 801.6123981563063 | 801.6133566173393 | 32.0 | -| arithmetic | vanilla | measurement | 1609.1744390348708 | 1609.1209913676068 | 1024.0 | -| arithmetic | vanilla | measurement | 1690.6170982860706 | 1690.0549676457904 | 1024.0 | -| arithmetic | vanilla | measurement | 1611.3780428336256 | 1609.005088830346 | 1024.0 | -| arithmetic | vanilla | measurement | 1671.336695223913 | 1670.9555043529904 | 1024.0 | -| arithmetic | vanilla | measurement | 1737.047181480137 | 1730.468951926303 | 1024.0 | -| arithmetic | vanilla | measurement | 1766.9685127158384 | 1766.9127586381348 | 1024.0 | -| arithmetic | vanilla | measurement | 1588.2588864818172 | 1588.2404972474692 | 1024.0 | -| arithmetic | vanilla | measurement | 1682.208947878868 | 1680.912917304788 | 1024.0 | -| arithmetic | vanilla | measurement | 1643.2361833526359 | 1643.233623992224 | 1024.0 | -| arithmetic | vanilla | measurement | 1703.6614306680335 | 1703.643846263515 | 1024.0 | -| arithmetic | vanilla | measurement | 1665.9098911407928 | 1665.9103119524304 | 1024.0 | -| arithmetic | vanilla | measurement | 1658.9306304812817 | 1653.5303421220683 | 1024.0 | -| arithmetic | vanilla | measurement | 1623.6143043754373 | 1616.7326489971922 | 1024.0 | -| arithmetic | vanilla | measurement | 1653.1023284924381 | 1653.0537397039213 | 1024.0 | -| arithmetic | vanilla | measurement | 1644.4696624769304 | 1644.47319913354 | 1024.0 | -| arithmetic | vanilla | measurement | 1682.1525798285445 | 1682.1547206776938 | 1024.0 | -| arithmetic | vanilla | measurement | 1719.6431058163246 | 1719.63633144312 | 1024.0 | -| arithmetic | vanilla | measurement | 1681.7786761234966 | 1681.5671205912386 | 1024.0 | -| arithmetic | vanilla | measurement | 1613.6447706685321 | 1613.568440513792 | 1024.0 | -| arithmetic | vanilla | measurement | 1609.356724018054 | 1608.9844805621817 | 1024.0 | -| arithmetic | vanilla | measurement | 1656.7508243764112 | 1656.651666919625 | 1024.0 | -| arithmetic | vanilla | measurement | 1681.5887590426819 | 1681.5870045116744 | 1024.0 | -| arithmetic | vanilla | measurement | 1710.993740713979 | 1710.9646472311397 | 1024.0 | -| arithmetic | vanilla | measurement | 1669.0191296773257 | 1669.0168508579507 | 1024.0 | -| arithmetic | vanilla | measurement | 1638.7054135233195 | 1638.7027138895298 | 1024.0 | -| arithmetic | vanilla | measurement | 1691.1376421663008 | 1691.1413236937697 | 1024.0 | -| arithmetic | vanilla | measurement | 1728.321746068102 | 1728.3204583396564 | 1024.0 | -| arithmetic | vanilla | measurement | 1661.1459756154145 | 1661.1429379544231 | 1024.0 | -| arithmetic | vanilla | measurement | 1622.395788205591 | 1622.3089147661033 | 1024.0 | -| arithmetic | vanilla | measurement | 1648.8280306477307 | 1648.8306658940362 | 1024.0 | -| arithmetic | vanilla | measurement | 1627.8422669541694 | 1625.5742605904209 | 1024.0 | -| arithmetic | vanilla | measurement | 1707.3267544145972 | 1707.3028624383253 | 1024.0 | -| arithmetic | vanilla | measurement | 1660.418263681009 | 1658.6419123700236 | 1024.0 | -| arithmetic | vanilla | measurement | 1616.956143619305 | 1616.9054898660293 | 1024.0 | -| arithmetic | vanilla | measurement | 1633.5055142408967 | 1633.4924161941335 | 1024.0 | -| arithmetic | vanilla | measurement | 1594.5922865009634 | 1594.5720001655804 | 1024.0 | -| arithmetic | vanilla | measurement | 1615.6975031844017 | 1615.6972709336512 | 1024.0 | -| arithmetic | vanilla | measurement | 1668.2646467709535 | 1668.257573458061 | 1024.0 | -| arithmetic | vanilla | measurement | 1659.4387638707308 | 1659.434063659797 | 1024.0 | -| arithmetic | vanilla | measurement | 1609.2287329201135 | 1609.2060274930234 | 1024.0 | -| arithmetic | vanilla | measurement | 1671.2142965540447 | 1670.363877812898 | 1024.0 | -| arithmetic | vanilla | measurement | 1625.1001140557923 | 1625.0922750037869 | 1024.0 | -| arithmetic | vanilla | measurement | 1631.8991248047864 | 1631.8727815413004 | 1024.0 | -| arithmetic | vanilla | measurement | 1608.6925431268037 | 1608.6919981419924 | 1024.0 | -| arithmetic | vanilla | measurement | 1622.3218564467677 | 1617.4035421754522 | 1024.0 | -| arithmetic | vanilla | measurement | 1632.6106067487883 | 1627.0667940598878 | 1024.0 | -| arithmetic | vanilla | measurement | 1580.6848351475755 | 1580.0031135455265 | 1024.0 | -| arithmetic | vanilla | measurement | 1686.2803685659949 | 1686.2807870785648 | 1024.0 | -| arithmetic | vanilla | measurement | 1673.447288178401 | 1646.5058223761448 | 1024.0 | -| arithmetic | vanilla | measurement | 1650.3715880905345 | 1613.3599341418496 | 1024.0 | -| arithmetic | vanilla | measurement | 1787.2793145253897 | 1778.4245653288035 | 32768.0 | -| arithmetic | vanilla | measurement | 1745.0570822329707 | 1712.712382660661 | 32768.0 | -| arithmetic | vanilla | measurement | 1814.9603577393025 | 1782.0925309050706 | 32768.0 | -| arithmetic | vanilla | measurement | 1940.5818000591187 | 1939.8476400885236 | 32768.0 | -| arithmetic | vanilla | measurement | 1669.2724210337183 | 1669.2291320325485 | 32768.0 | -| arithmetic | vanilla | measurement | 1701.1096896379554 | 1700.7119108033453 | 32768.0 | -| arithmetic | vanilla | measurement | 1619.6671437333748 | 1619.6632188792996 | 32768.0 | -| arithmetic | vanilla | measurement | 1601.335684283475 | 1601.337874829675 | 32768.0 | -| arithmetic | vanilla | measurement | 1615.9230591361013 | 1615.9217841830962 | 32768.0 | -| arithmetic | vanilla | measurement | 1704.9848536940378 | 1704.8378810794857 | 32768.0 | -| arithmetic | vanilla | measurement | 1679.2324944110528 | 1679.1568972413347 | 32768.0 | -| arithmetic | vanilla | measurement | 1642.933718111633 | 1642.894922690394 | 32768.0 | -| arithmetic | vanilla | measurement | 1614.5185243070478 | 1614.5167931202475 | 32768.0 | -| arithmetic | vanilla | measurement | 1627.8727985228265 | 1627.8275814656797 | 32768.0 | -| arithmetic | vanilla | measurement | 1679.478613302856 | 1679.4693667738027 | 32768.0 | -| arithmetic | vanilla | measurement | 1626.587274849193 | 1626.5849718135971 | 32768.0 | -| arithmetic | vanilla | measurement | 1687.0231772566156 | 1686.8952820518728 | 32768.0 | -| arithmetic | vanilla | measurement | 1679.4308338468277 | 1679.4282901891843 | 32768.0 | -| arithmetic | vanilla | measurement | 1640.8502806099373 | 1640.8143350874363 | 32768.0 | -| arithmetic | vanilla | measurement | 1729.6812244540447 | 1728.7213573240754 | 32768.0 | -| arithmetic | vanilla | measurement | 1714.4259558981457 | 1714.3981350699535 | 32768.0 | -| arithmetic | vanilla | measurement | 1621.6680155762497 | 1621.660528355202 | 32768.0 | -| arithmetic | vanilla | measurement | 1595.2502093629935 | 1595.2490844093493 | 32768.0 | -| arithmetic | vanilla | measurement | 1631.1609127152346 | 1631.1593658987897 | 32768.0 | -| arithmetic | vanilla | measurement | 1652.0163993877622 | 1651.9869192405397 | 32768.0 | -| arithmetic | vanilla | measurement | 1617.5367017440776 | 1617.5013843231304 | 32768.0 | -| arithmetic | vanilla | measurement | 1704.846455760332 | 1702.741350324387 | 32768.0 | -| arithmetic | vanilla | measurement | 1703.7565966283332 | 1693.1664937564954 | 32768.0 | -| arithmetic | vanilla | measurement | 1754.7727460212732 | 1754.340221616732 | 32768.0 | -| arithmetic | vanilla | measurement | 1678.2588277887191 | 1639.8599583765981 | 32768.0 | -| arithmetic | vanilla | measurement | 1662.2700086246714 | 1634.3638488556742 | 32768.0 | -| arithmetic | vanilla | measurement | 1655.2223979110843 | 1629.148719422987 | 32768.0 | -| arithmetic | vanilla | measurement | 1665.7835237417528 | 1636.264721322923 | 32768.0 | -| arithmetic | vanilla | measurement | 1680.616017528572 | 1648.3814950689712 | 32768.0 | -| arithmetic | vanilla | measurement | 1752.3959164059343 | 1728.7069766133836 | 32768.0 | -| arithmetic | vanilla | measurement | 1669.390797846056 | 1669.337740459704 | 32768.0 | -| arithmetic | vanilla | measurement | 1676.2403847351165 | 1672.0014311963585 | 32768.0 | -| arithmetic | vanilla | measurement | 1634.836287388197 | 1634.8142569654237 | 32768.0 | -| arithmetic | vanilla | measurement | 1687.7195636385075 | 1687.6651531818138 | 32768.0 | -| arithmetic | vanilla | measurement | 1650.510874592383 | 1650.4191561566627 | 32768.0 | -| arithmetic | vanilla | measurement | 1668.145453919917 | 1668.1490850342504 | 32768.0 | -| arithmetic | vanilla | measurement | 1658.71565128626 | 1658.603914853222 | 32768.0 | -| arithmetic | vanilla | measurement | 1647.3613270727265 | 1647.3702048673756 | 32768.0 | -| arithmetic | vanilla | measurement | 1638.665290674642 | 1638.5891935302482 | 32768.0 | -| arithmetic | vanilla | measurement | 1688.7960326514014 | 1688.7964263839822 | 32768.0 | -| arithmetic | vanilla | measurement | 1634.5530136375812 | 1634.5584759321757 | 32768.0 | -| arithmetic | vanilla | measurement | 1727.8173349751407 | 1727.6869367398874 | 32768.0 | -| arithmetic | vanilla | measurement | 1636.126942113468 | 1636.127345224506 | 32768.0 | -| arithmetic | vanilla | measurement | 1661.6555785402636 | 1661.567997450135 | 32768.0 | -| arithmetic | vanilla | measurement | 1712.8865292537746 | 1691.9483925602754 | 32768.0 | -| complete | vanilla | measurement | 22792.3851184786 | 22759.790567306973 | 1.0 | -| complete | vanilla | measurement | 23203.624486148277 | 23118.266948202403 | 1.0 | -| complete | vanilla | measurement | 24437.947081252125 | 24188.152814111672 | 1.0 | -| complete | vanilla | measurement | 24410.703341079585 | 24330.484901711654 | 1.0 | -| complete | vanilla | measurement | 24124.182898589417 | 24020.804843411315 | 1.0 | -| complete | vanilla | measurement | 24155.325024276965 | 23968.298153823165 | 1.0 | -| complete | vanilla | measurement | 24780.18181475385 | 24444.825809103822 | 1.0 | -| complete | vanilla | measurement | 25548.30387179554 | 25401.63136258316 | 1.0 | -| complete | vanilla | measurement | 23486.370020200186 | 23340.78458778683 | 1.0 | -| complete | vanilla | measurement | 23760.35264220735 | 23582.124037670983 | 1.0 | -| complete | vanilla | measurement | 24136.772666182125 | 23936.80488078331 | 1.0 | -| complete | vanilla | measurement | 25552.983817874494 | 25415.716234397176 | 1.0 | -| complete | vanilla | measurement | 25193.66936991574 | 24936.470737723328 | 1.0 | -| complete | vanilla | measurement | 24901.95989979383 | 24608.717467673174 | 1.0 | -| complete | vanilla | measurement | 23956.943979406493 | 23732.903767097672 | 1.0 | -| complete | vanilla | measurement | 25029.20595705341 | 24858.605650646477 | 1.0 | -| complete | vanilla | measurement | 24957.038530553516 | 24856.431272890353 | 1.0 | -| complete | vanilla | measurement | 24399.096868255547 | 24123.833844083973 | 1.0 | -| complete | vanilla | measurement | 24196.049106820537 | 23899.28892293889 | 1.0 | -| complete | vanilla | measurement | 25542.33190076546 | 25512.69702518877 | 1.0 | -| complete | vanilla | measurement | 35583.70039496416 | 35111.39183354764 | 32.0 | -| complete | vanilla | measurement | 31072.11955721301 | 30814.789454345024 | 32.0 | -| complete | vanilla | measurement | 34756.03968404421 | 34397.73566966745 | 32.0 | -| complete | vanilla | measurement | 32993.72836674032 | 32660.276961234576 | 32.0 | -| complete | vanilla | measurement | 29274.58722211107 | 29038.10343560535 | 32.0 | -| complete | vanilla | measurement | 30905.559480022068 | 30728.66489068537 | 32.0 | -| complete | vanilla | measurement | 31758.99889765729 | 31520.895278339216 | 32.0 | -| complete | vanilla | measurement | 33703.312098068505 | 33402.315956274 | 32.0 | -| complete | vanilla | measurement | 30585.89821788914 | 30258.01566231861 | 32.0 | -| complete | vanilla | measurement | 34651.7127503011 | 34358.01405474912 | 32.0 | -| complete | vanilla | measurement | 33578.90327025094 | 33394.26988792953 | 32.0 | -| complete | vanilla | measurement | 32262.492283684605 | 32103.09815359187 | 32.0 | -| complete | vanilla | measurement | 31068.125620092163 | 30884.79579276128 | 32.0 | -| complete | vanilla | measurement | 35098.88471429041 | 34734.11937350737 | 32.0 | -| complete | vanilla | measurement | 31326.039224726457 | 31265.810169024448 | 32.0 | -| complete | vanilla | measurement | 29679.551350382844 | 29667.7608855411 | 32.0 | -| complete | vanilla | measurement | 34160.38177474951 | 33780.28026823446 | 32.0 | -| complete | vanilla | measurement | 32381.71918055487 | 32345.97354400146 | 32.0 | -| complete | vanilla | measurement | 31777.50275584732 | 31541.13577071475 | 32.0 | -| complete | vanilla | measurement | 31529.369465399574 | 31490.999035458277 | 32.0 | -| complete | vanilla | measurement | 30595.669673736404 | 30589.463958908727 | 1024.0 | -| complete | vanilla | measurement | 29877.091289660122 | 29468.467843577375 | 1024.0 | -| complete | vanilla | measurement | 34749.25064746224 | 34318.37802140886 | 1024.0 | -| complete | vanilla | measurement | 32068.20139848096 | 31617.809133287432 | 1024.0 | -| complete | vanilla | measurement | 30824.032846989292 | 30821.112353245888 | 1024.0 | -| complete | vanilla | measurement | 30170.31060083666 | 30089.372453384152 | 1024.0 | -| complete | vanilla | measurement | 29264.817506870048 | 29198.94220476518 | 1024.0 | -| complete | vanilla | measurement | 30249.132208209077 | 30214.929816988766 | 1024.0 | -| complete | vanilla | measurement | 30166.86157630598 | 30166.832743439252 | 1024.0 | -| complete | vanilla | measurement | 31835.437068406645 | 31834.656379488897 | 1024.0 | -| complete | vanilla | measurement | 33134.298687868526 | 33134.2911774864 | 1024.0 | -| complete | vanilla | measurement | 32381.86023820969 | 32343.311723066076 | 1024.0 | -| complete | vanilla | measurement | 30340.40689748664 | 30079.613777624414 | 1024.0 | -| complete | vanilla | measurement | 32355.056500382514 | 32126.012258287417 | 1024.0 | -| complete | vanilla | measurement | 34694.4364640674 | 34191.56176622917 | 1024.0 | -| complete | vanilla | measurement | 36885.53431457356 | 36467.54881733455 | 1024.0 | -| complete | vanilla | measurement | 35027.89938707058 | 34789.83023998611 | 1024.0 | -| complete | vanilla | measurement | 33587.62737391806 | 33326.76174033153 | 1024.0 | -| complete | vanilla | measurement | 37003.565737195735 | 36587.17247928177 | 1024.0 | -| complete | vanilla | measurement | 32903.22656250574 | 32439.149991367212 | 1024.0 | -| complete | vanilla | measurement | 40668.83538723743 | 40668.24981878062 | 32768.0 | -| complete | vanilla | measurement | 37402.74438224472 | 37402.16579901157 | 32768.0 | -| complete | vanilla | measurement | 46248.954991791616 | 46196.17693574949 | 32768.0 | -| complete | vanilla | measurement | 40634.61700160823 | 39885.51196046127 | 32768.0 | -| complete | vanilla | measurement | 39601.099308168006 | 38905.11861614461 | 32768.0 | -| complete | vanilla | measurement | 38538.95927514134 | 37783.218978583755 | 32768.0 | -| complete | vanilla | measurement | 38794.16915987 | 38218.78451400356 | 32768.0 | -| complete | vanilla | measurement | 38254.80863262567 | 37851.92856672192 | 32768.0 | -| complete | vanilla | measurement | 36349.79934101258 | 36349.751696870415 | 32768.0 | -| complete | vanilla | measurement | 37353.11288310155 | 37353.031301483 | 32768.0 | -| complete | vanilla | measurement | 37442.02181216463 | 36854.76112026329 | 32768.0 | -| complete | vanilla | measurement | 37787.89074140884 | 37014.779505765764 | 32768.0 | -| complete | vanilla | measurement | 38519.35018124013 | 37777.27670510716 | 32768.0 | -| complete | vanilla | measurement | 37983.14266889149 | 37299.05871499188 | 32768.0 | -| complete | vanilla | measurement | 39508.65311369559 | 38880.46464579838 | 32768.0 | -| complete | vanilla | measurement | 38163.34425035981 | 37575.58892915995 | 32768.0 | -| complete | vanilla | measurement | 37972.42741358061 | 37299.12718286648 | 32768.0 | -| complete | vanilla | measurement | 41130.08507421065 | 40588.81344316304 | 32768.0 | -| complete | vanilla | measurement | 38845.02991766798 | 38391.072751235704 | 32768.0 | -| complete | vanilla | measurement | 37005.04230637997 | 37005.051927512555 | 32768.0 | -| nop | vanilla | measurement | 303.518526960116 | 301.9014828206564 | 1.0 | -| nop | vanilla | measurement | 328.00512935117064 | 327.2114961197941 | 1.0 | -| nop | vanilla | measurement | 314.50027815324444 | 312.62834862771047 | 1.0 | -| nop | vanilla | measurement | 313.9945984695294 | 309.9417493344898 | 1.0 | -| nop | vanilla | measurement | 310.23631881672645 | 306.25389315105474 | 1.0 | -| nop | vanilla | measurement | 312.31619684500987 | 308.06040862208977 | 1.0 | -| nop | vanilla | measurement | 309.919321367975 | 305.78170782170776 | 1.0 | -| nop | vanilla | measurement | 311.39033433714485 | 307.72232285703535 | 1.0 | -| nop | vanilla | measurement | 311.690600098201 | 307.99051534711623 | 1.0 | -| nop | vanilla | measurement | 310.4323547440936 | 305.7173917757776 | 1.0 | -| nop | vanilla | measurement | 310.9938115865003 | 305.7588417137076 | 1.0 | -| nop | vanilla | measurement | 310.44213503193436 | 306.1118189724036 | 1.0 | -| nop | vanilla | measurement | 308.91487843817936 | 304.42753607474054 | 1.0 | -| nop | vanilla | measurement | 318.68316187544 | 314.0424687271111 | 1.0 | -| nop | vanilla | measurement | 312.2969739481846 | 307.2380921822759 | 1.0 | -| nop | vanilla | measurement | 310.80569495218964 | 307.1913491433016 | 1.0 | -| nop | vanilla | measurement | 312.33587337241556 | 307.7371705365466 | 1.0 | -| nop | vanilla | measurement | 313.6466500999225 | 308.0794974209413 | 1.0 | -| nop | vanilla | measurement | 310.85350280727914 | 305.635719580712 | 1.0 | -| nop | vanilla | measurement | 309.22869471452634 | 305.2533948206384 | 1.0 | -| nop | vanilla | measurement | 330.75083138598546 | 326.6084585543005 | 32.0 | -| nop | vanilla | measurement | 332.6648078724064 | 328.22081538818026 | 32.0 | -| nop | vanilla | measurement | 332.8981989037112 | 327.83205694516977 | 32.0 | -| nop | vanilla | measurement | 333.40105479491143 | 329.0613293779696 | 32.0 | -| nop | vanilla | measurement | 333.4572772050267 | 327.4372041084082 | 32.0 | -| nop | vanilla | measurement | 334.44057824586474 | 328.4662437672578 | 32.0 | -| nop | vanilla | measurement | 332.09816842648036 | 329.8143280999303 | 32.0 | -| nop | vanilla | measurement | 326.88920780885803 | 326.88965174960214 | 32.0 | -| nop | vanilla | measurement | 325.0502761532662 | 325.04652412446626 | 32.0 | -| nop | vanilla | measurement | 327.6612077535455 | 327.65661313431985 | 32.0 | -| nop | vanilla | measurement | 328.5344554039851 | 327.30260613411804 | 32.0 | -| nop | vanilla | measurement | 328.2994650536226 | 328.30002929724753 | 32.0 | -| nop | vanilla | measurement | 328.44165059127045 | 328.44223464878553 | 32.0 | -| nop | vanilla | measurement | 329.50903850731527 | 329.5101353853671 | 32.0 | -| nop | vanilla | measurement | 324.3136216160352 | 324.29934852544204 | 32.0 | -| nop | vanilla | measurement | 330.27945894944486 | 330.2732900149226 | 32.0 | -| nop | vanilla | measurement | 330.1975177063575 | 330.19785644159754 | 32.0 | -| nop | vanilla | measurement | 326.89945995897074 | 326.8948860969212 | 32.0 | -| nop | vanilla | measurement | 336.33790153435376 | 336.33913145217554 | 32.0 | -| nop | vanilla | measurement | 348.2359947591225 | 348.21233617011865 | 32.0 | -| nop | vanilla | measurement | 961.1765567078551 | 961.1280707357316 | 1024.0 | -| nop | vanilla | measurement | 932.1480651417346 | 932.1315413878079 | 1024.0 | -| nop | vanilla | measurement | 985.2599102956906 | 985.2589267545453 | 1024.0 | -| nop | vanilla | measurement | 957.8535952908071 | 957.8449539884227 | 1024.0 | -| nop | vanilla | measurement | 942.4053879888493 | 928.0729218481512 | 1024.0 | -| nop | vanilla | measurement | 906.3546198105088 | 890.0827515940136 | 1024.0 | -| nop | vanilla | measurement | 888.757977130982 | 884.2308619539616 | 1024.0 | -| nop | vanilla | measurement | 940.8074438125454 | 940.792631364743 | 1024.0 | -| nop | vanilla | measurement | 859.1868655037314 | 859.1914539432047 | 1024.0 | -| nop | vanilla | measurement | 837.65754160489 | 837.6589504951687 | 1024.0 | -| nop | vanilla | measurement | 848.949561081648 | 848.9513232567613 | 1024.0 | -| nop | vanilla | measurement | 844.9993386551943 | 844.9798218888498 | 1024.0 | -| nop | vanilla | measurement | 895.5930293040003 | 895.5679476236763 | 1024.0 | -| nop | vanilla | measurement | 851.0907414406681 | 851.0694653273921 | 1024.0 | -| nop | vanilla | measurement | 858.0790859299923 | 858.0763472800115 | 1024.0 | -| nop | vanilla | measurement | 849.775235710253 | 849.7764651352037 | 1024.0 | -| nop | vanilla | measurement | 867.2048306498549 | 867.203547526458 | 1024.0 | -| nop | vanilla | measurement | 904.9285562882774 | 904.8959991408119 | 1024.0 | -| nop | vanilla | measurement | 807.0520823923956 | 806.0381686940358 | 1024.0 | -| nop | vanilla | measurement | 971.0577589417946 | 967.017455005877 | 1024.0 | -| nop | vanilla | measurement | 1112.1677915303087 | 1111.9716139390378 | 32768.0 | -| nop | vanilla | measurement | 1082.2873931202844 | 1082.2714093954808 | 32768.0 | -| nop | vanilla | measurement | 1107.7751017067394 | 1107.6478165725491 | 32768.0 | -| nop | vanilla | measurement | 1056.0737090092593 | 1056.0732841275699 | 32768.0 | -| nop | vanilla | measurement | 1070.6960046342008 | 1070.6936226772682 | 32768.0 | -| nop | vanilla | measurement | 1070.9719109786386 | 1070.949320945413 | 32768.0 | -| nop | vanilla | measurement | 1034.7757954273766 | 1034.7422149361953 | 32768.0 | -| nop | vanilla | measurement | 1062.3453308408978 | 1062.3478349965105 | 32768.0 | -| nop | vanilla | measurement | 1001.2520040737043 | 997.6316091262539 | 32768.0 | -| nop | vanilla | measurement | 1004.3829740037672 | 1004.3612092886864 | 32768.0 | -| nop | vanilla | measurement | 1042.7626298112193 | 1042.7631862925791 | 32768.0 | -| nop | vanilla | measurement | 973.3301912326397 | 973.1977962685884 | 32768.0 | -| nop | vanilla | measurement | 1021.5056493898867 | 1021.5057095481235 | 32768.0 | -| nop | vanilla | measurement | 1004.2440686094927 | 1004.2421152964064 | 32768.0 | -| nop | vanilla | measurement | 1129.3200524140589 | 1129.2717590747477 | 32768.0 | -| nop | vanilla | measurement | 1071.8831394442268 | 1071.8806916881674 | 32768.0 | -| nop | vanilla | measurement | 1044.9765056917904 | 1044.9798464419187 | 32768.0 | -| nop | vanilla | measurement | 1075.3501737126123 | 1075.3331860669682 | 32768.0 | -| nop | vanilla | measurement | 998.7915798751891 | 998.7579354634811 | 32768.0 | -| nop | vanilla | measurement | 970.1559155966636 | 970.1556918761821 | 32768.0 | -| sans_regulation | vanilla | measurement | 23738.825068529306 | 23687.111459186217 | 1.0 | -| sans_regulation | vanilla | measurement | 29138.335481110378 | 28867.22409312591 | 1.0 | -| sans_regulation | vanilla | measurement | 23012.358299848787 | 23011.809155957417 | 1.0 | -| sans_regulation | vanilla | measurement | 22879.23470146041 | 22879.278024990213 | 1.0 | -| sans_regulation | vanilla | measurement | 21606.437150686063 | 21606.055284610735 | 1.0 | -| sans_regulation | vanilla | measurement | 24315.654444480944 | 24189.47043537076 | 1.0 | -| sans_regulation | vanilla | measurement | 24290.38660074855 | 24256.591363781998 | 1.0 | -| sans_regulation | vanilla | measurement | 22075.367413065887 | 22074.203623936493 | 1.0 | -| sans_regulation | vanilla | measurement | 24206.385247900253 | 24205.96742728989 | 1.0 | -| sans_regulation | vanilla | measurement | 23650.88106511718 | 23650.93748887542 | 1.0 | -| sans_regulation | vanilla | measurement | 21503.51151611538 | 21503.290128510896 | 1.0 | -| sans_regulation | vanilla | measurement | 22603.92969280309 | 22556.04364387171 | 1.0 | -| sans_regulation | vanilla | measurement | 22832.249652884573 | 22832.306575059636 | 1.0 | -| sans_regulation | vanilla | measurement | 21128.389199355846 | 21128.38321882456 | 1.0 | -| sans_regulation | vanilla | measurement | 21393.670748656168 | 21393.717098002944 | 1.0 | -| sans_regulation | vanilla | measurement | 23769.544658429677 | 23769.575878395255 | 1.0 | -| sans_regulation | vanilla | measurement | 21918.967534143452 | 21918.465487166657 | 1.0 | -| sans_regulation | vanilla | measurement | 21961.279840536732 | 21960.961304332315 | 1.0 | -| sans_regulation | vanilla | measurement | 24999.865544092972 | 24999.538998255666 | 1.0 | -| sans_regulation | vanilla | measurement | 22841.799829083673 | 22807.612865330524 | 1.0 | -| sans_regulation | vanilla | measurement | 30745.112784305216 | 30744.647329614105 | 32.0 | -| sans_regulation | vanilla | measurement | 30997.847618278956 | 30997.52120313653 | 32.0 | -| sans_regulation | vanilla | measurement | 26773.265556124945 | 26772.853001989643 | 32.0 | -| sans_regulation | vanilla | measurement | 26852.465376690587 | 26780.96020754502 | 32.0 | -| sans_regulation | vanilla | measurement | 28659.167791518234 | 28658.562478055628 | 32.0 | -| sans_regulation | vanilla | measurement | 28455.672726569195 | 28433.84110326529 | 32.0 | -| sans_regulation | vanilla | measurement | 30140.9071119436 | 30125.235165606835 | 32.0 | -| sans_regulation | vanilla | measurement | 31165.901025973508 | 31114.383021885893 | 32.0 | -| sans_regulation | vanilla | measurement | 31729.553622241394 | 31530.9841220302 | 32.0 | -| sans_regulation | vanilla | measurement | 27850.56922713811 | 27517.928919751797 | 32.0 | -| sans_regulation | vanilla | measurement | 27863.727421728825 | 27490.690008972866 | 32.0 | -| sans_regulation | vanilla | measurement | 29210.20563332853 | 28824.136932859972 | 32.0 | -| sans_regulation | vanilla | measurement | 31009.491631888774 | 30626.252018881896 | 32.0 | -| sans_regulation | vanilla | measurement | 31467.93746339954 | 30930.420824718043 | 32.0 | -| sans_regulation | vanilla | measurement | 31278.665470298427 | 31062.188155892687 | 32.0 | -| sans_regulation | vanilla | measurement | 27853.74961958565 | 27853.73222018489 | 32.0 | -| sans_regulation | vanilla | measurement | 27778.196270441887 | 27775.59665275228 | 32.0 | -| sans_regulation | vanilla | measurement | 32296.026138179943 | 32295.63316818164 | 32.0 | -| sans_regulation | vanilla | measurement | 31422.34525805611 | 31360.819607537156 | 32.0 | -| sans_regulation | vanilla | measurement | 28538.44423207017 | 28536.98146919987 | 32.0 | -| sans_regulation | vanilla | measurement | 30988.458258912495 | 30987.942876424135 | 1024.0 | -| sans_regulation | vanilla | measurement | 30206.436782936536 | 30206.389504187995 | 1024.0 | -| sans_regulation | vanilla | measurement | 31261.62300030161 | 31258.946528522098 | 1024.0 | -| sans_regulation | vanilla | measurement | 30122.97102140553 | 30122.031836767354 | 1024.0 | -| sans_regulation | vanilla | measurement | 35259.54166168986 | 35259.20963836303 | 1024.0 | -| sans_regulation | vanilla | measurement | 30799.627525710945 | 30796.55345162975 | 1024.0 | -| sans_regulation | vanilla | measurement | 29496.76960026235 | 29495.437537215574 | 1024.0 | -| sans_regulation | vanilla | measurement | 31268.153030877256 | 31266.79321980052 | 1024.0 | -| sans_regulation | vanilla | measurement | 32659.24810447719 | 32658.48084633393 | 1024.0 | -| sans_regulation | vanilla | measurement | 29186.145885495564 | 29185.87265293163 | 1024.0 | -| sans_regulation | vanilla | measurement | 31275.829462934722 | 31275.838077091026 | 1024.0 | -| sans_regulation | vanilla | measurement | 27903.06482474803 | 27902.45206621421 | 1024.0 | -| sans_regulation | vanilla | measurement | 32888.622166635985 | 32887.919653844634 | 1024.0 | -| sans_regulation | vanilla | measurement | 29790.59291010803 | 29788.202572347443 | 1024.0 | -| sans_regulation | vanilla | measurement | 33735.681354419474 | 33735.25894168561 | 1024.0 | -| sans_regulation | vanilla | measurement | 35563.131277063156 | 35560.49581199635 | 1024.0 | -| sans_regulation | vanilla | measurement | 30951.492159888523 | 30758.95990631588 | 1024.0 | -| sans_regulation | vanilla | measurement | 34846.644714379516 | 34189.19364058605 | 1024.0 | -| sans_regulation | vanilla | measurement | 33492.68897625004 | 32931.72216267731 | 1024.0 | -| sans_regulation | vanilla | measurement | 32136.118613754817 | 31705.88761859398 | 1024.0 | -| sans_regulation | vanilla | measurement | 36815.19566032653 | 36814.18047117172 | 32768.0 | -| sans_regulation | vanilla | measurement | 37040.27954127459 | 37040.17309361454 | 32768.0 | -| sans_regulation | vanilla | measurement | 36282.9871668029 | 36282.962492250495 | 32768.0 | -| sans_regulation | vanilla | measurement | 38338.96193431918 | 38337.865034098155 | 32768.0 | -| sans_regulation | vanilla | measurement | 35984.64990697935 | 35983.68301301959 | 32768.0 | -| sans_regulation | vanilla | measurement | 35774.72926232416 | 35774.66379417282 | 32768.0 | -| sans_regulation | vanilla | measurement | 36623.65306876741 | 36623.80991940504 | 32768.0 | -| sans_regulation | vanilla | measurement | 34905.897954092594 | 34904.353316800945 | 32768.0 | -| sans_regulation | vanilla | measurement | 33155.0525727931 | 33155.12901425897 | 32768.0 | -| sans_regulation | vanilla | measurement | 36185.56261625121 | 36184.490638561576 | 32768.0 | -| sans_regulation | vanilla | measurement | 35980.14513325999 | 35968.70334779924 | 32768.0 | -| sans_regulation | vanilla | measurement | 35395.238127783734 | 35395.27495350279 | 32768.0 | -| sans_regulation | vanilla | measurement | 35854.08121512595 | 35669.64730316179 | 32768.0 | -| sans_regulation | vanilla | measurement | 37071.68704287151 | 36384.45740855602 | 32768.0 | -| sans_regulation | vanilla | measurement | 37510.0232486373 | 36879.26769993876 | 32768.0 | -| sans_regulation | vanilla | measurement | 36601.62641038003 | 36601.41816490973 | 32768.0 | -| sans_regulation | vanilla | measurement | 36582.208369545406 | 36580.7269063855 | 32768.0 | -| sans_regulation | vanilla | measurement | 36695.067141943335 | 36695.07352758869 | 32768.0 | -| sans_regulation | vanilla | measurement | 35868.58921264258 | 35867.27334159941 | 32768.0 | -| sans_regulation | vanilla | measurement | 35831.20601363138 | 35830.7473031621 | 32768.0 | -| control | vanilla | measurement | 2.4450000637443736e-06 | 1.8740000000002331e-06 | 1.0 | -| control | vanilla | measurement | 1.9560002328944393e-06 | 1.3719999999999184e-06 | 1.0 | -| control | vanilla | measurement | 1.3970002328278497e-06 | 1.341999999999663e-06 | 1.0 | -| control | vanilla | measurement | 1.3970002328278497e-06 | 1.4130000000000219e-06 | 1.0 | -| control | vanilla | measurement | 1.396999323333148e-06 | 1.3729999999998257e-06 | 1.0 | -| control | vanilla | measurement | 1.4659999578725547e-06 | 1.3219999999997817e-06 | 1.0 | -| control | vanilla | measurement | 2.373999450355768e-06 | 1.6729999999997788e-06 | 1.0 | -| control | vanilla | measurement | 1.886000063677784e-06 | 1.453000000000218e-06 | 1.0 | -| control | vanilla | measurement | 9.769992175279185e-07 | 1.3220000000002154e-06 | 1.0 | -| control | vanilla | measurement | 1.4659999578725547e-06 | 1.2319999999998825e-06 | 1.0 | -| control | vanilla | measurement | 1.4669994925498031e-06 | 1.642999999999957e-06 | 1.0 | -| control | vanilla | measurement | 1.886000063677784e-06 | 1.7529999999997374e-06 | 1.0 | -| control | vanilla | measurement | 1.9560002328944393e-06 | 1.7139999999998823e-06 | 1.0 | -| control | vanilla | measurement | 1.4670004020445049e-06 | 1.311999999999841e-06 | 1.0 | -| control | vanilla | measurement | 1.4670004020445049e-06 | 1.3930000000001407e-06 | 1.0 | -| control | vanilla | measurement | 1.4659999578725547e-06 | 1.3629999999998851e-06 | 1.0 | -| control | vanilla | measurement | 9.080004019779153e-07 | 1.1730000000001461e-06 | 1.0 | -| control | vanilla | measurement | 1.4670004020445049e-06 | 1.432999999999903e-06 | 1.0 | -| control | vanilla | measurement | 1.3959997886558995e-06 | 1.2319999999998825e-06 | 1.0 | -| control | vanilla | measurement | 1.4669994925498031e-06 | 1.453000000000218e-06 | 1.0 | -| control | vanilla | measurement | 5.168999450688716e-06 | 2.61500000000095e-06 | 32.0 | -| control | vanilla | measurement | 3.3520000215503387e-06 | 2.2450000000014125e-06 | 32.0 | -| control | vanilla | measurement | 3.2819998523336835e-06 | 2.4139999999991946e-06 | 32.0 | -| control | vanilla | measurement | 1.886000063677784e-06 | 1.5429999999996835e-06 | 32.0 | -| control | vanilla | measurement | 5.658000191033352e-06 | 3.837999999999064e-06 | 32.0 | -| control | vanilla | measurement | 2.9340008040890098e-06 | 2.164000000002414e-06 | 32.0 | -| control | vanilla | measurement | 3.840999852400273e-06 | 2.505000000003338e-06 | 32.0 | -| control | vanilla | measurement | 5.169000360183418e-06 | 2.3639999999977568e-06 | 32.0 | -| control | vanilla | measurement | 1.3970002328278497e-06 | 1.3319999999997223e-06 | 32.0 | -| control | vanilla | measurement | 1.8159998944611289e-06 | 1.4129999999987208e-06 | 32.0 | -| control | vanilla | measurement | 5.308999789122026e-06 | 2.5549999999978368e-06 | 32.0 | -| control | vanilla | measurement | 9.080004019779153e-07 | 1.3430000000008713e-06 | 32.0 | -| control | vanilla | measurement | 2.3749998945277184e-06 | 1.6840000000017952e-06 | 32.0 | -| control | vanilla | measurement | 1.8159998944611289e-06 | 1.594000000000595e-06 | 32.0 | -| control | vanilla | measurement | 1.886000063677784e-06 | 1.7240000000015574e-06 | 32.0 | -| control | vanilla | measurement | 4.400000761961564e-06 | 2.8759999999988795e-06 | 32.0 | -| control | vanilla | measurement | 3.772000127355568e-06 | 2.835999999999117e-06 | 32.0 | -| control | vanilla | measurement | 3.3520000215503387e-06 | 2.0039999999998948e-06 | 32.0 | -| control | vanilla | measurement | 8.451000212517101e-06 | 3.6870000000022163e-06 | 32.0 | -| control | vanilla | measurement | 5.1679999160114676e-06 | 2.7860000000011487e-06 | 32.0 | -| control | vanilla | measurement | 3.422000190766994e-06 | 2.644999999967812e-06 | 1024.0 | -| control | vanilla | measurement | 6.565999683516566e-06 | 3.778000000009829e-06 | 1024.0 | -| control | vanilla | measurement | 4.260000423528254e-06 | 2.2839999999635374e-06 | 1024.0 | -| control | vanilla | measurement | 3.840999852400273e-06 | 2.4350000000228356e-06 | 1024.0 | -| control | vanilla | measurement | 3.282999387010932e-06 | 2.143999999981716e-06 | 1024.0 | -| control | vanilla | measurement | 3.7719992178608663e-06 | 2.3749999999989058e-06 | 1024.0 | -| control | vanilla | measurement | 3.772000127355568e-06 | 2.6049999999888662e-06 | 1024.0 | -| control | vanilla | measurement | 3.9120004657888785e-06 | 2.4149999999778515e-06 | 1024.0 | -| control | vanilla | measurement | 5.308999789122026e-06 | 3.1060000000304733e-06 | 1024.0 | -| control | vanilla | measurement | 3.352999556227587e-06 | 2.3650000000041693e-06 | 1024.0 | -| control | vanilla | measurement | 3.840999852400273e-06 | 2.3239999999979948e-06 | 1024.0 | -| control | vanilla | measurement | 6.356000085361302e-06 | 4.107999999947154e-06 | 1024.0 | -| control | vanilla | measurement | 3.422000190766994e-06 | 2.1049999999744884e-06 | 1024.0 | -| control | vanilla | measurement | 3.3520000215503387e-06 | 2.1040000000027703e-06 | 1024.0 | -| control | vanilla | measurement | 2.863999725377653e-06 | 1.8039999999386325e-06 | 1024.0 | -| control | vanilla | measurement | 3.772000127355568e-06 | 3.206000000033349e-06 | 1024.0 | -| control | vanilla | measurement | 2.8630001907004043e-06 | 1.5529999999319697e-06 | 1024.0 | -| control | vanilla | measurement | 2.3749998945277184e-06 | 1.5729999999214428e-06 | 1024.0 | -| control | vanilla | measurement | 3.282999387010932e-06 | 1.8739999999572987e-06 | 1024.0 | -| control | vanilla | measurement | 4.260000423528254e-06 | 2.5649999999544093e-06 | 1024.0 | -| control | vanilla | measurement | 3.352999556227587e-06 | 2.5450000000759587e-06 | 32768.0 | -| control | vanilla | measurement | 3.353000465722289e-06 | 2.073999999296916e-06 | 32768.0 | -| control | vanilla | measurement | 4.2609999582055025e-06 | 2.75499999879969e-06 | 32768.0 | -| control | vanilla | measurement | 3.770999683183618e-06 | 2.555000000015184e-06 | 32768.0 | -| control | vanilla | measurement | 3.770999683183618e-06 | 2.7249999998701924e-06 | 32768.0 | -| control | vanilla | measurement | 3.772000127355568e-06 | 2.3850000001601757e-06 | 32768.0 | -| control | vanilla | measurement | 3.2830002965056337e-06 | 2.0639999984695123e-06 | 32768.0 | -| control | vanilla | measurement | 3.772000127355568e-06 | 2.304000000563633e-06 | 32768.0 | -| control | vanilla | measurement | 3.3520000215503387e-06 | 1.984000000732067e-06 | 32768.0 | -| control | vanilla | measurement | 2.8640006348723546e-06 | 1.794000000998608e-06 | 32768.0 | -| control | vanilla | measurement | 3.282999387010932e-06 | 2.024000000488968e-06 | 32768.0 | -| control | vanilla | measurement | 4.260000423528254e-06 | 2.5440000008813968e-06 | 32768.0 | -| control | vanilla | measurement | 3.770999683183618e-06 | 2.645000002132747e-06 | 32768.0 | -| control | vanilla | measurement | 3.352999556227587e-06 | 2.443999999712787e-06 | 32768.0 | -| control | vanilla | measurement | 6.104299973230809e-05 | 1.1472000000622984e-05 | 32768.0 | -| control | vanilla | measurement | 3.772000127355568e-06 | 2.644999998580033e-06 | 32768.0 | -| control | vanilla | measurement | 3.840999852400273e-06 | 2.4950000003798323e-06 | 32768.0 | -| control | vanilla | measurement | 4.2609999582055025e-06 | 2.666000000317581e-06 | 32768.0 | -| control | vanilla | measurement | 3.422000190766994e-06 | 2.0940000027280803e-06 | 32768.0 | -| control | vanilla | measurement | 4.819000423594844e-06 | 2.5150000020346397e-06 | 32768.0 | -| arithmetic | lite | measurement | 37.25939968715988 | 36.94151981585546 | 1.0 | -| arithmetic | lite | measurement | 36.402936588815344 | 36.40106426639122 | 1.0 | -| arithmetic | lite | measurement | 36.074829256523444 | 36.07387516536972 | 1.0 | -| arithmetic | lite | measurement | 35.99263434335295 | 35.9921233925219 | 1.0 | -| arithmetic | lite | measurement | 36.25945405040838 | 36.25919192936358 | 1.0 | -| arithmetic | lite | measurement | 36.7614544884002 | 36.733730960786744 | 1.0 | -| arithmetic | lite | measurement | 36.76952561471063 | 36.76862854492684 | 1.0 | -| arithmetic | lite | measurement | 36.33801424364706 | 36.33771970287603 | 1.0 | -| arithmetic | lite | measurement | 36.1752151773578 | 36.17528032975224 | 1.0 | -| arithmetic | lite | measurement | 36.05462551001532 | 36.05392071781429 | 1.0 | -| arithmetic | lite | measurement | 36.25260976895211 | 36.251134563180486 | 1.0 | -| arithmetic | lite | measurement | 36.48383429242734 | 36.483939213787174 | 1.0 | -| arithmetic | lite | measurement | 35.837375072928275 | 35.8374131740572 | 1.0 | -| arithmetic | lite | measurement | 36.44457045775965 | 36.444735527810664 | 1.0 | -| arithmetic | lite | measurement | 36.24640893223913 | 36.246412841415335 | 1.0 | -| arithmetic | lite | measurement | 36.149649183153045 | 36.1489214574239 | 1.0 | -| arithmetic | lite | measurement | 36.11878307064271 | 36.118863807489205 | 1.0 | -| arithmetic | lite | measurement | 36.31701496077276 | 36.31642452401514 | 1.0 | -| arithmetic | lite | measurement | 36.29804582541118 | 36.29771792656216 | 1.0 | -| arithmetic | lite | measurement | 36.05173321435791 | 36.051387594094365 | 1.0 | -| arithmetic | lite | measurement | 35.997805616483355 | 35.996932939667225 | 1.0 | -| arithmetic | lite | measurement | 35.949528083309474 | 35.94892810817672 | 1.0 | -| arithmetic | lite | measurement | 35.980973843194505 | 35.98105494491295 | 1.0 | -| arithmetic | lite | measurement | 36.10124400357101 | 36.100690886039686 | 1.0 | -| arithmetic | lite | measurement | 36.287499378492825 | 36.286801257888825 | 1.0 | -| arithmetic | lite | measurement | 36.18101608286411 | 36.181079723658144 | 1.0 | -| arithmetic | lite | measurement | 36.033695392793895 | 36.03277935064259 | 1.0 | -| arithmetic | lite | measurement | 35.97146060849447 | 35.96110138342966 | 1.0 | -| arithmetic | lite | measurement | 36.28039917857458 | 36.280527919662084 | 1.0 | -| arithmetic | lite | measurement | 36.18431268792042 | 36.18437231539695 | 1.0 | -| arithmetic | lite | measurement | 35.90008593848849 | 35.90014832846675 | 1.0 | -| arithmetic | lite | measurement | 36.09280651238392 | 36.09192189973027 | 1.0 | -| arithmetic | lite | measurement | 36.354496852242356 | 36.35466510175903 | 1.0 | -| arithmetic | lite | measurement | 35.984361453193834 | 35.98346969982697 | 1.0 | -| arithmetic | lite | measurement | 35.883309779494766 | 35.88216455734594 | 1.0 | -| arithmetic | lite | measurement | 35.89901832599877 | 35.899071386032524 | 1.0 | -| arithmetic | lite | measurement | 36.32183404621953 | 36.321962735144034 | 1.0 | -| arithmetic | lite | measurement | 36.974672619832575 | 36.618242566715125 | 1.0 | -| arithmetic | lite | measurement | 37.053031518433095 | 36.62524952050457 | 1.0 | -| arithmetic | lite | measurement | 37.25437227052867 | 36.57855232292133 | 1.0 | -| arithmetic | lite | measurement | 36.59231036416791 | 36.10443141327766 | 1.0 | -| arithmetic | lite | measurement | 36.74839086708554 | 36.23262618526465 | 1.0 | -| arithmetic | lite | measurement | 36.32271099701327 | 36.18125751141542 | 1.0 | -| arithmetic | lite | measurement | 35.979543253833484 | 35.978978878022104 | 1.0 | -| arithmetic | lite | measurement | 36.01893984284767 | 36.018661668386144 | 1.0 | -| arithmetic | lite | measurement | 36.058405285023746 | 36.05848258175133 | 1.0 | -| arithmetic | lite | measurement | 35.943607349548 | 35.94334439464011 | 1.0 | -| arithmetic | lite | measurement | 36.59782204532298 | 36.59782360890047 | 1.0 | -| arithmetic | lite | measurement | 35.73477636942875 | 35.73492293622595 | 1.0 | -| arithmetic | lite | measurement | 36.412216213269645 | 36.41120541350432 | 1.0 | -| arithmetic | lite | measurement | 32.42259154614784 | 32.4221331587587 | 32.0 | -| arithmetic | lite | measurement | 32.56184321686113 | 32.5609011067292 | 32.0 | -| arithmetic | lite | measurement | 33.25640111083587 | 33.25643682933786 | 32.0 | -| arithmetic | lite | measurement | 32.7417871674457 | 32.74188583166995 | 32.0 | -| arithmetic | lite | measurement | 32.566543891583464 | 32.566534015965885 | 32.0 | -| arithmetic | lite | measurement | 32.49130429850818 | 32.49103396386513 | 32.0 | -| arithmetic | lite | measurement | 32.512245216077744 | 32.512313745780766 | 32.0 | -| arithmetic | lite | measurement | 32.734614347759496 | 32.73463862152054 | 32.0 | -| arithmetic | lite | measurement | 32.675238753262484 | 32.67530755983445 | 32.0 | -| arithmetic | lite | measurement | 32.69375925784872 | 32.69345555829265 | 32.0 | -| arithmetic | lite | measurement | 32.73311384759461 | 32.730725415079895 | 32.0 | -| arithmetic | lite | measurement | 32.690746443772525 | 32.69012819988608 | 32.0 | -| arithmetic | lite | measurement | 32.64667751940818 | 32.6451820494501 | 32.0 | -| arithmetic | lite | measurement | 32.77765541902101 | 32.77771988775722 | 32.0 | -| arithmetic | lite | measurement | 33.57988442896753 | 33.579967864423786 | 32.0 | -| arithmetic | lite | measurement | 33.830958729880535 | 33.83095988350769 | 32.0 | -| arithmetic | lite | measurement | 33.18071314429759 | 33.17958205845819 | 32.0 | -| arithmetic | lite | measurement | 32.95961474231308 | 32.95843584712516 | 32.0 | -| arithmetic | lite | measurement | 34.01271126409156 | 34.0124340995952 | 32.0 | -| arithmetic | lite | measurement | 32.99232389093645 | 32.99133341781485 | 32.0 | -| arithmetic | lite | measurement | 32.741985372807825 | 32.74109153341396 | 32.0 | -| arithmetic | lite | measurement | 33.46988962615244 | 33.46995741745466 | 32.0 | -| arithmetic | lite | measurement | 33.21912571628318 | 33.218326618655006 | 32.0 | -| arithmetic | lite | measurement | 32.38594284055586 | 32.38547914616344 | 32.0 | -| arithmetic | lite | measurement | 32.52318966034744 | 32.52282421485519 | 32.0 | -| arithmetic | lite | measurement | 32.571978272434094 | 32.572019113366956 | 32.0 | -| arithmetic | lite | measurement | 32.56263188499742 | 32.56157223614725 | 32.0 | -| arithmetic | lite | measurement | 32.480104517623566 | 32.47934612252592 | 32.0 | -| arithmetic | lite | measurement | 32.73881108707303 | 32.73900352394854 | 32.0 | -| arithmetic | lite | measurement | 32.641615460104646 | 32.640656598356365 | 32.0 | -| arithmetic | lite | measurement | 32.689688686956195 | 32.68912152883289 | 32.0 | -| arithmetic | lite | measurement | 33.213547538476966 | 33.21359040994242 | 32.0 | -| arithmetic | lite | measurement | 32.98226724069425 | 32.982336462563396 | 32.0 | -| arithmetic | lite | measurement | 32.6031560480617 | 32.59927237628023 | 32.0 | -| arithmetic | lite | measurement | 32.63885881325545 | 32.63847103215657 | 32.0 | -| arithmetic | lite | measurement | 32.48984837723871 | 32.48869790924928 | 32.0 | -| arithmetic | lite | measurement | 32.533522044585965 | 32.532904585175956 | 32.0 | -| arithmetic | lite | measurement | 32.526015852133646 | 32.52605406267515 | 32.0 | -| arithmetic | lite | measurement | 32.77923344773616 | 32.77929320930392 | 32.0 | -| arithmetic | lite | measurement | 32.922462424323435 | 32.91530700236769 | 32.0 | -| arithmetic | lite | measurement | 32.49278057090668 | 32.49247871726456 | 32.0 | -| arithmetic | lite | measurement | 32.56692212008771 | 32.56640997031727 | 32.0 | -| arithmetic | lite | measurement | 32.586886455102466 | 32.58686988802015 | 32.0 | -| arithmetic | lite | measurement | 32.66826090857516 | 32.66652551551633 | 32.0 | -| arithmetic | lite | measurement | 32.77596801989131 | 32.774667386451064 | 32.0 | -| arithmetic | lite | measurement | 32.46760696467977 | 32.467677986354765 | 32.0 | -| arithmetic | lite | measurement | 32.58184733143681 | 32.573311209397374 | 32.0 | -| arithmetic | lite | measurement | 32.43970550786237 | 32.43975340943299 | 32.0 | -| arithmetic | lite | measurement | 32.57845107408995 | 32.57796850514252 | 32.0 | -| arithmetic | lite | measurement | 32.606537630226654 | 32.6042002835141 | 32.0 | -| arithmetic | lite | measurement | 32.84358767327927 | 32.84312007385357 | 1024.0 | -| arithmetic | lite | measurement | 32.5195875672589 | 32.51770211676165 | 1024.0 | -| arithmetic | lite | measurement | 33.347582953724455 | 33.347216649951505 | 1024.0 | -| arithmetic | lite | measurement | 32.616582941585804 | 32.616630784942316 | 1024.0 | -| arithmetic | lite | measurement | 32.90814654090218 | 32.90810665541945 | 1024.0 | -| arithmetic | lite | measurement | 32.91461574997655 | 32.913572544082726 | 1024.0 | -| arithmetic | lite | measurement | 33.27580884643901 | 33.27530035493703 | 1024.0 | -| arithmetic | lite | measurement | 33.31779595298476 | 33.316916891138476 | 1024.0 | -| arithmetic | lite | measurement | 33.12952267804216 | 33.1288716152079 | 1024.0 | -| arithmetic | lite | measurement | 34.21239141220364 | 34.212130622739906 | 1024.0 | -| arithmetic | lite | measurement | 33.92403964628505 | 33.92299337235164 | 1024.0 | -| arithmetic | lite | measurement | 34.175856627538984 | 34.17554252961088 | 1024.0 | -| arithmetic | lite | measurement | 34.91898814447669 | 34.91699511839519 | 1024.0 | -| arithmetic | lite | measurement | 34.45236030534325 | 34.450848781075535 | 1024.0 | -| arithmetic | lite | measurement | 33.670909321642526 | 33.67100922700995 | 1024.0 | -| arithmetic | lite | measurement | 33.54052335364208 | 33.53988106369734 | 1024.0 | -| arithmetic | lite | measurement | 33.3892812263121 | 33.38936372975349 | 1024.0 | -| arithmetic | lite | measurement | 33.65128663893902 | 33.649686954345974 | 1024.0 | -| arithmetic | lite | measurement | 33.993609459247054 | 33.993305620296326 | 1024.0 | -| arithmetic | lite | measurement | 33.50559309543118 | 33.505011975942345 | 1024.0 | -| arithmetic | lite | measurement | 33.94137126472459 | 33.94138655727061 | 1024.0 | -| arithmetic | lite | measurement | 34.0418954466852 | 34.041919943669505 | 1024.0 | -| arithmetic | lite | measurement | 32.58865708616578 | 32.587089808466864 | 1024.0 | -| arithmetic | lite | measurement | 33.37752192178321 | 33.37773836745836 | 1024.0 | -| arithmetic | lite | measurement | 32.872406253301975 | 32.87029299554492 | 1024.0 | -| arithmetic | lite | measurement | 33.953902630640684 | 33.95346465777551 | 1024.0 | -| arithmetic | lite | measurement | 33.12852937696859 | 33.12820128077356 | 1024.0 | -| arithmetic | lite | measurement | 33.81159052965333 | 33.81124191546676 | 1024.0 | -| arithmetic | lite | measurement | 33.734719330313645 | 33.73389717233693 | 1024.0 | -| arithmetic | lite | measurement | 33.93367773530096 | 33.93366330553433 | 1024.0 | -| arithmetic | lite | measurement | 33.83948258789389 | 33.83954716207635 | 1024.0 | -| arithmetic | lite | measurement | 33.928780166315065 | 33.92798629244465 | 1024.0 | -| arithmetic | lite | measurement | 34.364029517678446 | 34.36369705907616 | 1024.0 | -| arithmetic | lite | measurement | 33.30465561478728 | 33.304181543612465 | 1024.0 | -| arithmetic | lite | measurement | 33.1803566363734 | 33.179483183321075 | 1024.0 | -| arithmetic | lite | measurement | 33.13907351751217 | 33.13833769812138 | 1024.0 | -| arithmetic | lite | measurement | 33.636020434974974 | 33.636067415423334 | 1024.0 | -| arithmetic | lite | measurement | 33.21797844327787 | 33.21803232706158 | 1024.0 | -| arithmetic | lite | measurement | 33.10575329886226 | 33.10536839462608 | 1024.0 | -| arithmetic | lite | measurement | 32.796806450629894 | 32.79418853650992 | 1024.0 | -| arithmetic | lite | measurement | 32.72269722288073 | 32.722398417583115 | 1024.0 | -| arithmetic | lite | measurement | 33.31511221837056 | 33.31351133525802 | 1024.0 | -| arithmetic | lite | measurement | 33.224653695350284 | 33.22482014035019 | 1024.0 | -| arithmetic | lite | measurement | 32.57245031426095 | 32.571229733146026 | 1024.0 | -| arithmetic | lite | measurement | 32.886534187885246 | 32.886585818411426 | 1024.0 | -| arithmetic | lite | measurement | 33.37990531705548 | 33.37915981395145 | 1024.0 | -| arithmetic | lite | measurement | 33.71926482098905 | 33.71929626917182 | 1024.0 | -| arithmetic | lite | measurement | 33.44526342774309 | 33.444371949655874 | 1024.0 | -| arithmetic | lite | measurement | 33.23388056139813 | 33.233973755277795 | 1024.0 | -| arithmetic | lite | measurement | 33.09498988405509 | 33.094931733798816 | 1024.0 | -| arithmetic | lite | measurement | 74.95881326439178 | 74.9573986066382 | 32768.0 | -| arithmetic | lite | measurement | 77.05715202958437 | 77.05050884664948 | 32768.0 | -| arithmetic | lite | measurement | 71.69948144859538 | 71.69750615694373 | 32768.0 | -| arithmetic | lite | measurement | 74.27900210342798 | 74.27669989189465 | 32768.0 | -| arithmetic | lite | measurement | 79.41953061827385 | 79.41637917295803 | 32768.0 | -| arithmetic | lite | measurement | 89.80389308266373 | 89.80092465680538 | 32768.0 | -| arithmetic | lite | measurement | 83.1189896135092 | 83.11556663295939 | 32768.0 | -| arithmetic | lite | measurement | 82.23206862059013 | 82.23115162772883 | 32768.0 | -| arithmetic | lite | measurement | 84.07943198937822 | 84.07534019991743 | 32768.0 | -| arithmetic | lite | measurement | 76.35435357997045 | 76.3504427141991 | 32768.0 | -| arithmetic | lite | measurement | 82.43267737418405 | 82.43088430373678 | 32768.0 | -| arithmetic | lite | measurement | 92.88165813917071 | 92.65235456773455 | 32768.0 | -| arithmetic | lite | measurement | 80.74974280999989 | 80.23062951367402 | 32768.0 | -| arithmetic | lite | measurement | 83.52577094910016 | 83.24374912619243 | 32768.0 | -| arithmetic | lite | measurement | 87.40113328278292 | 87.34686964582342 | 32768.0 | -| arithmetic | lite | measurement | 84.97534787399657 | 84.97536783244543 | 32768.0 | -| arithmetic | lite | measurement | 85.25275449602613 | 85.2341828231458 | 32768.0 | -| arithmetic | lite | measurement | 80.17747187638598 | 80.17548161578672 | 32768.0 | -| arithmetic | lite | measurement | 74.47007578942032 | 74.46848719348638 | 32768.0 | -| arithmetic | lite | measurement | 74.63523347793408 | 74.51141021788662 | 32768.0 | -| arithmetic | lite | measurement | 73.88286462983048 | 73.83420004732149 | 32768.0 | -| arithmetic | lite | measurement | 80.98822578916577 | 80.98848365284937 | 32768.0 | -| arithmetic | lite | measurement | 80.535628446285 | 80.53350636012135 | 32768.0 | -| arithmetic | lite | measurement | 71.48732940357675 | 71.48650102651455 | 32768.0 | -| arithmetic | lite | measurement | 74.66568983173994 | 74.66162338951253 | 32768.0 | -| arithmetic | lite | measurement | 72.99665815068008 | 72.79788537820255 | 32768.0 | -| arithmetic | lite | measurement | 78.21235761742764 | 76.66590318795954 | 32768.0 | -| arithmetic | lite | measurement | 80.95975400767146 | 80.55143018036692 | 32768.0 | -| arithmetic | lite | measurement | 78.40822533208343 | 77.9470821638076 | 32768.0 | -| arithmetic | lite | measurement | 81.09219664027053 | 80.90049466115508 | 32768.0 | -| arithmetic | lite | measurement | 84.23715833433411 | 84.23594356098508 | 32768.0 | -| arithmetic | lite | measurement | 80.57434901136305 | 80.57309541883856 | 32768.0 | -| arithmetic | lite | measurement | 82.49305609268713 | 82.49114556633869 | 32768.0 | -| arithmetic | lite | measurement | 87.69112961951502 | 87.55319011986896 | 32768.0 | -| arithmetic | lite | measurement | 78.8061530822119 | 78.47188313027954 | 32768.0 | -| arithmetic | lite | measurement | 74.2127686624977 | 74.1765582764428 | 32768.0 | -| arithmetic | lite | measurement | 73.3364341506627 | 73.31743806057743 | 32768.0 | -| arithmetic | lite | measurement | 76.16675057540763 | 76.16272624568272 | 32768.0 | -| arithmetic | lite | measurement | 79.13710503516391 | 78.89499060903125 | 32768.0 | -| arithmetic | lite | measurement | 70.45980714707156 | 70.45688173168787 | 32768.0 | -| arithmetic | lite | measurement | 70.56714309895766 | 70.54434671326891 | 32768.0 | -| arithmetic | lite | measurement | 72.08031126061118 | 72.0795632164687 | 32768.0 | -| arithmetic | lite | measurement | 78.3183929745159 | 78.31737718718799 | 32768.0 | -| arithmetic | lite | measurement | 70.07208018046157 | 70.06832588898499 | 32768.0 | -| arithmetic | lite | measurement | 68.56598091642026 | 68.56537337990834 | 32768.0 | -| arithmetic | lite | measurement | 76.20033931430359 | 76.19502556632536 | 32768.0 | -| arithmetic | lite | measurement | 79.13725951404521 | 79.13033351931209 | 32768.0 | -| arithmetic | lite | measurement | 86.78484719638791 | 86.59293864690743 | 32768.0 | -| arithmetic | lite | measurement | 73.83544994764978 | 73.82324293701517 | 32768.0 | -| arithmetic | lite | measurement | 80.12618215372174 | 80.10648711744321 | 32768.0 | -| complete | lite | measurement | 723.5279090006092 | 722.5609159376743 | 1.0 | -| complete | lite | measurement | 689.9299645152024 | 689.7053114989237 | 1.0 | -| complete | lite | measurement | 624.1789696112947 | 624.1679703236015 | 1.0 | -| complete | lite | measurement | 653.9315293574862 | 653.916674137209 | 1.0 | -| complete | lite | measurement | 423.6287079914073 | 423.6317366798336 | 1.0 | -| complete | lite | measurement | 792.4165147021598 | 792.3879458353523 | 1.0 | -| complete | lite | measurement | 511.5062973823863 | 511.50934649964444 | 1.0 | -| complete | lite | measurement | 749.8973241093031 | 749.8136929936142 | 1.0 | -| complete | lite | measurement | 388.2626551933546 | 388.2642869868442 | 1.0 | -| complete | lite | measurement | 305.38903747960063 | 305.38522807093125 | 1.0 | -| complete | lite | measurement | 740.9871862242487 | 740.980073510138 | 1.0 | -| complete | lite | measurement | 553.8847645191187 | 553.8509768632572 | 1.0 | -| complete | lite | measurement | 194.43151290348658 | 194.42738576027602 | 1.0 | -| complete | lite | measurement | 601.5960902772182 | 601.5977776743886 | 1.0 | -| complete | lite | measurement | 545.330795618239 | 544.7967815768486 | 1.0 | -| complete | lite | measurement | 674.8158376997293 | 674.7654438807175 | 1.0 | -| complete | lite | measurement | 170.12929296211638 | 170.1298524009958 | 1.0 | -| complete | lite | measurement | 587.2471940088383 | 587.2103107102614 | 1.0 | -| complete | lite | measurement | 322.8478778162661 | 322.84949258941117 | 1.0 | -| complete | lite | measurement | 563.8738514749195 | 563.6268605906007 | 1.0 | -| complete | lite | measurement | 609.04551319188 | 609.0485407477425 | 1.0 | -| complete | lite | measurement | 306.3110416138241 | 306.3045620994985 | 1.0 | -| complete | lite | measurement | 218.02243318822838 | 218.02484456543633 | 1.0 | -| complete | lite | measurement | 674.5809559039634 | 674.364465258575 | 1.0 | -| complete | lite | measurement | 295.649423141528 | 294.62779790726546 | 1.0 | -| complete | lite | measurement | 314.86336169814024 | 314.86643124055956 | 1.0 | -| complete | lite | measurement | 662.8352875888231 | 662.8362112888786 | 1.0 | -| complete | lite | measurement | 517.3836462062272 | 517.0420442127489 | 1.0 | -| complete | lite | measurement | 632.4348264209331 | 632.4040379511121 | 1.0 | -| complete | lite | measurement | 754.6268560515682 | 752.629038206435 | 1.0 | -| complete | lite | measurement | 800.1500194617701 | 794.33572050261 | 1.0 | -| complete | lite | measurement | 681.6686721753787 | 679.886796884842 | 1.0 | -| complete | lite | measurement | 201.37702711633662 | 201.37070647122894 | 1.0 | -| complete | lite | measurement | 627.2485228175797 | 627.251651368133 | 1.0 | -| complete | lite | measurement | 554.924210116796 | 554.9053888899592 | 1.0 | -| complete | lite | measurement | 669.5785626833974 | 669.5726630346591 | 1.0 | -| complete | lite | measurement | 470.0901946019736 | 470.0582032796977 | 1.0 | -| complete | lite | measurement | 640.2799887428051 | 640.2810849270171 | 1.0 | -| complete | lite | measurement | 165.8120282920551 | 165.7990726692745 | 1.0 | -| complete | lite | measurement | 581.4286544275313 | 581.4181408221815 | 1.0 | -| complete | lite | measurement | 649.630423756873 | 649.618785596189 | 1.0 | -| complete | lite | measurement | 588.2940246635737 | 588.2872728056749 | 1.0 | -| complete | lite | measurement | 639.9167853451828 | 639.9053332864304 | 1.0 | -| complete | lite | measurement | 574.9246424637286 | 574.9282453454751 | 1.0 | -| complete | lite | measurement | 260.29852854859837 | 260.29892344755626 | 1.0 | -| complete | lite | measurement | 600.6002196910182 | 600.5923421455467 | 1.0 | -| complete | lite | measurement | 683.8223592021812 | 683.7702541535267 | 1.0 | -| complete | lite | measurement | 287.4957349836851 | 287.4844406568465 | 1.0 | -| complete | lite | measurement | 755.5579008648407 | 755.5587008745622 | 1.0 | -| complete | lite | measurement | 747.1659243417268 | 747.1626562147138 | 1.0 | -| complete | lite | measurement | 776.3168000001315 | 776.2680910000022 | 32.0 | -| complete | lite | measurement | 543.0815469990193 | 543.075224999999 | 32.0 | -| complete | lite | measurement | 412.9729790001875 | 412.9646849999986 | 32.0 | -| complete | lite | measurement | 674.3139720001636 | 674.2880940000014 | 32.0 | -| complete | lite | measurement | 634.8671240011754 | 634.8521429999997 | 32.0 | -| complete | lite | measurement | 512.6864540015958 | 512.6809889999997 | 32.0 | -| complete | lite | measurement | 691.3717829993402 | 691.374003 | 32.0 | -| complete | lite | measurement | 633.4266260000732 | 633.4187910000005 | 32.0 | -| complete | lite | measurement | 489.41193700011354 | 489.3602950000009 | 32.0 | -| complete | lite | measurement | 513.7209170006827 | 513.7216349999995 | 32.0 | -| complete | lite | measurement | 309.69600600110425 | 309.6972660000006 | 32.0 | -| complete | lite | measurement | 495.7537029986269 | 495.7237939999999 | 32.0 | -| complete | lite | measurement | 569.4776290001755 | 569.4802719999999 | 32.0 | -| complete | lite | measurement | 599.9039649996121 | 599.9052739999975 | 32.0 | -| complete | lite | measurement | 607.7107620003517 | 607.7117190000009 | 32.0 | -| complete | lite | measurement | 734.9686329998804 | 734.9156200000024 | 32.0 | -| complete | lite | measurement | 593.8594770013879 | 593.8521859999994 | 32.0 | -| complete | lite | measurement | 310.89634899944934 | 310.89884400000045 | 32.0 | -| complete | lite | measurement | 370.71848300001875 | 370.710944999999 | 32.0 | -| complete | lite | measurement | 661.2054079996597 | 661.1997110000019 | 32.0 | -| complete | lite | measurement | 483.797777998916 | 483.78999399999856 | 32.0 | -| complete | lite | measurement | 620.9343409991561 | 620.9356069999999 | 32.0 | -| complete | lite | measurement | 395.30866500172124 | 395.3105550000018 | 32.0 | -| complete | lite | measurement | 634.598955999536 | 634.5919449999968 | 32.0 | -| complete | lite | measurement | 658.0641679993278 | 658.0550870000011 | 32.0 | -| complete | lite | measurement | 644.5295499997883 | 644.5327519999964 | 32.0 | -| complete | lite | measurement | 585.7931290011038 | 585.793308999996 | 32.0 | -| complete | lite | measurement | 431.48241299968504 | 431.41328699999804 | 32.0 | -| complete | lite | measurement | 778.6283740006184 | 778.4097549999984 | 32.0 | -| complete | lite | measurement | 504.6634600003017 | 504.62722900000045 | 32.0 | -| complete | lite | measurement | 342.38503400047193 | 342.3669459999985 | 32.0 | -| complete | lite | measurement | 568.0772220002837 | 567.9875729999964 | 32.0 | -| complete | lite | measurement | 817.7993829995103 | 817.7901270000021 | 32.0 | -| complete | lite | measurement | 390.50928899996507 | 390.51038299999874 | 32.0 | -| complete | lite | measurement | 587.0823339992057 | 587.0631909999986 | 32.0 | -| complete | lite | measurement | 416.9056129994715 | 416.87725900000316 | 32.0 | -| complete | lite | measurement | 841.1729830004333 | 841.1669579999952 | 32.0 | -| complete | lite | measurement | 626.7516659991088 | 626.7257439999979 | 32.0 | -| complete | lite | measurement | 765.5477930002235 | 765.549417999999 | 32.0 | -| complete | lite | measurement | 475.855500999387 | 475.85810100000003 | 32.0 | -| complete | lite | measurement | 480.6617640006152 | 480.66167500000034 | 32.0 | -| complete | lite | measurement | 395.58328200109827 | 395.5847309999996 | 32.0 | -| complete | lite | measurement | 703.3904649997567 | 703.3916070000004 | 32.0 | -| complete | lite | measurement | 623.9100550010335 | 623.9122310000056 | 32.0 | -| complete | lite | measurement | 814.8501989999204 | 814.844903000001 | 32.0 | -| complete | lite | measurement | 438.45897699975467 | 438.46025199999866 | 32.0 | -| complete | lite | measurement | 500.5332190012268 | 500.5350469999996 | 32.0 | -| complete | lite | measurement | 723.3550299988565 | 723.3563179999933 | 32.0 | -| complete | lite | measurement | 448.06437700026436 | 448.06628900000334 | 32.0 | -| complete | lite | measurement | 592.5250799991773 | 592.5249860000008 | 32.0 | -| complete | lite | measurement | 1106.8748360259938 | 1106.8841259606756 | 1024.0 | -| complete | lite | measurement | 1086.087296470808 | 1085.9942637347988 | 1024.0 | -| complete | lite | measurement | 759.2298063443624 | 759.226185976529 | 1024.0 | -| complete | lite | measurement | 1110.697751070937 | 1110.6550588819525 | 1024.0 | -| complete | lite | measurement | 1206.4877604082665 | 1206.4489238944236 | 1024.0 | -| complete | lite | measurement | 1148.6865323754814 | 1148.688340707468 | 1024.0 | -| complete | lite | measurement | 1135.7528644291735 | 1135.7561105305708 | 1024.0 | -| complete | lite | measurement | 1105.1430731269438 | 1105.119603723379 | 1024.0 | -| complete | lite | measurement | 1236.0028347806197 | 1235.9706646360069 | 1024.0 | -| complete | lite | measurement | 1275.0093807182702 | 1274.936756564472 | 1024.0 | -| complete | lite | measurement | 1039.724544393963 | 1039.7241608674087 | 1024.0 | -| complete | lite | measurement | 1237.1267536750613 | 1237.1284360154393 | 1024.0 | -| complete | lite | measurement | 1218.6751154279739 | 1218.6302461999048 | 1024.0 | -| complete | lite | measurement | 1374.4634127814647 | 1374.2130403687854 | 1024.0 | -| complete | lite | measurement | 1310.888763514825 | 1310.890290222405 | 1024.0 | -| complete | lite | measurement | 1154.8662668336758 | 1154.8706635243193 | 1024.0 | -| complete | lite | measurement | 1085.1086240372922 | 1084.8112312959868 | 1024.0 | -| complete | lite | measurement | 834.3854451523633 | 834.3825251424829 | 1024.0 | -| complete | lite | measurement | 1010.9688025720333 | 1010.96792249373 | 1024.0 | -| complete | lite | measurement | 1093.9132575165775 | 1093.9132241664195 | 1024.0 | -| complete | lite | measurement | 1382.0368188176062 | 1382.0236176267883 | 1024.0 | -| complete | lite | measurement | 1127.8321141895935 | 1127.8298130155504 | 1024.0 | -| complete | lite | measurement | 664.2453513290922 | 664.2463407223185 | 1024.0 | -| complete | lite | measurement | 1327.7695359109885 | 1327.7718741430915 | 1024.0 | -| complete | lite | measurement | 1113.7395038962864 | 1113.7402376029227 | 1024.0 | -| complete | lite | measurement | 1075.1306464054423 | 1075.1308150091695 | 1024.0 | -| complete | lite | measurement | 1121.9231218175569 | 1121.9227901668337 | 1024.0 | -| complete | lite | measurement | 1310.8038886531228 | 1310.789505376825 | 1024.0 | -| complete | lite | measurement | 962.5158896021986 | 962.5145574404677 | 1024.0 | -| complete | lite | measurement | 1052.4198998022484 | 1052.4209651599765 | 1024.0 | -| complete | lite | measurement | 728.2507300027688 | 728.2480675308225 | 1024.0 | -| complete | lite | measurement | 835.0209199512572 | 835.0186187755288 | 1024.0 | -| complete | lite | measurement | 1325.9237962374355 | 1325.9236850686534 | 1024.0 | -| complete | lite | measurement | 1077.9636517953254 | 1077.962308513233 | 1024.0 | -| complete | lite | measurement | 1241.2847937069932 | 1240.9641742816546 | 1024.0 | -| complete | lite | measurement | 1305.9336753604957 | 1305.9093147608598 | 1024.0 | -| complete | lite | measurement | 1049.9126034791852 | 1049.8834756282652 | 1024.0 | -| complete | lite | measurement | 1219.7487938279448 | 1219.711576657695 | 1024.0 | -| complete | lite | measurement | 623.4981342320508 | 623.499740608164 | 1024.0 | -| complete | lite | measurement | 989.6119109046139 | 989.6134691064394 | 1024.0 | -| complete | lite | measurement | 1023.7802487936325 | 1023.7787813771474 | 1024.0 | -| complete | lite | measurement | 1184.9257027689446 | 1184.8060879264333 | 1024.0 | -| complete | lite | measurement | 1061.8913240838033 | 1061.8955781102873 | 1024.0 | -| complete | lite | measurement | 1068.9951679024118 | 1068.969065670592 | 1024.0 | -| complete | lite | measurement | 1267.6781669887555 | 1267.5211107899431 | 1024.0 | -| complete | lite | measurement | 1270.249720227031 | 1270.2490513670098 | 1024.0 | -| complete | lite | measurement | 1210.1837494722904 | 1209.9341682044974 | 1024.0 | -| complete | lite | measurement | 1068.3397736623865 | 1062.8797366802273 | 1024.0 | -| complete | lite | measurement | 1166.1845072678482 | 1159.5487860461888 | 1024.0 | -| complete | lite | measurement | 1092.607443803562 | 1087.2167144688642 | 1024.0 | -| complete | lite | measurement | 1177.9611134609809 | 1176.7434786759688 | 32768.0 | -| complete | lite | measurement | 1176.3125202894216 | 1176.2004268899861 | 32768.0 | -| complete | lite | measurement | 1026.7121697740579 | 1026.4845228515806 | 32768.0 | -| complete | lite | measurement | 1088.5411560128925 | 1086.1502830021482 | 32768.0 | -| complete | lite | measurement | 1155.7913039929804 | 1148.5600755581706 | 32768.0 | -| complete | lite | measurement | 1105.223596241675 | 1103.7487800220897 | 32768.0 | -| complete | lite | measurement | 1090.184566494866 | 1089.993673026842 | 32768.0 | -| complete | lite | measurement | 1220.3665784702587 | 1220.177052958906 | 32768.0 | -| complete | lite | measurement | 1020.2505980555331 | 1011.7139155221487 | 32768.0 | -| complete | lite | measurement | 1200.437177616643 | 1192.9223605788357 | 32768.0 | -| complete | lite | measurement | 1144.1962602807405 | 1144.1672235785038 | 32768.0 | -| complete | lite | measurement | 1343.6937780440192 | 1343.6825275160002 | 32768.0 | -| complete | lite | measurement | 995.6473479622433 | 995.6188499884095 | 32768.0 | -| complete | lite | measurement | 1126.9057705138587 | 1126.8913612744022 | 32768.0 | -| complete | lite | measurement | 1011.3190177431773 | 1011.2922532425869 | 32768.0 | -| complete | lite | measurement | 1090.829484051278 | 1090.7728713465478 | 32768.0 | -| complete | lite | measurement | 1093.6149015970873 | 1093.5960052372561 | 32768.0 | -| complete | lite | measurement | 1089.1282238365632 | 1089.115074808051 | 32768.0 | -| complete | lite | measurement | 1246.9551997377691 | 1246.9105290435216 | 32768.0 | -| complete | lite | measurement | 1078.4352968457056 | 1078.4350049781121 | 32768.0 | -| complete | lite | measurement | 1261.8833035557673 | 1261.8526493091995 | 32768.0 | -| complete | lite | measurement | 1209.6609784365103 | 1209.6467928697164 | 32768.0 | -| complete | lite | measurement | 1236.4352013735588 | 1236.3985311165945 | 32768.0 | -| complete | lite | measurement | 953.6447661653634 | 953.6460659292751 | 32768.0 | -| complete | lite | measurement | 1086.4710342197236 | 1086.4600755581737 | 32768.0 | -| complete | lite | measurement | 1039.5666030193054 | 1039.5016639162068 | 32768.0 | -| complete | lite | measurement | 939.0857489668688 | 939.0763164713984 | 32768.0 | -| complete | lite | measurement | 970.1934861770001 | 970.1444524760846 | 32768.0 | -| complete | lite | measurement | 1154.792115492083 | 1154.7634743115932 | 32768.0 | -| complete | lite | measurement | 1159.8957024606573 | 1159.8476971126865 | 32768.0 | -| complete | lite | measurement | 903.6874156109146 | 903.6914608366012 | 32768.0 | -| complete | lite | measurement | 958.4452216968043 | 958.0679532466879 | 32768.0 | -| complete | lite | measurement | 1047.5559675953236 | 1047.5075339943564 | 32768.0 | -| complete | lite | measurement | 959.0539340703652 | 959.0388210744615 | 32768.0 | -| complete | lite | measurement | 969.6542177534521 | 969.354573723754 | 32768.0 | -| complete | lite | measurement | 1064.8366320706366 | 1064.819507371692 | 32768.0 | -| complete | lite | measurement | 1057.6566631680837 | 1057.5784400103664 | 32768.0 | -| complete | lite | measurement | 1295.7334338052856 | 1295.7341061905943 | 32768.0 | -| complete | lite | measurement | 1119.6907789042928 | 1119.6672249423816 | 32768.0 | -| complete | lite | measurement | 1024.9618035760132 | 1024.9259216322814 | 32768.0 | -| complete | lite | measurement | 1029.080908608122 | 1029.041412419364 | 32768.0 | -| complete | lite | measurement | 898.9702159000116 | 898.9226292603647 | 32768.0 | -| complete | lite | measurement | 1310.3794329058114 | 1310.3604942649422 | 32768.0 | -| complete | lite | measurement | 1023.9365052325968 | 1023.9197501398014 | 32768.0 | -| complete | lite | measurement | 1211.947838955993 | 1211.9327791492244 | 32768.0 | -| complete | lite | measurement | 1064.8364711349407 | 1064.8139973540879 | 32768.0 | -| complete | lite | measurement | 901.597452297904 | 901.5964512213461 | 32768.0 | -| complete | lite | measurement | 958.2952701133279 | 958.2867895964466 | 32768.0 | -| complete | lite | measurement | 1207.8354646014614 | 1207.819079117858 | 32768.0 | -| complete | lite | measurement | 1329.5655937588165 | 1329.535876488357 | 32768.0 | -| nop | lite | measurement | 38.110806619504665 | 38.1103878926192 | 1.0 | -| nop | lite | measurement | 38.11982587211032 | 38.119949977731416 | 1.0 | -| nop | lite | measurement | 35.60234371277777 | 35.60241076147078 | 1.0 | -| nop | lite | measurement | 35.52463371233377 | 35.52400712354319 | 1.0 | -| nop | lite | measurement | 35.60890345713732 | 35.598908831390666 | 1.0 | -| nop | lite | measurement | 35.56469826472105 | 35.56482291633165 | 1.0 | -| nop | lite | measurement | 35.587525729533816 | 35.58658169680518 | 1.0 | -| nop | lite | measurement | 35.59162558347666 | 35.591650153373905 | 1.0 | -| nop | lite | measurement | 35.60446562969847 | 35.604553426382886 | 1.0 | -| nop | lite | measurement | 35.54877823649765 | 35.548899666793055 | 1.0 | -| nop | lite | measurement | 35.55656571477951 | 35.55600808581229 | 1.0 | -| nop | lite | measurement | 35.55250075029243 | 35.551779212311956 | 1.0 | -| nop | lite | measurement | 35.589655344987555 | 35.5886050608781 | 1.0 | -| nop | lite | measurement | 35.64580476279574 | 35.64589305086534 | 1.0 | -| nop | lite | measurement | 35.553413005948464 | 35.55355906071306 | 1.0 | -| nop | lite | measurement | 35.58651017091633 | 35.58656859282283 | 1.0 | -| nop | lite | measurement | 35.599918493220784 | 35.599511614578184 | 1.0 | -| nop | lite | measurement | 35.703484015827875 | 35.702958709952256 | 1.0 | -| nop | lite | measurement | 35.893405000517724 | 35.793778425745394 | 1.0 | -| nop | lite | measurement | 35.81522980865446 | 35.81531323732079 | 1.0 | -| nop | lite | measurement | 35.84754892460622 | 35.84723661309451 | 1.0 | -| nop | lite | measurement | 35.844696788295025 | 35.84365141812669 | 1.0 | -| nop | lite | measurement | 36.199621371308744 | 35.909685824925454 | 1.0 | -| nop | lite | measurement | 35.80679553968121 | 35.79414806724712 | 1.0 | -| nop | lite | measurement | 35.74602500250775 | 35.74449800636577 | 1.0 | -| nop | lite | measurement | 35.66693449752423 | 35.667047901552834 | 1.0 | -| nop | lite | measurement | 35.598739025619565 | 35.59882054330985 | 1.0 | -| nop | lite | measurement | 35.5881571230598 | 35.58826626833473 | 1.0 | -| nop | lite | measurement | 35.61087014644528 | 35.610933809983365 | 1.0 | -| nop | lite | measurement | 35.57156906487411 | 35.571136633422135 | 1.0 | -| nop | lite | measurement | 35.57613039728185 | 35.5761895290117 | 1.0 | -| nop | lite | measurement | 35.60136408087952 | 35.60148944231259 | 1.0 | -| nop | lite | measurement | 35.56451884932358 | 35.5646368943824 | 1.0 | -| nop | lite | measurement | 35.56032060641034 | 35.56036854513535 | 1.0 | -| nop | lite | measurement | 35.592723587856014 | 35.59245162569375 | 1.0 | -| nop | lite | measurement | 35.618258663137574 | 35.575754312998356 | 1.0 | -| nop | lite | measurement | 35.58992517790013 | 35.589981361223046 | 1.0 | -| nop | lite | measurement | 35.594627323593166 | 35.594772613965524 | 1.0 | -| nop | lite | measurement | 35.560348670724025 | 35.56044809722792 | 1.0 | -| nop | lite | measurement | 35.522294651493006 | 35.522377861739045 | 1.0 | -| nop | lite | measurement | 35.56033387412664 | 35.560444056833425 | 1.0 | -| nop | lite | measurement | 35.52965892577639 | 35.52885406821049 | 1.0 | -| nop | lite | measurement | 35.56817109298652 | 35.56825353890782 | 1.0 | -| nop | lite | measurement | 35.65557148848413 | 35.65312721351518 | 1.0 | -| nop | lite | measurement | 35.796585189589536 | 35.796566843987144 | 1.0 | -| nop | lite | measurement | 35.72708166725118 | 35.72728647156318 | 1.0 | -| nop | lite | measurement | 35.83322894731306 | 35.83339340235235 | 1.0 | -| nop | lite | measurement | 35.55572160000488 | 35.55532662413073 | 1.0 | -| nop | lite | measurement | 35.591038088224536 | 35.591208549168925 | 1.0 | -| nop | lite | measurement | 35.59113746013219 | 35.590686901472296 | 1.0 | -| nop | lite | measurement | 32.15815746538057 | 32.157640515155286 | 32.0 | -| nop | lite | measurement | 32.01621556424229 | 32.01571680258658 | 32.0 | -| nop | lite | measurement | 32.0283739304795 | 32.02792233960789 | 32.0 | -| nop | lite | measurement | 32.05420458513627 | 32.05432570445549 | 32.0 | -| nop | lite | measurement | 32.03679845075908 | 32.03689164420103 | 32.0 | -| nop | lite | measurement | 32.03965387165498 | 32.03981214887703 | 32.0 | -| nop | lite | measurement | 32.04951533454708 | 32.04947606371195 | 32.0 | -| nop | lite | measurement | 32.03388639736518 | 32.033269961191245 | 32.0 | -| nop | lite | measurement | 32.0627294182118 | 32.061882455761264 | 32.0 | -| nop | lite | measurement | 32.02344184410205 | 32.02230037384856 | 32.0 | -| nop | lite | measurement | 32.05258952328848 | 32.05262236379488 | 32.0 | -| nop | lite | measurement | 32.04441596838553 | 32.04444476698029 | 32.0 | -| nop | lite | measurement | 32.038449430424116 | 32.038554887345335 | 32.0 | -| nop | lite | measurement | 32.071815570519604 | 32.07191202503869 | 32.0 | -| nop | lite | measurement | 32.176388653263515 | 32.17604669904645 | 32.0 | -| nop | lite | measurement | 32.02859536240377 | 32.02869875245737 | 32.0 | -| nop | lite | measurement | 32.0670492940753 | 32.06713086699465 | 32.0 | -| nop | lite | measurement | 32.077792948182825 | 32.07795834469721 | 32.0 | -| nop | lite | measurement | 32.07947745720951 | 32.078769848615195 | 32.0 | -| nop | lite | measurement | 32.095405127710414 | 32.094862318540414 | 32.0 | -| nop | lite | measurement | 32.064849626054404 | 32.0645349087693 | 32.0 | -| nop | lite | measurement | 32.01090179358638 | 32.010945749300255 | 32.0 | -| nop | lite | measurement | 32.092971121265684 | 32.09303243875307 | 32.0 | -| nop | lite | measurement | 32.00502523393077 | 32.0050775948947 | 32.0 | -| nop | lite | measurement | 32.04682517233799 | 32.046436047308774 | 32.0 | -| nop | lite | measurement | 32.048628320469334 | 32.04833771680788 | 32.0 | -| nop | lite | measurement | 32.028052691054484 | 32.02821946541969 | 32.0 | -| nop | lite | measurement | 32.045869905309154 | 32.045007601993106 | 32.0 | -| nop | lite | measurement | 32.01345062694742 | 32.013556818731786 | 32.0 | -| nop | lite | measurement | 32.04937671556183 | 32.049362477047346 | 32.0 | + +| Library | Implementation | Wall Nanoseconds | CPU Nanoseconds | num agents | +|-|-|-|-|-| +| arithmetic | vanilla | 760.19 | 749.88 | 1 | +| arithmetic | vanilla | 768.35 | 754.18 | 1 | +| arithmetic | vanilla | 768.87 | 755.16 | 1 | +| arithmetic | vanilla | 757.27 | 748.97 | 1 | +| arithmetic | vanilla | 759.50 | 746.07 | 1 | +| arithmetic | vanilla | 759.35 | 747.11 | 1 | +| arithmetic | vanilla | 759.50 | 746.07 | 1 | +| arithmetic | vanilla | 752.69 | 751.92 | 1 | +| arithmetic | vanilla | 741.52 | 741.52 | 1 | +| arithmetic | vanilla | 745.24 | 745.22 | 1 | +| arithmetic | vanilla | 740.75 | 740.74 | 1 | +| arithmetic | vanilla | 747.54 | 747.54 | 1 | +| arithmetic | vanilla | 745.73 | 745.74 | 1 | +| arithmetic | vanilla | 742.21 | 742.22 | 1 | +| arithmetic | vanilla | 741.18 | 741.17 | 1 | +| arithmetic | vanilla | 742.07 | 742.07 | 1 | +| arithmetic | vanilla | 746.56 | 746.56 | 1 | +| arithmetic | vanilla | 738.83 | 738.82 | 1 | +| arithmetic | vanilla | 776.20 | 776.18 | 1 | +| arithmetic | vanilla | 764.06 | 764.04 | 1 | +| arithmetic | vanilla | 769.96 | 769.93 | 1 | +| arithmetic | vanilla | 760.74 | 760.74 | 1 | +| arithmetic | vanilla | 761.06 | 761.06 | 1 | +| arithmetic | vanilla | 773.91 | 773.91 | 1 | +| arithmetic | vanilla | 779.18 | 779.18 | 1 | +| arithmetic | vanilla | 743.94 | 743.94 | 1 | +| arithmetic | vanilla | 748.39 | 748.39 | 1 | +| arithmetic | vanilla | 763.53 | 763.52 | 1 | +| arithmetic | vanilla | 750.32 | 750.30 | 1 | +| arithmetic | vanilla | 741.52 | 741.51 | 1 | +| arithmetic | vanilla | 773.12 | 773.12 | 1 | +| arithmetic | vanilla | 747.61 | 747.61 | 1 | +| arithmetic | vanilla | 750.60 | 750.60 | 1 | +| arithmetic | vanilla | 758.21 | 758.20 | 1 | +| arithmetic | vanilla | 756.08 | 756.07 | 1 | +| arithmetic | vanilla | 760.37 | 760.35 | 1 | +| arithmetic | vanilla | 765.80 | 765.78 | 1 | +| arithmetic | vanilla | 760.50 | 760.50 | 1 | +| arithmetic | vanilla | 760.77 | 760.43 | 1 | +| arithmetic | vanilla | 758.77 | 758.76 | 1 | +| arithmetic | vanilla | 765.48 | 765.48 | 1 | +| arithmetic | vanilla | 784.83 | 784.83 | 1 | +| arithmetic | vanilla | 765.47 | 765.48 | 1 | +| arithmetic | vanilla | 755.47 | 755.47 | 1 | +| arithmetic | vanilla | 781.90 | 781.89 | 1 | +| arithmetic | vanilla | 753.27 | 753.24 | 1 | +| arithmetic | vanilla | 751.04 | 751.03 | 1 | +| arithmetic | vanilla | 751.33 | 751.26 | 1 | +| arithmetic | vanilla | 749.13 | 749.13 | 1 | +| arithmetic | vanilla | 751.68 | 751.68 | 1 | +| arithmetic | vanilla | 799.42 | 799.34 | 32 | +| arithmetic | vanilla | 792.84 | 792.83 | 32 | +| arithmetic | vanilla | 807.18 | 807.17 | 32 | +| arithmetic | vanilla | 780.16 | 780.15 | 32 | +| arithmetic | vanilla | 777.25 | 777.25 | 32 | +| arithmetic | vanilla | 757.24 | 757.23 | 32 | +| arithmetic | vanilla | 801.78 | 801.72 | 32 | +| arithmetic | vanilla | 801.99 | 801.99 | 32 | +| arithmetic | vanilla | 771.23 | 771.23 | 32 | +| arithmetic | vanilla | 789.31 | 789.21 | 32 | +| arithmetic | vanilla | 813.77 | 810.35 | 32 | +| arithmetic | vanilla | 790.63 | 787.39 | 32 | +| arithmetic | vanilla | 773.45 | 772.57 | 32 | +| arithmetic | vanilla | 775.79 | 775.80 | 32 | +| arithmetic | vanilla | 769.40 | 769.40 | 32 | +| arithmetic | vanilla | 797.99 | 791.57 | 32 | +| arithmetic | vanilla | 779.22 | 779.22 | 32 | +| arithmetic | vanilla | 789.42 | 789.38 | 32 | +| arithmetic | vanilla | 851.14 | 839.15 | 32 | +| arithmetic | vanilla | 822.59 | 807.25 | 32 | +| arithmetic | vanilla | 827.15 | 812.93 | 32 | +| arithmetic | vanilla | 815.08 | 798.61 | 32 | +| arithmetic | vanilla | 827.47 | 811.93 | 32 | +| arithmetic | vanilla | 810.20 | 799.03 | 32 | +| arithmetic | vanilla | 799.69 | 797.19 | 32 | +| arithmetic | vanilla | 790.41 | 790.37 | 32 | +| arithmetic | vanilla | 797.15 | 797.11 | 32 | +| arithmetic | vanilla | 813.77 | 813.63 | 32 | +| arithmetic | vanilla | 841.25 | 841.21 | 32 | +| arithmetic | vanilla | 835.91 | 832.54 | 32 | +| arithmetic | vanilla | 834.34 | 828.55 | 32 | +| arithmetic | vanilla | 822.83 | 818.04 | 32 | +| arithmetic | vanilla | 817.66 | 813.09 | 32 | +| arithmetic | vanilla | 815.65 | 810.53 | 32 | +| arithmetic | vanilla | 823.80 | 815.10 | 32 | +| arithmetic | vanilla | 787.88 | 787.65 | 32 | +| arithmetic | vanilla | 782.20 | 782.20 | 32 | +| arithmetic | vanilla | 781.27 | 781.28 | 32 | +| arithmetic | vanilla | 788.72 | 788.72 | 32 | +| arithmetic | vanilla | 810.90 | 810.91 | 32 | +| arithmetic | vanilla | 818.32 | 818.30 | 32 | +| arithmetic | vanilla | 787.87 | 787.86 | 32 | +| arithmetic | vanilla | 802.66 | 802.60 | 32 | +| arithmetic | vanilla | 809.53 | 809.54 | 32 | +| arithmetic | vanilla | 815.69 | 815.70 | 32 | +| arithmetic | vanilla | 802.44 | 802.44 | 32 | +| arithmetic | vanilla | 831.92 | 831.90 | 32 | +| arithmetic | vanilla | 815.76 | 815.24 | 32 | +| arithmetic | vanilla | 798.05 | 798.04 | 32 | +| arithmetic | vanilla | 801.61 | 801.61 | 32 | +| arithmetic | vanilla | 1609.17 | 1609.12 | 1024 | +| arithmetic | vanilla | 1690.62 | 1690.05 | 1024 | +| arithmetic | vanilla | 1611.38 | 1609.01 | 1024 | +| arithmetic | vanilla | 1671.34 | 1670.96 | 1024 | +| arithmetic | vanilla | 1737.05 | 1730.47 | 1024 | +| arithmetic | vanilla | 1766.97 | 1766.91 | 1024 | +| arithmetic | vanilla | 1588.26 | 1588.24 | 1024 | +| arithmetic | vanilla | 1682.21 | 1680.91 | 1024 | +| arithmetic | vanilla | 1643.24 | 1643.23 | 1024 | +| arithmetic | vanilla | 1703.66 | 1703.64 | 1024 | +| arithmetic | vanilla | 1665.91 | 1665.91 | 1024 | +| arithmetic | vanilla | 1658.93 | 1653.53 | 1024 | +| arithmetic | vanilla | 1623.61 | 1616.73 | 1024 | +| arithmetic | vanilla | 1653.10 | 1653.05 | 1024 | +| arithmetic | vanilla | 1644.47 | 1644.47 | 1024 | +| arithmetic | vanilla | 1682.15 | 1682.15 | 1024 | +| arithmetic | vanilla | 1719.64 | 1719.64 | 1024 | +| arithmetic | vanilla | 1681.78 | 1681.57 | 1024 | +| arithmetic | vanilla | 1613.64 | 1613.57 | 1024 | +| arithmetic | vanilla | 1609.36 | 1608.98 | 1024 | +| arithmetic | vanilla | 1656.75 | 1656.65 | 1024 | +| arithmetic | vanilla | 1681.59 | 1681.59 | 1024 | +| arithmetic | vanilla | 1710.99 | 1710.96 | 1024 | +| arithmetic | vanilla | 1669.02 | 1669.02 | 1024 | +| arithmetic | vanilla | 1638.71 | 1638.70 | 1024 | +| arithmetic | vanilla | 1691.14 | 1691.14 | 1024 | +| arithmetic | vanilla | 1728.32 | 1728.32 | 1024 | +| arithmetic | vanilla | 1661.15 | 1661.14 | 1024 | +| arithmetic | vanilla | 1622.40 | 1622.31 | 1024 | +| arithmetic | vanilla | 1648.83 | 1648.83 | 1024 | +| arithmetic | vanilla | 1627.84 | 1625.57 | 1024 | +| arithmetic | vanilla | 1707.33 | 1707.30 | 1024 | +| arithmetic | vanilla | 1660.42 | 1658.64 | 1024 | +| arithmetic | vanilla | 1616.96 | 1616.91 | 1024 | +| arithmetic | vanilla | 1633.51 | 1633.49 | 1024 | +| arithmetic | vanilla | 1594.59 | 1594.57 | 1024 | +| arithmetic | vanilla | 1615.70 | 1615.70 | 1024 | +| arithmetic | vanilla | 1668.26 | 1668.26 | 1024 | +| arithmetic | vanilla | 1659.44 | 1659.43 | 1024 | +| arithmetic | vanilla | 1609.23 | 1609.21 | 1024 | +| arithmetic | vanilla | 1671.21 | 1670.36 | 1024 | +| arithmetic | vanilla | 1625.10 | 1625.09 | 1024 | +| arithmetic | vanilla | 1631.90 | 1631.87 | 1024 | +| arithmetic | vanilla | 1608.69 | 1608.69 | 1024 | +| arithmetic | vanilla | 1622.32 | 1617.40 | 1024 | +| arithmetic | vanilla | 1632.61 | 1627.07 | 1024 | +| arithmetic | vanilla | 1580.68 | 1580.00 | 1024 | +| arithmetic | vanilla | 1686.28 | 1686.28 | 1024 | +| arithmetic | vanilla | 1673.45 | 1646.51 | 1024 | +| arithmetic | vanilla | 1650.37 | 1613.36 | 1024 | +| arithmetic | vanilla | 1787.28 | 1778.42 | 32768 | +| arithmetic | vanilla | 1745.06 | 1712.71 | 32768 | +| arithmetic | vanilla | 1814.96 | 1782.09 | 32768 | +| arithmetic | vanilla | 1940.58 | 1939.85 | 32768 | +| arithmetic | vanilla | 1669.27 | 1669.23 | 32768 | +| arithmetic | vanilla | 1701.11 | 1700.71 | 32768 | +| arithmetic | vanilla | 1619.67 | 1619.66 | 32768 | +| arithmetic | vanilla | 1601.34 | 1601.34 | 32768 | +| arithmetic | vanilla | 1615.92 | 1615.92 | 32768 | +| arithmetic | vanilla | 1704.98 | 1704.84 | 32768 | +| arithmetic | vanilla | 1679.23 | 1679.16 | 32768 | +| arithmetic | vanilla | 1642.93 | 1642.89 | 32768 | +| arithmetic | vanilla | 1614.52 | 1614.52 | 32768 | +| arithmetic | vanilla | 1627.87 | 1627.83 | 32768 | +| arithmetic | vanilla | 1679.48 | 1679.47 | 32768 | +| arithmetic | vanilla | 1626.59 | 1626.58 | 32768 | +| arithmetic | vanilla | 1687.02 | 1686.90 | 32768 | +| arithmetic | vanilla | 1679.43 | 1679.43 | 32768 | +| arithmetic | vanilla | 1640.85 | 1640.81 | 32768 | +| arithmetic | vanilla | 1729.68 | 1728.72 | 32768 | +| arithmetic | vanilla | 1714.43 | 1714.40 | 32768 | +| arithmetic | vanilla | 1621.67 | 1621.66 | 32768 | +| arithmetic | vanilla | 1595.25 | 1595.25 | 32768 | +| arithmetic | vanilla | 1631.16 | 1631.16 | 32768 | +| arithmetic | vanilla | 1652.02 | 1651.99 | 32768 | +| arithmetic | vanilla | 1617.54 | 1617.50 | 32768 | +| arithmetic | vanilla | 1704.85 | 1702.74 | 32768 | +| arithmetic | vanilla | 1703.76 | 1693.17 | 32768 | +| arithmetic | vanilla | 1754.77 | 1754.34 | 32768 | +| arithmetic | vanilla | 1678.26 | 1639.86 | 32768 | +| arithmetic | vanilla | 1662.27 | 1634.36 | 32768 | +| arithmetic | vanilla | 1655.22 | 1629.15 | 32768 | +| arithmetic | vanilla | 1665.78 | 1636.26 | 32768 | +| arithmetic | vanilla | 1680.62 | 1648.38 | 32768 | +| arithmetic | vanilla | 1752.40 | 1728.71 | 32768 | +| arithmetic | vanilla | 1669.39 | 1669.34 | 32768 | +| arithmetic | vanilla | 1676.24 | 1672.00 | 32768 | +| arithmetic | vanilla | 1634.84 | 1634.81 | 32768 | +| arithmetic | vanilla | 1687.72 | 1687.67 | 32768 | +| arithmetic | vanilla | 1650.51 | 1650.42 | 32768 | +| arithmetic | vanilla | 1668.15 | 1668.15 | 32768 | +| arithmetic | vanilla | 1658.72 | 1658.60 | 32768 | +| arithmetic | vanilla | 1647.36 | 1647.37 | 32768 | +| arithmetic | vanilla | 1638.67 | 1638.59 | 32768 | +| arithmetic | vanilla | 1688.80 | 1688.80 | 32768 | +| arithmetic | vanilla | 1634.55 | 1634.56 | 32768 | +| arithmetic | vanilla | 1727.82 | 1727.69 | 32768 | +| arithmetic | vanilla | 1636.13 | 1636.13 | 32768 | +| arithmetic | vanilla | 1661.66 | 1661.57 | 32768 | +| arithmetic | vanilla | 1712.89 | 1691.95 | 32768 | +| complete | vanilla | 22792.39 | 22759.79 | 1 | +| complete | vanilla | 23203.62 | 23118.27 | 1 | +| complete | vanilla | 24437.95 | 24188.15 | 1 | +| complete | vanilla | 24410.70 | 24330.48 | 1 | +| complete | vanilla | 24124.18 | 24020.80 | 1 | +| complete | vanilla | 24155.33 | 23968.30 | 1 | +| complete | vanilla | 24780.18 | 24444.83 | 1 | +| complete | vanilla | 25548.30 | 25401.63 | 1 | +| complete | vanilla | 23486.37 | 23340.78 | 1 | +| complete | vanilla | 23760.35 | 23582.12 | 1 | +| complete | vanilla | 24136.77 | 23936.80 | 1 | +| complete | vanilla | 25552.98 | 25415.72 | 1 | +| complete | vanilla | 25193.67 | 24936.47 | 1 | +| complete | vanilla | 24901.96 | 24608.72 | 1 | +| complete | vanilla | 23956.94 | 23732.90 | 1 | +| complete | vanilla | 25029.21 | 24858.61 | 1 | +| complete | vanilla | 24957.04 | 24856.43 | 1 | +| complete | vanilla | 24399.10 | 24123.83 | 1 | +| complete | vanilla | 24196.05 | 23899.29 | 1 | +| complete | vanilla | 25542.33 | 25512.70 | 1 | +| complete | vanilla | 35583.70 | 35111.39 | 32 | +| complete | vanilla | 31072.12 | 30814.79 | 32 | +| complete | vanilla | 34756.04 | 34397.74 | 32 | +| complete | vanilla | 32993.73 | 32660.28 | 32 | +| complete | vanilla | 29274.59 | 29038.10 | 32 | +| complete | vanilla | 30905.56 | 30728.66 | 32 | +| complete | vanilla | 31759.00 | 31520.90 | 32 | +| complete | vanilla | 33703.31 | 33402.32 | 32 | +| complete | vanilla | 30585.90 | 30258.02 | 32 | +| complete | vanilla | 34651.71 | 34358.01 | 32 | +| complete | vanilla | 33578.90 | 33394.27 | 32 | +| complete | vanilla | 32262.49 | 32103.10 | 32 | +| complete | vanilla | 31068.13 | 30884.80 | 32 | +| complete | vanilla | 35098.88 | 34734.12 | 32 | +| complete | vanilla | 31326.04 | 31265.81 | 32 | +| complete | vanilla | 29679.55 | 29667.76 | 32 | +| complete | vanilla | 34160.38 | 33780.28 | 32 | +| complete | vanilla | 32381.72 | 32345.97 | 32 | +| complete | vanilla | 31777.50 | 31541.14 | 32 | +| complete | vanilla | 31529.37 | 31491.00 | 32 | +| complete | vanilla | 30595.67 | 30589.46 | 1024 | +| complete | vanilla | 29877.09 | 29468.47 | 1024 | +| complete | vanilla | 34749.25 | 34318.38 | 1024 | +| complete | vanilla | 32068.20 | 31617.81 | 1024 | +| complete | vanilla | 30824.03 | 30821.11 | 1024 | +| complete | vanilla | 30170.31 | 30089.37 | 1024 | +| complete | vanilla | 29264.82 | 29198.94 | 1024 | +| complete | vanilla | 30249.13 | 30214.93 | 1024 | +| complete | vanilla | 30166.86 | 30166.83 | 1024 | +| complete | vanilla | 31835.44 | 31834.66 | 1024 | +| complete | vanilla | 33134.30 | 33134.29 | 1024 | +| complete | vanilla | 32381.86 | 32343.31 | 1024 | +| complete | vanilla | 30340.41 | 30079.61 | 1024 | +| complete | vanilla | 32355.06 | 32126.01 | 1024 | +| complete | vanilla | 34694.44 | 34191.56 | 1024 | +| complete | vanilla | 36885.53 | 36467.55 | 1024 | +| complete | vanilla | 35027.90 | 34789.83 | 1024 | +| complete | vanilla | 33587.63 | 33326.76 | 1024 | +| complete | vanilla | 37003.57 | 36587.17 | 1024 | +| complete | vanilla | 32903.23 | 32439.15 | 1024 | +| complete | vanilla | 40668.84 | 40668.25 | 32768 | +| complete | vanilla | 37402.74 | 37402.17 | 32768 | +| complete | vanilla | 46248.95 | 46196.18 | 32768 | +| complete | vanilla | 40634.62 | 39885.51 | 32768 | +| complete | vanilla | 39601.10 | 38905.12 | 32768 | +| complete | vanilla | 38538.96 | 37783.22 | 32768 | +| complete | vanilla | 38794.17 | 38218.78 | 32768 | +| complete | vanilla | 38254.81 | 37851.93 | 32768 | +| complete | vanilla | 36349.80 | 36349.75 | 32768 | +| complete | vanilla | 37353.11 | 37353.03 | 32768 | +| complete | vanilla | 37442.02 | 36854.76 | 32768 | +| complete | vanilla | 37787.89 | 37014.78 | 32768 | +| complete | vanilla | 38519.35 | 37777.28 | 32768 | +| complete | vanilla | 37983.14 | 37299.06 | 32768 | +| complete | vanilla | 39508.65 | 38880.46 | 32768 | +| complete | vanilla | 38163.34 | 37575.59 | 32768 | +| complete | vanilla | 37972.43 | 37299.13 | 32768 | +| complete | vanilla | 41130.09 | 40588.81 | 32768 | +| complete | vanilla | 38845.03 | 38391.07 | 32768 | +| complete | vanilla | 37005.04 | 37005.05 | 32768 | +| nop | vanilla | 303.52 | 301.90 | 1 | +| nop | vanilla | 328.01 | 327.21 | 1 | +| nop | vanilla | 314.50 | 312.63 | 1 | +| nop | vanilla | 313.99 | 309.94 | 1 | +| nop | vanilla | 310.24 | 306.25 | 1 | +| nop | vanilla | 312.32 | 308.06 | 1 | +| nop | vanilla | 309.92 | 305.78 | 1 | +| nop | vanilla | 311.39 | 307.72 | 1 | +| nop | vanilla | 311.69 | 307.99 | 1 | +| nop | vanilla | 310.43 | 305.72 | 1 | +| nop | vanilla | 310.99 | 305.76 | 1 | +| nop | vanilla | 310.44 | 306.11 | 1 | +| nop | vanilla | 308.91 | 304.43 | 1 | +| nop | vanilla | 318.68 | 314.04 | 1 | +| nop | vanilla | 312.30 | 307.24 | 1 | +| nop | vanilla | 310.81 | 307.19 | 1 | +| nop | vanilla | 312.34 | 307.74 | 1 | +| nop | vanilla | 313.65 | 308.08 | 1 | +| nop | vanilla | 310.85 | 305.64 | 1 | +| nop | vanilla | 309.23 | 305.25 | 1 | +| nop | vanilla | 330.75 | 326.61 | 32 | +| nop | vanilla | 332.66 | 328.22 | 32 | +| nop | vanilla | 332.90 | 327.83 | 32 | +| nop | vanilla | 333.40 | 329.06 | 32 | +| nop | vanilla | 333.46 | 327.44 | 32 | +| nop | vanilla | 334.44 | 328.47 | 32 | +| nop | vanilla | 332.10 | 329.81 | 32 | +| nop | vanilla | 326.89 | 326.89 | 32 | +| nop | vanilla | 325.05 | 325.05 | 32 | +| nop | vanilla | 327.66 | 327.66 | 32 | +| nop | vanilla | 328.53 | 327.30 | 32 | +| nop | vanilla | 328.30 | 328.30 | 32 | +| nop | vanilla | 328.44 | 328.44 | 32 | +| nop | vanilla | 329.51 | 329.51 | 32 | +| nop | vanilla | 324.31 | 324.30 | 32 | +| nop | vanilla | 330.28 | 330.27 | 32 | +| nop | vanilla | 330.20 | 330.20 | 32 | +| nop | vanilla | 326.90 | 326.89 | 32 | +| nop | vanilla | 336.34 | 336.34 | 32 | +| nop | vanilla | 348.24 | 348.21 | 32 | +| nop | vanilla | 961.18 | 961.13 | 1024 | +| nop | vanilla | 932.15 | 932.13 | 1024 | +| nop | vanilla | 985.26 | 985.26 | 1024 | +| nop | vanilla | 957.85 | 957.84 | 1024 | +| nop | vanilla | 942.41 | 928.07 | 1024 | +| nop | vanilla | 906.35 | 890.08 | 1024 | +| nop | vanilla | 888.76 | 884.23 | 1024 | +| nop | vanilla | 940.81 | 940.79 | 1024 | +| nop | vanilla | 859.19 | 859.19 | 1024 | +| nop | vanilla | 837.66 | 837.66 | 1024 | +| nop | vanilla | 848.95 | 848.95 | 1024 | +| nop | vanilla | 845.00 | 844.98 | 1024 | +| nop | vanilla | 895.59 | 895.57 | 1024 | +| nop | vanilla | 851.09 | 851.07 | 1024 | +| nop | vanilla | 858.08 | 858.08 | 1024 | +| nop | vanilla | 849.78 | 849.78 | 1024 | +| nop | vanilla | 867.20 | 867.20 | 1024 | +| nop | vanilla | 904.93 | 904.90 | 1024 | +| nop | vanilla | 807.05 | 806.04 | 1024 | +| nop | vanilla | 971.06 | 967.02 | 1024 | +| nop | vanilla | 1112.17 | 1111.97 | 32768 | +| nop | vanilla | 1082.29 | 1082.27 | 32768 | +| nop | vanilla | 1107.78 | 1107.65 | 32768 | +| nop | vanilla | 1056.07 | 1056.07 | 32768 | +| nop | vanilla | 1070.70 | 1070.69 | 32768 | +| nop | vanilla | 1070.97 | 1070.95 | 32768 | +| nop | vanilla | 1034.78 | 1034.74 | 32768 | +| nop | vanilla | 1062.35 | 1062.35 | 32768 | +| nop | vanilla | 1001.25 | 997.63 | 32768 | +| nop | vanilla | 1004.38 | 1004.36 | 32768 | +| nop | vanilla | 1042.76 | 1042.76 | 32768 | +| nop | vanilla | 973.33 | 973.20 | 32768 | +| nop | vanilla | 1021.51 | 1021.51 | 32768 | +| nop | vanilla | 1004.24 | 1004.24 | 32768 | +| nop | vanilla | 1129.32 | 1129.27 | 32768 | +| nop | vanilla | 1071.88 | 1071.88 | 32768 | +| nop | vanilla | 1044.98 | 1044.98 | 32768 | +| nop | vanilla | 1075.35 | 1075.33 | 32768 | +| nop | vanilla | 998.79 | 998.76 | 32768 | +| nop | vanilla | 970.16 | 970.16 | 32768 | +| sans_regulation | vanilla | 23738.83 | 23687.11 | 1 | +| sans_regulation | vanilla | 29138.34 | 28867.22 | 1 | +| sans_regulation | vanilla | 23012.36 | 23011.81 | 1 | +| sans_regulation | vanilla | 22879.23 | 22879.28 | 1 | +| sans_regulation | vanilla | 21606.44 | 21606.06 | 1 | +| sans_regulation | vanilla | 24315.65 | 24189.47 | 1 | +| sans_regulation | vanilla | 24290.39 | 24256.59 | 1 | +| sans_regulation | vanilla | 22075.37 | 22074.20 | 1 | +| sans_regulation | vanilla | 24206.39 | 24205.97 | 1 | +| sans_regulation | vanilla | 23650.88 | 23650.94 | 1 | +| sans_regulation | vanilla | 21503.51 | 21503.29 | 1 | +| sans_regulation | vanilla | 22603.93 | 22556.04 | 1 | +| sans_regulation | vanilla | 22832.25 | 22832.31 | 1 | +| sans_regulation | vanilla | 21128.39 | 21128.38 | 1 | +| sans_regulation | vanilla | 21393.67 | 21393.72 | 1 | +| sans_regulation | vanilla | 23769.54 | 23769.58 | 1 | +| sans_regulation | vanilla | 21918.97 | 21918.47 | 1 | +| sans_regulation | vanilla | 21961.28 | 21960.96 | 1 | +| sans_regulation | vanilla | 24999.87 | 24999.54 | 1 | +| sans_regulation | vanilla | 22841.80 | 22807.61 | 1 | +| sans_regulation | vanilla | 30745.11 | 30744.65 | 32 | +| sans_regulation | vanilla | 30997.85 | 30997.52 | 32 | +| sans_regulation | vanilla | 26773.27 | 26772.85 | 32 | +| sans_regulation | vanilla | 26852.47 | 26780.96 | 32 | +| sans_regulation | vanilla | 28659.17 | 28658.56 | 32 | +| sans_regulation | vanilla | 28455.67 | 28433.84 | 32 | +| sans_regulation | vanilla | 30140.91 | 30125.24 | 32 | +| sans_regulation | vanilla | 31165.90 | 31114.38 | 32 | +| sans_regulation | vanilla | 31729.55 | 31530.98 | 32 | +| sans_regulation | vanilla | 27850.57 | 27517.93 | 32 | +| sans_regulation | vanilla | 27863.73 | 27490.69 | 32 | +| sans_regulation | vanilla | 29210.21 | 28824.14 | 32 | +| sans_regulation | vanilla | 31009.49 | 30626.25 | 32 | +| sans_regulation | vanilla | 31467.94 | 30930.42 | 32 | +| sans_regulation | vanilla | 31278.67 | 31062.19 | 32 | +| sans_regulation | vanilla | 27853.75 | 27853.73 | 32 | +| sans_regulation | vanilla | 27778.20 | 27775.60 | 32 | +| sans_regulation | vanilla | 32296.03 | 32295.63 | 32 | +| sans_regulation | vanilla | 31422.35 | 31360.82 | 32 | +| sans_regulation | vanilla | 28538.44 | 28536.98 | 32 | +| sans_regulation | vanilla | 30988.46 | 30987.94 | 1024 | +| sans_regulation | vanilla | 30206.44 | 30206.39 | 1024 | +| sans_regulation | vanilla | 31261.62 | 31258.95 | 1024 | +| sans_regulation | vanilla | 30122.97 | 30122.03 | 1024 | +| sans_regulation | vanilla | 35259.54 | 35259.21 | 1024 | +| sans_regulation | vanilla | 30799.63 | 30796.55 | 1024 | +| sans_regulation | vanilla | 29496.77 | 29495.44 | 1024 | +| sans_regulation | vanilla | 31268.15 | 31266.79 | 1024 | +| sans_regulation | vanilla | 32659.25 | 32658.48 | 1024 | +| sans_regulation | vanilla | 29186.15 | 29185.87 | 1024 | +| sans_regulation | vanilla | 31275.83 | 31275.84 | 1024 | +| sans_regulation | vanilla | 27903.06 | 27902.45 | 1024 | +| sans_regulation | vanilla | 32888.62 | 32887.92 | 1024 | +| sans_regulation | vanilla | 29790.59 | 29788.20 | 1024 | +| sans_regulation | vanilla | 33735.68 | 33735.26 | 1024 | +| sans_regulation | vanilla | 35563.13 | 35560.50 | 1024 | +| sans_regulation | vanilla | 30951.49 | 30758.96 | 1024 | +| sans_regulation | vanilla | 34846.64 | 34189.19 | 1024 | +| sans_regulation | vanilla | 33492.69 | 32931.72 | 1024 | +| sans_regulation | vanilla | 32136.12 | 31705.89 | 1024 | +| sans_regulation | vanilla | 36815.20 | 36814.18 | 32768 | +| sans_regulation | vanilla | 37040.28 | 37040.17 | 32768 | +| sans_regulation | vanilla | 36282.99 | 36282.96 | 32768 | +| sans_regulation | vanilla | 38338.96 | 38337.87 | 32768 | +| sans_regulation | vanilla | 35984.65 | 35983.68 | 32768 | +| sans_regulation | vanilla | 35774.73 | 35774.66 | 32768 | +| sans_regulation | vanilla | 36623.65 | 36623.81 | 32768 | +| sans_regulation | vanilla | 34905.90 | 34904.35 | 32768 | +| sans_regulation | vanilla | 33155.05 | 33155.13 | 32768 | +| sans_regulation | vanilla | 36185.56 | 36184.49 | 32768 | +| sans_regulation | vanilla | 35980.15 | 35968.70 | 32768 | +| sans_regulation | vanilla | 35395.24 | 35395.27 | 32768 | +| sans_regulation | vanilla | 35854.08 | 35669.65 | 32768 | +| sans_regulation | vanilla | 37071.69 | 36384.46 | 32768 | +| sans_regulation | vanilla | 37510.02 | 36879.27 | 32768 | +| sans_regulation | vanilla | 36601.63 | 36601.42 | 32768 | +| sans_regulation | vanilla | 36582.21 | 36580.73 | 32768 | +| sans_regulation | vanilla | 36695.07 | 36695.07 | 32768 | +| sans_regulation | vanilla | 35868.59 | 35867.27 | 32768 | +| sans_regulation | vanilla | 35831.21 | 35830.75 | 32768 | +| control | vanilla | 0.00 | 0.00 | 1 | +| control | vanilla | 0.00 | 0.00 | 1 | +| control | vanilla | 0.00 | 0.00 | 1 | +| control | vanilla | 0.00 | 0.00 | 1 | +| control | vanilla | 0.00 | 0.00 | 1 | +| control | vanilla | 0.00 | 0.00 | 1 | +| control | vanilla | 0.00 | 0.00 | 1 | +| control | vanilla | 0.00 | 0.00 | 1 | +| control | vanilla | 0.00 | 0.00 | 1 | +| control | vanilla | 0.00 | 0.00 | 1 | +| control | vanilla | 0.00 | 0.00 | 1 | +| control | vanilla | 0.00 | 0.00 | 1 | +| control | vanilla | 0.00 | 0.00 | 1 | +| control | vanilla | 0.00 | 0.00 | 1 | +| control | vanilla | 0.00 | 0.00 | 1 | +| control | vanilla | 0.00 | 0.00 | 1 | +| control | vanilla | 0.00 | 0.00 | 1 | +| control | vanilla | 0.00 | 0.00 | 1 | +| control | vanilla | 0.00 | 0.00 | 1 | +| control | vanilla | 0.00 | 0.00 | 1 | +| control | vanilla | 0.00 | 0.00 | 32 | +| control | vanilla | 0.00 | 0.00 | 32 | +| control | vanilla | 0.00 | 0.00 | 32 | +| control | vanilla | 0.00 | 0.00 | 32 | +| control | vanilla | 0.00 | 0.00 | 32 | +| control | vanilla | 0.00 | 0.00 | 32 | +| control | vanilla | 0.00 | 0.00 | 32 | +| control | vanilla | 0.00 | 0.00 | 32 | +| control | vanilla | 0.00 | 0.00 | 32 | +| control | vanilla | 0.00 | 0.00 | 32 | +| control | vanilla | 0.00 | 0.00 | 32 | +| control | vanilla | 0.00 | 0.00 | 32 | +| control | vanilla | 0.00 | 0.00 | 32 | +| control | vanilla | 0.00 | 0.00 | 32 | +| control | vanilla | 0.00 | 0.00 | 32 | +| control | vanilla | 0.00 | 0.00 | 32 | +| control | vanilla | 0.00 | 0.00 | 32 | +| control | vanilla | 0.00 | 0.00 | 32 | +| control | vanilla | 0.00 | 0.00 | 32 | +| control | vanilla | 0.00 | 0.00 | 32 | +| control | vanilla | 0.00 | 0.00 | 1024 | +| control | vanilla | 0.00 | 0.00 | 1024 | +| control | vanilla | 0.00 | 0.00 | 1024 | +| control | vanilla | 0.00 | 0.00 | 1024 | +| control | vanilla | 0.00 | 0.00 | 1024 | +| control | vanilla | 0.00 | 0.00 | 1024 | +| control | vanilla | 0.00 | 0.00 | 1024 | +| control | vanilla | 0.00 | 0.00 | 1024 | +| control | vanilla | 0.00 | 0.00 | 1024 | +| control | vanilla | 0.00 | 0.00 | 1024 | +| control | vanilla | 0.00 | 0.00 | 1024 | +| control | vanilla | 0.00 | 0.00 | 1024 | +| control | vanilla | 0.00 | 0.00 | 1024 | +| control | vanilla | 0.00 | 0.00 | 1024 | +| control | vanilla | 0.00 | 0.00 | 1024 | +| control | vanilla | 0.00 | 0.00 | 1024 | +| control | vanilla | 0.00 | 0.00 | 1024 | +| control | vanilla | 0.00 | 0.00 | 1024 | +| control | vanilla | 0.00 | 0.00 | 1024 | +| control | vanilla | 0.00 | 0.00 | 1024 | +| control | vanilla | 0.00 | 0.00 | 32768 | +| control | vanilla | 0.00 | 0.00 | 32768 | +| control | vanilla | 0.00 | 0.00 | 32768 | +| control | vanilla | 0.00 | 0.00 | 32768 | +| control | vanilla | 0.00 | 0.00 | 32768 | +| control | vanilla | 0.00 | 0.00 | 32768 | +| control | vanilla | 0.00 | 0.00 | 32768 | +| control | vanilla | 0.00 | 0.00 | 32768 | +| control | vanilla | 0.00 | 0.00 | 32768 | +| control | vanilla | 0.00 | 0.00 | 32768 | +| control | vanilla | 0.00 | 0.00 | 32768 | +| control | vanilla | 0.00 | 0.00 | 32768 | +| control | vanilla | 0.00 | 0.00 | 32768 | +| control | vanilla | 0.00 | 0.00 | 32768 | +| control | vanilla | 0.00 | 0.00 | 32768 | +| control | vanilla | 0.00 | 0.00 | 32768 | +| control | vanilla | 0.00 | 0.00 | 32768 | +| control | vanilla | 0.00 | 0.00 | 32768 | +| control | vanilla | 0.00 | 0.00 | 32768 | +| control | vanilla | 0.00 | 0.00 | 32768 | +| arithmetic | lite | 37.26 | 36.94 | 1 | +| arithmetic | lite | 36.40 | 36.40 | 1 | +| arithmetic | lite | 36.07 | 36.07 | 1 | +| arithmetic | lite | 35.99 | 35.99 | 1 | +| arithmetic | lite | 36.26 | 36.26 | 1 | +| arithmetic | lite | 36.76 | 36.73 | 1 | +| arithmetic | lite | 36.77 | 36.77 | 1 | +| arithmetic | lite | 36.34 | 36.34 | 1 | +| arithmetic | lite | 36.18 | 36.18 | 1 | +| arithmetic | lite | 36.05 | 36.05 | 1 | +| arithmetic | lite | 36.25 | 36.25 | 1 | +| arithmetic | lite | 36.48 | 36.48 | 1 | +| arithmetic | lite | 35.84 | 35.84 | 1 | +| arithmetic | lite | 36.44 | 36.44 | 1 | +| arithmetic | lite | 36.25 | 36.25 | 1 | +| arithmetic | lite | 36.15 | 36.15 | 1 | +| arithmetic | lite | 36.12 | 36.12 | 1 | +| arithmetic | lite | 36.32 | 36.32 | 1 | +| arithmetic | lite | 36.30 | 36.30 | 1 | +| arithmetic | lite | 36.05 | 36.05 | 1 | +| arithmetic | lite | 36.00 | 36.00 | 1 | +| arithmetic | lite | 35.95 | 35.95 | 1 | +| arithmetic | lite | 35.98 | 35.98 | 1 | +| arithmetic | lite | 36.10 | 36.10 | 1 | +| arithmetic | lite | 36.29 | 36.29 | 1 | +| arithmetic | lite | 36.18 | 36.18 | 1 | +| arithmetic | lite | 36.03 | 36.03 | 1 | +| arithmetic | lite | 35.97 | 35.96 | 1 | +| arithmetic | lite | 36.28 | 36.28 | 1 | +| arithmetic | lite | 36.18 | 36.18 | 1 | +| arithmetic | lite | 35.90 | 35.90 | 1 | +| arithmetic | lite | 36.09 | 36.09 | 1 | +| arithmetic | lite | 36.35 | 36.35 | 1 | +| arithmetic | lite | 35.98 | 35.98 | 1 | +| arithmetic | lite | 35.88 | 35.88 | 1 | +| arithmetic | lite | 35.90 | 35.90 | 1 | +| arithmetic | lite | 36.32 | 36.32 | 1 | +| arithmetic | lite | 36.97 | 36.62 | 1 | +| arithmetic | lite | 37.05 | 36.63 | 1 | +| arithmetic | lite | 37.25 | 36.58 | 1 | +| arithmetic | lite | 36.59 | 36.10 | 1 | +| arithmetic | lite | 36.75 | 36.23 | 1 | +| arithmetic | lite | 36.32 | 36.18 | 1 | +| arithmetic | lite | 35.98 | 35.98 | 1 | +| arithmetic | lite | 36.02 | 36.02 | 1 | +| arithmetic | lite | 36.06 | 36.06 | 1 | +| arithmetic | lite | 35.94 | 35.94 | 1 | +| arithmetic | lite | 36.60 | 36.60 | 1 | +| arithmetic | lite | 35.73 | 35.73 | 1 | +| arithmetic | lite | 36.41 | 36.41 | 1 | +| arithmetic | lite | 32.42 | 32.42 | 32 | +| arithmetic | lite | 32.56 | 32.56 | 32 | +| arithmetic | lite | 33.26 | 33.26 | 32 | +| arithmetic | lite | 32.74 | 32.74 | 32 | +| arithmetic | lite | 32.57 | 32.57 | 32 | +| arithmetic | lite | 32.49 | 32.49 | 32 | +| arithmetic | lite | 32.51 | 32.51 | 32 | +| arithmetic | lite | 32.73 | 32.73 | 32 | +| arithmetic | lite | 32.68 | 32.68 | 32 | +| arithmetic | lite | 32.69 | 32.69 | 32 | +| arithmetic | lite | 32.73 | 32.73 | 32 | +| arithmetic | lite | 32.69 | 32.69 | 32 | +| arithmetic | lite | 32.65 | 32.65 | 32 | +| arithmetic | lite | 32.78 | 32.78 | 32 | +| arithmetic | lite | 33.58 | 33.58 | 32 | +| arithmetic | lite | 33.83 | 33.83 | 32 | +| arithmetic | lite | 33.18 | 33.18 | 32 | +| arithmetic | lite | 32.96 | 32.96 | 32 | +| arithmetic | lite | 34.01 | 34.01 | 32 | +| arithmetic | lite | 32.99 | 32.99 | 32 | +| arithmetic | lite | 32.74 | 32.74 | 32 | +| arithmetic | lite | 33.47 | 33.47 | 32 | +| arithmetic | lite | 33.22 | 33.22 | 32 | +| arithmetic | lite | 32.39 | 32.39 | 32 | +| arithmetic | lite | 32.52 | 32.52 | 32 | +| arithmetic | lite | 32.57 | 32.57 | 32 | +| arithmetic | lite | 32.56 | 32.56 | 32 | +| arithmetic | lite | 32.48 | 32.48 | 32 | +| arithmetic | lite | 32.74 | 32.74 | 32 | +| arithmetic | lite | 32.64 | 32.64 | 32 | +| arithmetic | lite | 32.69 | 32.69 | 32 | +| arithmetic | lite | 33.21 | 33.21 | 32 | +| arithmetic | lite | 32.98 | 32.98 | 32 | +| arithmetic | lite | 32.60 | 32.60 | 32 | +| arithmetic | lite | 32.64 | 32.64 | 32 | +| arithmetic | lite | 32.49 | 32.49 | 32 | +| arithmetic | lite | 32.53 | 32.53 | 32 | +| arithmetic | lite | 32.53 | 32.53 | 32 | +| arithmetic | lite | 32.78 | 32.78 | 32 | +| arithmetic | lite | 32.92 | 32.92 | 32 | +| arithmetic | lite | 32.49 | 32.49 | 32 | +| arithmetic | lite | 32.57 | 32.57 | 32 | +| arithmetic | lite | 32.59 | 32.59 | 32 | +| arithmetic | lite | 32.67 | 32.67 | 32 | +| arithmetic | lite | 32.78 | 32.77 | 32 | +| arithmetic | lite | 32.47 | 32.47 | 32 | +| arithmetic | lite | 32.58 | 32.57 | 32 | +| arithmetic | lite | 32.44 | 32.44 | 32 | +| arithmetic | lite | 32.58 | 32.58 | 32 | +| arithmetic | lite | 32.61 | 32.60 | 32 | +| arithmetic | lite | 32.84 | 32.84 | 1024 | +| arithmetic | lite | 32.52 | 32.52 | 1024 | +| arithmetic | lite | 33.35 | 33.35 | 1024 | +| arithmetic | lite | 32.62 | 32.62 | 1024 | +| arithmetic | lite | 32.91 | 32.91 | 1024 | +| arithmetic | lite | 32.91 | 32.91 | 1024 | +| arithmetic | lite | 33.28 | 33.28 | 1024 | +| arithmetic | lite | 33.32 | 33.32 | 1024 | +| arithmetic | lite | 33.13 | 33.13 | 1024 | +| arithmetic | lite | 34.21 | 34.21 | 1024 | +| arithmetic | lite | 33.92 | 33.92 | 1024 | +| arithmetic | lite | 34.18 | 34.18 | 1024 | +| arithmetic | lite | 34.92 | 34.92 | 1024 | +| arithmetic | lite | 34.45 | 34.45 | 1024 | +| arithmetic | lite | 33.67 | 33.67 | 1024 | +| arithmetic | lite | 33.54 | 33.54 | 1024 | +| arithmetic | lite | 33.39 | 33.39 | 1024 | +| arithmetic | lite | 33.65 | 33.65 | 1024 | +| arithmetic | lite | 33.99 | 33.99 | 1024 | +| arithmetic | lite | 33.51 | 33.51 | 1024 | +| arithmetic | lite | 33.94 | 33.94 | 1024 | +| arithmetic | lite | 34.04 | 34.04 | 1024 | +| arithmetic | lite | 32.59 | 32.59 | 1024 | +| arithmetic | lite | 33.38 | 33.38 | 1024 | +| arithmetic | lite | 32.87 | 32.87 | 1024 | +| arithmetic | lite | 33.95 | 33.95 | 1024 | +| arithmetic | lite | 33.13 | 33.13 | 1024 | +| arithmetic | lite | 33.81 | 33.81 | 1024 | +| arithmetic | lite | 33.73 | 33.73 | 1024 | +| arithmetic | lite | 33.93 | 33.93 | 1024 | +| arithmetic | lite | 33.84 | 33.84 | 1024 | +| arithmetic | lite | 33.93 | 33.93 | 1024 | +| arithmetic | lite | 34.36 | 34.36 | 1024 | +| arithmetic | lite | 33.30 | 33.30 | 1024 | +| arithmetic | lite | 33.18 | 33.18 | 1024 | +| arithmetic | lite | 33.14 | 33.14 | 1024 | +| arithmetic | lite | 33.64 | 33.64 | 1024 | +| arithmetic | lite | 33.22 | 33.22 | 1024 | +| arithmetic | lite | 33.11 | 33.11 | 1024 | +| arithmetic | lite | 32.80 | 32.79 | 1024 | +| arithmetic | lite | 32.72 | 32.72 | 1024 | +| arithmetic | lite | 33.32 | 33.31 | 1024 | +| arithmetic | lite | 33.22 | 33.22 | 1024 | +| arithmetic | lite | 32.57 | 32.57 | 1024 | +| arithmetic | lite | 32.89 | 32.89 | 1024 | +| arithmetic | lite | 33.38 | 33.38 | 1024 | +| arithmetic | lite | 33.72 | 33.72 | 1024 | +| arithmetic | lite | 33.45 | 33.44 | 1024 | +| arithmetic | lite | 33.23 | 33.23 | 1024 | +| arithmetic | lite | 33.09 | 33.09 | 1024 | +| arithmetic | lite | 74.96 | 74.96 | 32768 | +| arithmetic | lite | 77.06 | 77.05 | 32768 | +| arithmetic | lite | 71.70 | 71.70 | 32768 | +| arithmetic | lite | 74.28 | 74.28 | 32768 | +| arithmetic | lite | 79.42 | 79.42 | 32768 | +| arithmetic | lite | 89.80 | 89.80 | 32768 | +| arithmetic | lite | 83.12 | 83.12 | 32768 | +| arithmetic | lite | 82.23 | 82.23 | 32768 | +| arithmetic | lite | 84.08 | 84.08 | 32768 | +| arithmetic | lite | 76.35 | 76.35 | 32768 | +| arithmetic | lite | 82.43 | 82.43 | 32768 | +| arithmetic | lite | 92.88 | 92.65 | 32768 | +| arithmetic | lite | 80.75 | 80.23 | 32768 | +| arithmetic | lite | 83.53 | 83.24 | 32768 | +| arithmetic | lite | 87.40 | 87.35 | 32768 | +| arithmetic | lite | 84.98 | 84.98 | 32768 | +| arithmetic | lite | 85.25 | 85.23 | 32768 | +| arithmetic | lite | 80.18 | 80.18 | 32768 | +| arithmetic | lite | 74.47 | 74.47 | 32768 | +| arithmetic | lite | 74.64 | 74.51 | 32768 | +| arithmetic | lite | 73.88 | 73.83 | 32768 | +| arithmetic | lite | 80.99 | 80.99 | 32768 | +| arithmetic | lite | 80.54 | 80.53 | 32768 | +| arithmetic | lite | 71.49 | 71.49 | 32768 | +| arithmetic | lite | 74.67 | 74.66 | 32768 | +| arithmetic | lite | 73.00 | 72.80 | 32768 | +| arithmetic | lite | 78.21 | 76.67 | 32768 | +| arithmetic | lite | 80.96 | 80.55 | 32768 | +| arithmetic | lite | 78.41 | 77.95 | 32768 | +| arithmetic | lite | 81.09 | 80.90 | 32768 | +| arithmetic | lite | 84.24 | 84.24 | 32768 | +| arithmetic | lite | 80.57 | 80.57 | 32768 | +| arithmetic | lite | 82.49 | 82.49 | 32768 | +| arithmetic | lite | 87.69 | 87.55 | 32768 | +| arithmetic | lite | 78.81 | 78.47 | 32768 | +| arithmetic | lite | 74.21 | 74.18 | 32768 | +| arithmetic | lite | 73.34 | 73.32 | 32768 | +| arithmetic | lite | 76.17 | 76.16 | 32768 | +| arithmetic | lite | 79.14 | 78.89 | 32768 | +| arithmetic | lite | 70.46 | 70.46 | 32768 | +| arithmetic | lite | 70.57 | 70.54 | 32768 | +| arithmetic | lite | 72.08 | 72.08 | 32768 | +| arithmetic | lite | 78.32 | 78.32 | 32768 | +| arithmetic | lite | 70.07 | 70.07 | 32768 | +| arithmetic | lite | 68.57 | 68.57 | 32768 | +| arithmetic | lite | 76.20 | 76.20 | 32768 | +| arithmetic | lite | 79.14 | 79.13 | 32768 | +| arithmetic | lite | 86.78 | 86.59 | 32768 | +| arithmetic | lite | 73.84 | 73.82 | 32768 | +| arithmetic | lite | 80.13 | 80.11 | 32768 | +| complete | lite | 723.53 | 722.56 | 1 | +| complete | lite | 689.93 | 689.71 | 1 | +| complete | lite | 624.18 | 624.17 | 1 | +| complete | lite | 653.93 | 653.92 | 1 | +| complete | lite | 423.63 | 423.63 | 1 | +| complete | lite | 792.42 | 792.39 | 1 | +| complete | lite | 511.51 | 511.51 | 1 | +| complete | lite | 749.90 | 749.81 | 1 | +| complete | lite | 388.26 | 388.26 | 1 | +| complete | lite | 305.39 | 305.39 | 1 | +| complete | lite | 740.99 | 740.98 | 1 | +| complete | lite | 553.88 | 553.85 | 1 | +| complete | lite | 194.43 | 194.43 | 1 | +| complete | lite | 601.60 | 601.60 | 1 | +| complete | lite | 545.33 | 544.80 | 1 | +| complete | lite | 674.82 | 674.77 | 1 | +| complete | lite | 170.13 | 170.13 | 1 | +| complete | lite | 587.25 | 587.21 | 1 | +| complete | lite | 322.85 | 322.85 | 1 | +| complete | lite | 563.87 | 563.63 | 1 | +| complete | lite | 609.05 | 609.05 | 1 | +| complete | lite | 306.31 | 306.30 | 1 | +| complete | lite | 218.02 | 218.02 | 1 | +| complete | lite | 674.58 | 674.36 | 1 | +| complete | lite | 295.65 | 294.63 | 1 | +| complete | lite | 314.86 | 314.87 | 1 | +| complete | lite | 662.84 | 662.84 | 1 | +| complete | lite | 517.38 | 517.04 | 1 | +| complete | lite | 632.43 | 632.40 | 1 | +| complete | lite | 754.63 | 752.63 | 1 | +| complete | lite | 800.15 | 794.34 | 1 | +| complete | lite | 681.67 | 679.89 | 1 | +| complete | lite | 201.38 | 201.37 | 1 | +| complete | lite | 627.25 | 627.25 | 1 | +| complete | lite | 554.92 | 554.91 | 1 | +| complete | lite | 669.58 | 669.57 | 1 | +| complete | lite | 470.09 | 470.06 | 1 | +| complete | lite | 640.28 | 640.28 | 1 | +| complete | lite | 165.81 | 165.80 | 1 | +| complete | lite | 581.43 | 581.42 | 1 | +| complete | lite | 649.63 | 649.62 | 1 | +| complete | lite | 588.29 | 588.29 | 1 | +| complete | lite | 639.92 | 639.91 | 1 | +| complete | lite | 574.92 | 574.93 | 1 | +| complete | lite | 260.30 | 260.30 | 1 | +| complete | lite | 600.60 | 600.59 | 1 | +| complete | lite | 683.82 | 683.77 | 1 | +| complete | lite | 287.50 | 287.48 | 1 | +| complete | lite | 755.56 | 755.56 | 1 | +| complete | lite | 747.17 | 747.16 | 1 | +| complete | lite | 776.32 | 776.27 | 32 | +| complete | lite | 543.08 | 543.08 | 32 | +| complete | lite | 412.97 | 412.96 | 32 | +| complete | lite | 674.31 | 674.29 | 32 | +| complete | lite | 634.87 | 634.85 | 32 | +| complete | lite | 512.69 | 512.68 | 32 | +| complete | lite | 691.37 | 691.37 | 32 | +| complete | lite | 633.43 | 633.42 | 32 | +| complete | lite | 489.41 | 489.36 | 32 | +| complete | lite | 513.72 | 513.72 | 32 | +| complete | lite | 309.70 | 309.70 | 32 | +| complete | lite | 495.75 | 495.72 | 32 | +| complete | lite | 569.48 | 569.48 | 32 | +| complete | lite | 599.90 | 599.91 | 32 | +| complete | lite | 607.71 | 607.71 | 32 | +| complete | lite | 734.97 | 734.92 | 32 | +| complete | lite | 593.86 | 593.85 | 32 | +| complete | lite | 310.90 | 310.90 | 32 | +| complete | lite | 370.72 | 370.71 | 32 | +| complete | lite | 661.21 | 661.20 | 32 | +| complete | lite | 483.80 | 483.79 | 32 | +| complete | lite | 620.93 | 620.94 | 32 | +| complete | lite | 395.31 | 395.31 | 32 | +| complete | lite | 634.60 | 634.59 | 32 | +| complete | lite | 658.06 | 658.06 | 32 | +| complete | lite | 644.53 | 644.53 | 32 | +| complete | lite | 585.79 | 585.79 | 32 | +| complete | lite | 431.48 | 431.41 | 32 | +| complete | lite | 778.63 | 778.41 | 32 | +| complete | lite | 504.66 | 504.63 | 32 | +| complete | lite | 342.39 | 342.37 | 32 | +| complete | lite | 568.08 | 567.99 | 32 | +| complete | lite | 817.80 | 817.79 | 32 | +| complete | lite | 390.51 | 390.51 | 32 | +| complete | lite | 587.08 | 587.06 | 32 | +| complete | lite | 416.91 | 416.88 | 32 | +| complete | lite | 841.17 | 841.17 | 32 | +| complete | lite | 626.75 | 626.73 | 32 | +| complete | lite | 765.55 | 765.55 | 32 | +| complete | lite | 475.86 | 475.86 | 32 | +| complete | lite | 480.66 | 480.66 | 32 | +| complete | lite | 395.58 | 395.58 | 32 | +| complete | lite | 703.39 | 703.39 | 32 | +| complete | lite | 623.91 | 623.91 | 32 | +| complete | lite | 814.85 | 814.84 | 32 | +| complete | lite | 438.46 | 438.46 | 32 | +| complete | lite | 500.53 | 500.54 | 32 | +| complete | lite | 723.36 | 723.36 | 32 | +| complete | lite | 448.06 | 448.07 | 32 | +| complete | lite | 592.53 | 592.52 | 32 | +| complete | lite | 1106.87 | 1106.88 | 1024 | +| complete | lite | 1086.09 | 1085.99 | 1024 | +| complete | lite | 759.23 | 759.23 | 1024 | +| complete | lite | 1110.70 | 1110.66 | 1024 | +| complete | lite | 1206.49 | 1206.45 | 1024 | +| complete | lite | 1148.69 | 1148.69 | 1024 | +| complete | lite | 1135.75 | 1135.76 | 1024 | +| complete | lite | 1105.14 | 1105.12 | 1024 | +| complete | lite | 1236.00 | 1235.97 | 1024 | +| complete | lite | 1275.01 | 1274.94 | 1024 | +| complete | lite | 1039.72 | 1039.72 | 1024 | +| complete | lite | 1237.13 | 1237.13 | 1024 | +| complete | lite | 1218.68 | 1218.63 | 1024 | +| complete | lite | 1374.46 | 1374.21 | 1024 | +| complete | lite | 1310.89 | 1310.89 | 1024 | +| complete | lite | 1154.87 | 1154.87 | 1024 | +| complete | lite | 1085.11 | 1084.81 | 1024 | +| complete | lite | 834.39 | 834.38 | 1024 | +| complete | lite | 1010.97 | 1010.97 | 1024 | +| complete | lite | 1093.91 | 1093.91 | 1024 | +| complete | lite | 1382.04 | 1382.02 | 1024 | +| complete | lite | 1127.83 | 1127.83 | 1024 | +| complete | lite | 664.25 | 664.25 | 1024 | +| complete | lite | 1327.77 | 1327.77 | 1024 | +| complete | lite | 1113.74 | 1113.74 | 1024 | +| complete | lite | 1075.13 | 1075.13 | 1024 | +| complete | lite | 1121.92 | 1121.92 | 1024 | +| complete | lite | 1310.80 | 1310.79 | 1024 | +| complete | lite | 962.52 | 962.51 | 1024 | +| complete | lite | 1052.42 | 1052.42 | 1024 | +| complete | lite | 728.25 | 728.25 | 1024 | +| complete | lite | 835.02 | 835.02 | 1024 | +| complete | lite | 1325.92 | 1325.92 | 1024 | +| complete | lite | 1077.96 | 1077.96 | 1024 | +| complete | lite | 1241.28 | 1240.96 | 1024 | +| complete | lite | 1305.93 | 1305.91 | 1024 | +| complete | lite | 1049.91 | 1049.88 | 1024 | +| complete | lite | 1219.75 | 1219.71 | 1024 | +| complete | lite | 623.50 | 623.50 | 1024 | +| complete | lite | 989.61 | 989.61 | 1024 | +| complete | lite | 1023.78 | 1023.78 | 1024 | +| complete | lite | 1184.93 | 1184.81 | 1024 | +| complete | lite | 1061.89 | 1061.90 | 1024 | +| complete | lite | 1069.00 | 1068.97 | 1024 | +| complete | lite | 1267.68 | 1267.52 | 1024 | +| complete | lite | 1270.25 | 1270.25 | 1024 | +| complete | lite | 1210.18 | 1209.93 | 1024 | +| complete | lite | 1068.34 | 1062.88 | 1024 | +| complete | lite | 1166.18 | 1159.55 | 1024 | +| complete | lite | 1092.61 | 1087.22 | 1024 | +| complete | lite | 1177.96 | 1176.74 | 32768 | +| complete | lite | 1176.31 | 1176.20 | 32768 | +| complete | lite | 1026.71 | 1026.48 | 32768 | +| complete | lite | 1088.54 | 1086.15 | 32768 | +| complete | lite | 1155.79 | 1148.56 | 32768 | +| complete | lite | 1105.22 | 1103.75 | 32768 | +| complete | lite | 1090.18 | 1089.99 | 32768 | +| complete | lite | 1220.37 | 1220.18 | 32768 | +| complete | lite | 1020.25 | 1011.71 | 32768 | +| complete | lite | 1200.44 | 1192.92 | 32768 | +| complete | lite | 1144.20 | 1144.17 | 32768 | +| complete | lite | 1343.69 | 1343.68 | 32768 | +| complete | lite | 995.65 | 995.62 | 32768 | +| complete | lite | 1126.91 | 1126.89 | 32768 | +| complete | lite | 1011.32 | 1011.29 | 32768 | +| complete | lite | 1090.83 | 1090.77 | 32768 | +| complete | lite | 1093.61 | 1093.60 | 32768 | +| complete | lite | 1089.13 | 1089.12 | 32768 | +| complete | lite | 1246.96 | 1246.91 | 32768 | +| complete | lite | 1078.44 | 1078.44 | 32768 | +| complete | lite | 1261.88 | 1261.85 | 32768 | +| complete | lite | 1209.66 | 1209.65 | 32768 | +| complete | lite | 1236.44 | 1236.40 | 32768 | +| complete | lite | 953.64 | 953.65 | 32768 | +| complete | lite | 1086.47 | 1086.46 | 32768 | +| complete | lite | 1039.57 | 1039.50 | 32768 | +| complete | lite | 939.09 | 939.08 | 32768 | +| complete | lite | 970.19 | 970.14 | 32768 | +| complete | lite | 1154.79 | 1154.76 | 32768 | +| complete | lite | 1159.90 | 1159.85 | 32768 | +| complete | lite | 903.69 | 903.69 | 32768 | +| complete | lite | 958.45 | 958.07 | 32768 | +| complete | lite | 1047.56 | 1047.51 | 32768 | +| complete | lite | 959.05 | 959.04 | 32768 | +| complete | lite | 969.65 | 969.35 | 32768 | +| complete | lite | 1064.84 | 1064.82 | 32768 | +| complete | lite | 1057.66 | 1057.58 | 32768 | +| complete | lite | 1295.73 | 1295.73 | 32768 | +| complete | lite | 1119.69 | 1119.67 | 32768 | +| complete | lite | 1024.96 | 1024.93 | 32768 | +| complete | lite | 1029.08 | 1029.04 | 32768 | +| complete | lite | 898.97 | 898.92 | 32768 | +| complete | lite | 1310.38 | 1310.36 | 32768 | +| complete | lite | 1023.94 | 1023.92 | 32768 | +| complete | lite | 1211.95 | 1211.93 | 32768 | +| complete | lite | 1064.84 | 1064.81 | 32768 | +| complete | lite | 901.60 | 901.60 | 32768 | +| complete | lite | 958.30 | 958.29 | 32768 | +| complete | lite | 1207.84 | 1207.82 | 32768 | +| complete | lite | 1329.57 | 1329.54 | 32768 | +| nop | lite | 38.11 | 38.11 | 1 | +| nop | lite | 38.12 | 38.12 | 1 | +| nop | lite | 35.60 | 35.60 | 1 | +| nop | lite | 35.52 | 35.52 | 1 | +| nop | lite | 35.61 | 35.60 | 1 | +| nop | lite | 35.56 | 35.56 | 1 | +| nop | lite | 35.59 | 35.59 | 1 | +| nop | lite | 35.59 | 35.59 | 1 | +| nop | lite | 35.60 | 35.60 | 1 | +| nop | lite | 35.55 | 35.55 | 1 | +| nop | lite | 35.56 | 35.56 | 1 | +| nop | lite | 35.55 | 35.55 | 1 | +| nop | lite | 35.59 | 35.59 | 1 | +| nop | lite | 35.65 | 35.65 | 1 | +| nop | lite | 35.55 | 35.55 | 1 | +| nop | lite | 35.59 | 35.59 | 1 | +| nop | lite | 35.60 | 35.60 | 1 | +| nop | lite | 35.70 | 35.70 | 1 | +| nop | lite | 35.89 | 35.79 | 1 | +| nop | lite | 35.82 | 35.82 | 1 | +| nop | lite | 35.85 | 35.85 | 1 | +| nop | lite | 35.84 | 35.84 | 1 | +| nop | lite | 36.20 | 35.91 | 1 | +| nop | lite | 35.81 | 35.79 | 1 | +| nop | lite | 35.75 | 35.74 | 1 | +| nop | lite | 35.67 | 35.67 | 1 | +| nop | lite | 35.60 | 35.60 | 1 | +| nop | lite | 35.59 | 35.59 | 1 | +| nop | lite | 35.61 | 35.61 | 1 | +| nop | lite | 35.57 | 35.57 | 1 | +| nop | lite | 35.58 | 35.58 | 1 | +| nop | lite | 35.60 | 35.60 | 1 | +| nop | lite | 35.56 | 35.56 | 1 | +| nop | lite | 35.56 | 35.56 | 1 | +| nop | lite | 35.59 | 35.59 | 1 | +| nop | lite | 35.62 | 35.58 | 1 | +| nop | lite | 35.59 | 35.59 | 1 | +| nop | lite | 35.59 | 35.59 | 1 | +| nop | lite | 35.56 | 35.56 | 1 | +| nop | lite | 35.52 | 35.52 | 1 | +| nop | lite | 35.56 | 35.56 | 1 | +| nop | lite | 35.53 | 35.53 | 1 | +| nop | lite | 35.57 | 35.57 | 1 | +| nop | lite | 35.66 | 35.65 | 1 | +| nop | lite | 35.80 | 35.80 | 1 | +| nop | lite | 35.73 | 35.73 | 1 | +| nop | lite | 35.83 | 35.83 | 1 | +| nop | lite | 35.56 | 35.56 | 1 | +| nop | lite | 35.59 | 35.59 | 1 | +| nop | lite | 35.59 | 35.59 | 1 | +| nop | lite | 32.16 | 32.16 | 32 | +| nop | lite | 32.02 | 32.02 | 32 | +| nop | lite | 32.03 | 32.03 | 32 | +| nop | lite | 32.05 | 32.05 | 32 | +| nop | lite | 32.04 | 32.04 | 32 | +| nop | lite | 32.04 | 32.04 | 32 | +| nop | lite | 32.05 | 32.05 | 32 | +| nop | lite | 32.03 | 32.03 | 32 | +| nop | lite | 32.06 | 32.06 | 32 | +| nop | lite | 32.02 | 32.02 | 32 | +| nop | lite | 32.05 | 32.05 | 32 | +| nop | lite | 32.04 | 32.04 | 32 | +| nop | lite | 32.04 | 32.04 | 32 | +| nop | lite | 32.07 | 32.07 | 32 | +| nop | lite | 32.18 | 32.18 | 32 | +| nop | lite | 32.03 | 32.03 | 32 | +| nop | lite | 32.07 | 32.07 | 32 | +| nop | lite | 32.08 | 32.08 | 32 | +| nop | lite | 32.08 | 32.08 | 32 | +| nop | lite | 32.10 | 32.09 | 32 | +| nop | lite | 32.06 | 32.06 | 32 | +| nop | lite | 32.01 | 32.01 | 32 | +| nop | lite | 32.09 | 32.09 | 32 | +| nop | lite | 32.01 | 32.01 | 32 | +| nop | lite | 32.05 | 32.05 | 32 | +| nop | lite | 32.05 | 32.05 | 32 | +| nop | lite | 32.03 | 32.03 | 32 | +| nop | lite | 32.05 | 32.05 | 32 | +| nop | lite | 32.01 | 32.01 | 32 | +| nop | lite | 32.05 | 32.05 | 32 | \ No newline at end of file From c80d34342c3fec1c7be2269c0b5cf444ac9fcc22 Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Wed, 30 Jun 2021 16:17:28 -0400 Subject: [PATCH 055/113] Add raw timings table --- joss/paper.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/joss/paper.md b/joss/paper.md index a726fb69..6fb00936 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -54,7 +54,7 @@ Also, parallel and distributed computing resources are ultimately key to increas To accelerating execution of event-driven genetic programs. [????] We performed a set of microbenchmarks to quantify the effectiveness of `signalgp-lite`'s optimizations. Cache size limitations profoundly affect memory access time, which is key to performance [@skadron1999branch]. -In order to determine how the libary performs across cache levels, we benchmarked over different orders of magnitude of memory loads, by varying agent counts between from 1 and 32768. [addfig](raw timings) +In order to determine how the libary performs across cache levels, we benchmarked over different orders of magnitude of memory loads, by varying agent counts between from 1 and 32768 \ref{raw-timings-table}. \autoref{fig:bench-wall} shows wall-clock times measured using Google Benchmark version 1.5.2-1. We performed five microbenchmark experiments, overviewed below, in order to understand how different aspects of the library influenced performance. @@ -167,6 +167,7 @@ Any opinions, findings, and conclusions or recommendations expressed in this mat ![Wall time benchmarking results of 20 replicates. The x-axis represents different agent counts.\label{fig:raw-timings}](figures/raw-timings.png) +Table: (Raw benchmark timings, also available as a .csv file in the supplement repository.) \label{raw-timings-table} | Library | Implementation | Wall Nanoseconds | CPU Nanoseconds | num agents | |-|-|-|-|-| From da573a14e2aaf8230459a6f7d34ed90cf5bbc30e Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Wed, 30 Jun 2021 16:18:08 -0400 Subject: [PATCH 056/113] Change --- joss/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss/paper.md b/joss/paper.md index 6fb00936..df989055 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -54,7 +54,7 @@ Also, parallel and distributed computing resources are ultimately key to increas To accelerating execution of event-driven genetic programs. [????] We performed a set of microbenchmarks to quantify the effectiveness of `signalgp-lite`'s optimizations. Cache size limitations profoundly affect memory access time, which is key to performance [@skadron1999branch]. -In order to determine how the libary performs across cache levels, we benchmarked over different orders of magnitude of memory loads, by varying agent counts between from 1 and 32768 \ref{raw-timings-table}. +In order to determine how the libary performs across cache levels, we benchmarked over different orders of magnitude of memory loads, by varying agent counts between from 1 and 32768 (Supplementary Table \ref{raw-timings-table}). \autoref{fig:bench-wall} shows wall-clock times measured using Google Benchmark version 1.5.2-1. We performed five microbenchmark experiments, overviewed below, in order to understand how different aspects of the library influenced performance. From eba777c3cc11c3d81ed9bd34d26a6cdb31e7e324 Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Thu, 1 Jul 2021 19:45:53 -0400 Subject: [PATCH 057/113] Forgot to commit --- joss/paper.md | 55 +++++++++++++++++++++++++++++---------------------- 1 file changed, 31 insertions(+), 24 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index df989055..d7b9948d 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -22,9 +22,11 @@ bibliography: paper.bib # Abstract Event-driven genetic programming representations have been shown to outperform traditional traditional imperative representations on interaction-heavy problems. +Traditional programming paradigms struggle on these tasks due to the need to drive the simulation procedurally, making their design and development an arduous task. +Event-driven computing addresses these problems by abstracting away signal triggering and hanlding, simplifying program design and implementation. + Existing work uses the SignalGP library, which caters to traditional program synthesis applications. However, large-scale artificial life applications require a different set of implementation priorities. -SignalGP is an existing implementation that is focused on extending tag-based referencing by applying event-driven techiques to genetic programming. With signalgp-lite, our goal is to increase performance to enable large-scale experiments, such as those undertaken in Artificial Life, by removing control-flow overhead (e.g., the call stack) and trading run-time flexibility for better-performing compile-time configuration. Here, we report benchmarking experiments that show an 8x to 30x speedup. We also report solution quality equivalent to SignalGP on two benchmark problems originally developed for that implementation. @@ -32,30 +34,26 @@ The library is currently used in Artificial Life experiments studying the evolut # Summary -`signalgp-lite` is a software framework for event-driven genetic programming. -Unlike the traditional imperative paradigm, where a single chain of execution must actively pull XXX, event-driven simulations trigger event-handlers (i.e., program modules) in response to enviromental, self-generated, and cohabitant-generated signals. +`signalgp-lite` is a software framework for event-driven genetic programming written in C++. +Unlike the traditional imperative paradigm, where a single chain of execution actively controls every aspect of the program, event-driven simulations trigger event-handlers (i.e., program modules) in response to enviromental, self-generated, and cohabitant-generated signals. This representation outperforms imperative programming on interaction intensive problems, where inputs from the enviroment or other organisms ocmust be handled [cite], as is the case in large-scale digital evolution experiments as well as various other artifical life simulations. -`signalgp-lite` is written in C++. -It was chosen due to its low-level control over runtime performance. -C++ is the language of choice for many artificial life and genetic programming utilities. - # Statement of need Despite being able to simulate evolution with faster generations than possible in biological experiements [cite avida paper], the scale of artifical life populations is profoundly limited by available computational resources [cite blog]. Large population sizes are essential to studying essential topics such as ecologies, the transition to multicelularity, and the role of rare events in evolution. Also, parallel and distributed computing resources are ultimately key to increasing scale. [join these] Computational efficiency is crucial to enabling larger-scale artificial life situations.] -[add transition] `signalgp-lite` fills a niche for interaction-heavy genetic programming applications that can tolerate trading a high level of customizability at runtime for a considerable speed-up. +In comparison to SignalGP, which was designed with generic genetic programming in mind, `signalgp-lite` fills a niche for interaction-heavy genetic programming applications that can tolerate trading a high level of customizability at runtime for a considerable speed-up. +Since the simulation parameters of artificial life experiments need not change during execution, they are a clear candidate for using `signalgp-lite`. ## Execution Speed -To accelerating execution of event-driven genetic programs. [????] -We performed a set of microbenchmarks to quantify the effectiveness of `signalgp-lite`'s optimizations. +We performed a set of microbenchmarks to quantify the effectiveness of `signalgp-lite`'s optimizations in accelerating execution of event-driven genetic programs. Cache size limitations profoundly affect memory access time, which is key to performance [@skadron1999branch]. In order to determine how the libary performs across cache levels, we benchmarked over different orders of magnitude of memory loads, by varying agent counts between from 1 and 32768 (Supplementary Table \ref{raw-timings-table}). -\autoref{fig:bench-wall} shows wall-clock times measured using Google Benchmark version 1.5.2-1. +\autoref{fig:bench-wall} shows raw wall-clock timings measured using Google Benchmark version 1.5.2-1. We performed five microbenchmark experiments, overviewed below, in order to understand how different aspects of the library influenced performance. @@ -101,29 +99,38 @@ Two experiments from the original SignalGP paper were replicated [@lalejini2018e The Changing Enviroment Problem consisted of K mutually-exclusive enviromental signals (2, 4, 8, 16). Organisms were tasked to respond to each with a particular signaling instruction. -\autoref{fig:tts-changing} shows the number of generations elapsed before a full solution was found [add](population size and selection scheme). -`signalgp-lite` evolved fill solutions to each problem within 3500 updates in all 100 tested replicates. +A total of 100 replicates were evolved for up to 10000 generations. +On each update, 100 organisms were chosen based on the elite and roulette selection schemes. +\autoref{fig:tts-changing} shows the number of generations elapsed before a full solution was found. +`signalgp-lite` evolved full solutions to each problem within 3500 updates in all 100 tested replicates. -In the `K=16` case, we achieved a 100% signal reproduction rate compared to an average of 32% on the vanilla implementation (Figure 2 in @lalejini2018evolving). +In the `K=16` case, we achieved a 100% signal reproduction rate compared to an average of 32% on the vanilla implementation (Figure 2 in [@lalejini2018evolving]). We suspect this occured due to differences in how default mutation parameters and program initialization are performed. ## Contextual Signal Problem -This problem tests regulation by evolving programs that maintain memory of previously encountered signals. -A total of 16 input signal pairs and 4 response signals were tested. +This problem tests regulation by evolving programs that must maintain memory of previously encountered signals. +Programs must remember an initial signal (i.e., the "context") in order to respond appropiately to the second signal. The unordered input signal pairs were assigned a response signal that then had to be replicated by the organism. -Table 2 in @lalejini2018evolving provides a visual representation of this sequence. - -TODO: expand more on the problem -TODO: explain how `signalgp-lite` compared in this problem. +A total of 16 input signal pairs and 4 response signals were tested. +Table 2 in [@lalejini2018evolving] provides a visual representation of these sequences. +A total of XX replicates were evolved for up to 10000 generations. +Programs were reproduced based on a 16-way lexicase selection scheme, with each of the input signal pairs being a test case. +Each program was sent the first signal of each test case and given 128 updates to process it. +After this, their internal CPU state was reset, and the second signal was sent. +After another 128 updates, their response was evaluated. +In order to save resources and computing time, as soon as a replicate evolved a fully-correct solution, their evolution was halted. +\autoref{fig:tts-context} shows the number of generations elapsed before a full solution was found. -# Projects Using the Sofrware +`signalgp-lite` evolved full solutions in half as many updates compared to SignalGP. +Moreover, less programs were unable to reach a full solution in 10000 updates under `signalgp-lite`. -In light of the above, `signalgp-lite` is the framework of choice in DISHTINY. +MAYBE TODO: talk about mutation rates maybe? -TODO: talk more about it +# Projects Using the Sofrware +`signalgp-lite` is the framework of choice in DISHTINY, a framework for studying digital organism multicelularity. # Experimental Materials @@ -167,7 +174,7 @@ Any opinions, findings, and conclusions or recommendations expressed in this mat ![Wall time benchmarking results of 20 replicates. The x-axis represents different agent counts.\label{fig:raw-timings}](figures/raw-timings.png) -Table: (Raw benchmark timings, also available as a .csv file in the supplement repository.) \label{raw-timings-table} +Table: Raw benchmark timings, also available as a CSV file in the supplement repository. \label{raw-timings-table} | Library | Implementation | Wall Nanoseconds | CPU Nanoseconds | num agents | |-|-|-|-|-| From 2001b0c38e94cbdd9608ff249092d127c25d168f Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Fri, 2 Jul 2021 15:45:39 -0400 Subject: [PATCH 058/113] Blah --- joss/paper.md | 64 +++++++++++++++++++++++++-------------------------- 1 file changed, 31 insertions(+), 33 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index d7b9948d..8babbdd3 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -21,70 +21,71 @@ bibliography: paper.bib # Abstract -Event-driven genetic programming representations have been shown to outperform traditional traditional imperative representations on interaction-heavy problems. -Traditional programming paradigms struggle on these tasks due to the need to drive the simulation procedurally, making their design and development an arduous task. -Event-driven computing addresses these problems by abstracting away signal triggering and hanlding, simplifying program design and implementation. +Event-driven genetic programming representations have been shown to outperform traditional traditional imperative representations on interaction-intensive problems. +Traditional programming paradigms struggle on these tasks due to the need to poll a large number of sensors procedurally, making the design and development of complex evolutionary simulations an arduous task. +Event-driven genetic programming representations address these problems by organizing genome content into modules and triggering these modules in response to enviromental signals, simplifying simulation design and implementation. -Existing work uses the SignalGP library, which caters to traditional program synthesis applications. +Existing work developing event-driven Genetic Programming methodology uses the SignalGP library. This library caters to traditional program synthesis applications. However, large-scale artificial life applications require a different set of implementation priorities. -With signalgp-lite, our goal is to increase performance to enable large-scale experiments, such as those undertaken in Artificial Life, by removing control-flow overhead (e.g., the call stack) and trading run-time flexibility for better-performing compile-time configuration. +With signalgp-lite, our goal is to increase performance to enable large-scale experiments by removing control-flow overhead (e.g., the call stack) and trading run-time flexibility for better-performing compile-time configuration. Here, we report benchmarking experiments that show an 8x to 30x speedup. We also report solution quality equivalent to SignalGP on two benchmark problems originally developed for that implementation. -The library is currently used in Artificial Life experiments studying the evolution of multicelularity, and our expectation is that it will be useful in future Artifical Life and Genetic Programming projects. +The library has enabled order-of-magnitude scale-up of existing Artificial Life experiments studying the evolution of multicelularity; we anticipate it will enable novel work in other Artificial Life and Genetic Programming contexts. # Summary -`signalgp-lite` is a software framework for event-driven genetic programming written in C++. -Unlike the traditional imperative paradigm, where a single chain of execution actively controls every aspect of the program, event-driven simulations trigger event-handlers (i.e., program modules) in response to enviromental, self-generated, and cohabitant-generated signals. -This representation outperforms imperative programming on interaction intensive problems, where inputs from the enviroment or other organisms ocmust be handled [cite], as is the case in large-scale digital evolution experiments as well as various other artifical life simulations. +`signalgp-lite` is a C++ software library for event-driven genetic programming. +Unlike the traditional imperative paradigm, where a single chain of execution directly manages every aspect of the program, event-driven simulations trigger event handlers (i.e., program modules) in response to enviromental, self-generated, and cohabitant-generated signals. +This representation outperforms imperative programming on interaction intensive problems, where inputs from the enviroment or other organisms must be handled [cite], as is the case in some genetic programming contexts and many artifical life simulations. # Statement of need -Despite being able to simulate evolution with faster generations than possible in biological experiements [cite avida paper], the scale of artifical life populations is profoundly limited by available computational resources [cite blog]. -Large population sizes are essential to studying essential topics such as ecologies, the transition to multicelularity, and the role of rare events in evolution. -Also, parallel and distributed computing resources are ultimately key to increasing scale. [join these] Computational efficiency is crucial to enabling larger-scale artificial life situations.] +Despite being able to simulate evolution with much faster generational turnover than is possible in biological experiements [cite avida paper], the scale of artifical life populations is profoundly limited by available computational resources [cite blog]. +Large population sizes are essential to studying key biological topics such as ecologies, the transition to multicelularity, and the role of rare events in evolution. +In conjunction with parallel and distributied computing, computational efficiency is crucial to enabling larger-scale artificial life situations. -In comparison to SignalGP, which was designed with generic genetic programming in mind, `signalgp-lite` fills a niche for interaction-heavy genetic programming applications that can tolerate trading a high level of customizability at runtime for a considerable speed-up. -Since the simulation parameters of artificial life experiments need not change during execution, they are a clear candidate for using `signalgp-lite`. +In comparison to SignalGP [introduce this before], which was designed to target generic genetic programming problems, `signalgp-lite` fills a niche for interaction-heavy genetic programming applications that can tolerate trading a high level of customizability at runtime for a considerable speedup. +Since the simulation parameters of artificial life experiments need not change during execution, they are a clear candidate for using `signalgp-lite`. [focus more on control flow] -## Execution Speed +## Execution Speed Benchmarking We performed a set of microbenchmarks to quantify the effectiveness of `signalgp-lite`'s optimizations in accelerating execution of event-driven genetic programs. -Cache size limitations profoundly affect memory access time, which is key to performance [@skadron1999branch]. -In order to determine how the libary performs across cache levels, we benchmarked over different orders of magnitude of memory loads, by varying agent counts between from 1 and 32768 (Supplementary Table \ref{raw-timings-table}). -\autoref{fig:bench-wall} shows raw wall-clock timings measured using Google Benchmark version 1.5.2-1. -We performed five microbenchmark experiments, overviewed below, in order to understand how different aspects of the library influenced performance. +Hardware caching size profoundly affects memory access time, which is key to performance [@skadron1999branch]. +In order to determine the relative perfomance of the libraries across cache levels, we benchmarked over different orders of magnitude of memory loads, by varying agent counts between from 1 and 32768 (Supplementary Table \ref{raw-timings-table}). +Supplementary \autoref{fig:raw-timings} shows raw wall-clock timings measured using Google Benchmark version 1.5.2-1. + +We performed five microbenchmark experiments, reported below, in order to isolate how different aspects of the library design influenced performance. ### control -This experiemnt verifies the validity of our benchmarking process. -The 1x wall speedup (\autoref{fig:bench-wall}) confirms that our results are not directly affected by the experimental aparatus, so that any speedups in other experiments stem solely from differences between SignalGP and `signalgp-lite`. +[mention what the experiment is] This experiemnt verifies the validity of our benchmarking process. +The 1x wall speedup (\autoref{fig:bench-wall}) confirms that furthers results are not unduely skewed by our experimental aparatus. ### nop -Benchmarking the `nop` instruction in isolation denotes the relative performance impact of `signalgp-lite`'s byte-code interpreter compared to SignalGP's lamda-based instructions. +[mention what the experiment is] Benchmarking the `nop` instruction isolates the relative performance impact of `signalgp-lite`'s byte-code interpreter compared to SignalGP's lamda-based instructions. -We observe a 10x to 30x speedup under `signalgp-lite` (\autoref{fig:bench-wall}). +We observe an 8x to 30x speedup under `signalgp-lite` (\autoref{fig:bench-wall}). ### arithmetic -Benchmarking arithmetic operations aids in measuring the performance effect of `signalgp-lite`'s fixed-length array registers compared to SignalGP's variable-length vectors. -This compares the effects of compile-time optimizations that streamline register access at the cost of the ability to change the number of registers on the fly. +Benchmarking arithmetic operations incorporates the performance effect of `signalgp-lite`'s fixed-length array registers compared to SignalGP's variable-length vector registers. +This compile-time optimization strimlines register access at the cost of the ability to change the number of registers on the fly. -\autoref{fig:bench-wall} shows this trade-off results in a 20x to 50x speed-up. +\autoref{fig:bench-wall} shows that incorporating this trade-off increases speedup to 20x to 50x. ### `complete` -In order to improve performance, `signalgp-lite` omits a function stack and implements inner while-loops in terms of `jump` instructions. +In order to improve performance, `signalgp-lite` omits a function stack and implements inner loops and conditionals in terms of tagged `jump` instructions. The complete benchmark adds control flow to the prior benchmarks' instruction set. - `signalgp-lite`'s stripped-down control flow modele nables a 30x to 55x speed-up compared to vanilla SignalGP. +`signalgp-lite`'s stripped-down control flow model increases speedup to 30x to 55x compared to vanilla SignalGP. ### `sans_regulation` -Since regulation hinders tag-match caching, we wanted to measure changes both with and without regulation, +Since regulation [what is regulation?] hinders tag-match caching, we wanted to measure changes both with and without regulation, This benchmark measures the complete instruction set without regulation-specific instructions ([list them here]). As shown on \autoref{fig:bench-wall}, this yields a 35x to 47x speed-up. @@ -132,9 +133,6 @@ MAYBE TODO: talk about mutation rates maybe? `signalgp-lite` is the framework of choice in DISHTINY, a framework for studying digital organism multicelularity. -# Experimental Materials - - # Figures + # Statement of need Despite being able to simulate evolution with much faster generational turnover than is possible in biological experiements [@ofria2004avida], the scale of artifical life populations is profoundly limited by available computational resources [@Moreno_2020]. From a66215e57d03deb93d3affc8f8f851dbdfcad878 Mon Sep 17 00:00:00 2001 From: Matthew Andres Moreno Date: Thu, 15 Jul 2021 15:06:06 -0400 Subject: [PATCH 067/113] signalgp-lite -> SignalGP-Lite --- joss/paper.md | 39 +++++++++++++++++++-------------------- 1 file changed, 19 insertions(+), 20 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index 9c2327ce..8133ef0e 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -1,4 +1,3 @@ - --- title: 'SignalGP-Lite: Event Driven Genetic Programming Library for Large-Scale Artificial Life Applications' tags: @@ -27,13 +26,13 @@ bibliography: paper.bib Event-driven genetic programming representations have been shown to outperform traditional imperative representations on interaction-intensive problems. These representations organize genome content into modules that are triggered in response to enviromental signals, simplifying simulation design and implementation. Existing work developing event-driven genetic programming methodology has largely used the SignalGP library, which caters to traditional program synthesis applications. -The signalgp-lite library enables larger-scale artificial life experiments with streamlined agents by reducing control flow overhead and trading run-time flexibility for better performance due to compile-time configuration. +The SignalGP-Lite library enables larger-scale artificial life experiments with streamlined agents by reducing control flow overhead and trading run-time flexibility for better performance due to compile-time configuration. Here, we report benchmarking experiments that show an 8x to 30x speedup. We also report solution quality equivalent to SignalGP on two benchmark problems originally developed to test the ability of evolved programs to respond to a large number of signals and to modulate signal response based on context. # Summary -`signalgp-lite` is a C++ library for event-driven genetic programming. +`SignalGP-Lite` is a C++ library for event-driven genetic programming. Unlike the traditional imperative genetic programming paradigm, where a single chain of execution directly manages every aspect of the program, event-driven genetic programs trigger event handlers (i.e., program modules) in response to signals that are generated internally, externally from other agents, or externally from the environment. Event-driven representation outperforms traditional imperative genetic programming on interaction intensive problems where the simulation must handle inputs from the enviroment or other organisms, as is the case in some genetic programming contexts and many artifical life simulations. @@ -46,18 +45,18 @@ Large population sizes are essential to studying phenomena such as ecologies, th In conjunction with parallel and distributied computing, computational efficiency is crucial to enabling larger-scale artificial life situations. SignalGP is a genetic programming framework that sets out to simplify and streamline genetic programming development by the use of an event-driven paradigm where program modules are triggered in response to signals from the enviroment. -In comparison to SignalGP, which was designed to target generic genetic programming problems, `signalgp-lite` fills a niche for interaction-heavy genetic programming applications that can tolerate trading flexible customization at runtime for a considerable speedup. -Since the simulation parameters of many artificial life experiments need not change during execution, they are a clear candidate for using `signalgp-lite`. +In comparison to SignalGP, which was designed to target generic genetic programming problems, `SignalGP-Lite` fills a niche for interaction-heavy genetic programming applications that can tolerate trading flexible customization at runtime for a considerable speedup. +Since the simulation parameters of many artificial life experiments need not change during execution, they are a clear candidate for using `SignalGP-Lite`. The library has enabled order-of-magnitude scale-up of existing artificial life experiments studying the evolution of multicelularity; we anticipate it will also enable novel work in other artificial life and Genetic Programming contexts. ## Execution Speed Benchmarking -We performed a set of microbenchmarks to quantify the effectiveness of `signalgp-lite`'s optimizations in accelerating execution of event-driven genetic programs. +We performed a set of microbenchmarks to quantify the effectiveness of `SignalGP-Lite`'s optimizations in accelerating execution of event-driven genetic programs. Hardware caching size profoundly affects memory access time, which is key to computational performance [@skadron1999branch]. -In order to determine the relative perfomance of the SignalGP and `signalgp-lite` across cache levels, we benchmarked over different orders of magnitude of memory loads by varying agent counts between from 1 and 32768 (Supplementary Table \ref{raw-timings-table}). +In order to determine the relative perfomance of the SignalGP and `SignalGP-Lite` across cache levels, we benchmarked over different orders of magnitude of memory loads by varying agent counts between from 1 and 32768 (Supplementary Table \ref{raw-timings-table}). Supplementary \autoref{fig:raw-timings} shows raw wall-clock timings. We performed five microbenchmark experiments, reported below, in order to isolate how different aspects of the library design influenced performance. @@ -71,35 +70,35 @@ The 1x wall speedup (\autoref{fig:bench-wall}) confirms that further results are ### nop -The `nop` instruction is benchmarked directly, being the only call measured inside the benchmarking loop. This isolates the relative performance impact of `signalgp-lite`'s byte-code interpreter compared to SignalGP's lambda-based instructions. +The `nop` instruction is benchmarked directly, being the only call measured inside the benchmarking loop. This isolates the relative performance impact of `SignalGP-Lite`'s byte-code interpreter compared to SignalGP's lambda-based instructions. -We observe an 8x to 30x speedup under `signalgp-lite` (\autoref{fig:bench-wall}). +We observe an 8x to 30x speedup under `SignalGP-Lite` (\autoref{fig:bench-wall}). ### arithmetic -Benchmarking arithmetic operations incorporates the performance effect of `signalgp-lite`'s fixed-length array registers compared to SignalGP's variable-length vector registers. +Benchmarking arithmetic operations incorporates the performance effect of `SignalGP-Lite`'s fixed-length array registers compared to SignalGP's variable-length vector registers. This compile-time optimization strimlines register access at the cost of the ability to change the number of registers on the fly. \autoref{fig:bench-wall} shows that incorporating this trade-off increases speedup to 20x to 50x. ### complete -In order to improve performance, `signalgp-lite` omits a function stack and implements inner loops and conditionals in terms of tagged `jump` instructions. +In order to improve performance, `SignalGP-Lite` omits a function stack and implements inner loops and conditionals in terms of tagged `jump` instructions. The complete benchmark adds control flow to the prior benchmarks' instruction set. -`signalgp-lite`'s stripped-down control flow model increases speedup to 30x to 55x compared to vanilla SignalGP. +`SignalGP-Lite`'s stripped-down control flow model increases speedup to 30x to 55x compared to vanilla SignalGP. ### sans_regulation -Regulation operations allow SignalGP and `signalgp-lite` programs to adjust which program modules are expressed. Since this invalidates tag-match caches, we wanted to measure changes both with and without regulation. +Regulation operations allow SignalGP and `SignalGP-Lite` programs to adjust which program modules are expressed. Since this invalidates tag-match caches, we wanted to measure changes both with and without regulation. This benchmark measures the complete instruction set without regulation-specific instructions ([list them here]). -As shown on \autoref{fig:bench-wall}, this yields a 35x to 47x speed-up. This means that `signalgp-lite` offers performance improvements even on simulations that do not heavily depend on regulation. +As shown on \autoref{fig:bench-wall}, this yields a 35x to 47x speed-up. This means that `SignalGP-Lite` offers performance improvements even on simulations that do not heavily depend on regulation. # Test Problem Benchmarking -Since `signalgp-lite` is intended to serve as a specialized alternative to the original SignalGP for certain artifical life applications, it must match SignalGP performance on benchmarks measuring responsitivy and plasticity. -To verify `signalgp-lite`'s aptitude, we replicated two experiments from two SignalGP papers, reported below [@lalejini2018evolving][@lalejini_tag-based_2021]. +Since `SignalGP-Lite` is intended to serve as a specialized alternative to the original SignalGP for certain artifical life applications, it must match SignalGP performance on benchmarks measuring responsitivy and plasticity. +To verify `SignalGP-Lite`'s aptitude, we replicated two experiments from two SignalGP papers, reported below [@lalejini2018evolving][@lalejini_tag-based_2021]. ## Changing Enviroment Problem @@ -108,7 +107,7 @@ The Changing Enviroment Problem dispatched K = 2, 4, 8, or 16 mutually-exclusive A total of 100 replicates were evolved for up to 10,000 generations. All 100 organisms were chosen using the elite and roulette selection schemes. \autoref{fig:tts-changing} shows the number of generations elapsed before a full solution was found. -`signalgp-lite` evolved full solutions to each problem within 3,500 updates in all 100 tested replicates. +`SignalGP-Lite` evolved full solutions to each problem within 3,500 updates in all 100 tested replicates. In the `K=16` case, we achieved a superior 100% signal reproduction rate compared to an average of 32% on the original SignalGP implementation (Figure 2 in @lalejini2018evolving). We suspect this improvement occured due to differences in how mutation, tag matching, and program initialization were performed, rather than an intrinsic difference between the libraries. @@ -130,12 +129,12 @@ In order to save resources and computing time, as soon as a replicate evolved a All RNG instructions were disabled to ensure that a solution was not reached by chance. \autoref{fig:tts-context} shows the number of generations elapsed before a full solution was found. -`signalgp-lite` evolved full solutions in half as many generations compared to SignalGP when regulation was enabled. -Moreover, fewer replicates failed to reach a full solution in 10000 generations under `signalgp-lite`. With regulation disabled, however, the performance of both libraries was similar. +`SignalGP-Lite` evolved full solutions in half as many generations compared to SignalGP when regulation was enabled. +Moreover, fewer replicates failed to reach a full solution in 10000 generations under `SignalGP-Lite`. With regulation disabled, however, the performance of both libraries was similar. # Projects Using the Sofrware -`signalgp-lite` is used in DISHTINY, a digital framework for studying organism multicelularity. +`SignalGP-Lite` is used in DISHTINY, a digital framework for studying organism multicelularity. # Figures From 48aaaa347a85ff4487d39e1b9b18d43b23724edf Mon Sep 17 00:00:00 2001 From: Matthew Andres Moreno Date: Thu, 15 Jul 2021 15:07:24 -0400 Subject: [PATCH 068/113] Strip inline code blocks on SignalGP-Lite --- joss/paper.md | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index 8133ef0e..b9a93de9 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -32,7 +32,7 @@ We also report solution quality equivalent to SignalGP on two benchmark problems # Summary -`SignalGP-Lite` is a C++ library for event-driven genetic programming. +SignalGP-Lite is a C++ library for event-driven genetic programming. Unlike the traditional imperative genetic programming paradigm, where a single chain of execution directly manages every aspect of the program, event-driven genetic programs trigger event handlers (i.e., program modules) in response to signals that are generated internally, externally from other agents, or externally from the environment. Event-driven representation outperforms traditional imperative genetic programming on interaction intensive problems where the simulation must handle inputs from the enviroment or other organisms, as is the case in some genetic programming contexts and many artifical life simulations. @@ -45,18 +45,18 @@ Large population sizes are essential to studying phenomena such as ecologies, th In conjunction with parallel and distributied computing, computational efficiency is crucial to enabling larger-scale artificial life situations. SignalGP is a genetic programming framework that sets out to simplify and streamline genetic programming development by the use of an event-driven paradigm where program modules are triggered in response to signals from the enviroment. -In comparison to SignalGP, which was designed to target generic genetic programming problems, `SignalGP-Lite` fills a niche for interaction-heavy genetic programming applications that can tolerate trading flexible customization at runtime for a considerable speedup. -Since the simulation parameters of many artificial life experiments need not change during execution, they are a clear candidate for using `SignalGP-Lite`. +In comparison to SignalGP, which was designed to target generic genetic programming problems, SignalGP-Lite fills a niche for interaction-heavy genetic programming applications that can tolerate trading flexible customization at runtime for a considerable speedup. +Since the simulation parameters of many artificial life experiments need not change during execution, they are a clear candidate for using SignalGP-Lite. The library has enabled order-of-magnitude scale-up of existing artificial life experiments studying the evolution of multicelularity; we anticipate it will also enable novel work in other artificial life and Genetic Programming contexts. ## Execution Speed Benchmarking -We performed a set of microbenchmarks to quantify the effectiveness of `SignalGP-Lite`'s optimizations in accelerating execution of event-driven genetic programs. +We performed a set of microbenchmarks to quantify the effectiveness of SignalGP-Lite's optimizations in accelerating execution of event-driven genetic programs. Hardware caching size profoundly affects memory access time, which is key to computational performance [@skadron1999branch]. -In order to determine the relative perfomance of the SignalGP and `SignalGP-Lite` across cache levels, we benchmarked over different orders of magnitude of memory loads by varying agent counts between from 1 and 32768 (Supplementary Table \ref{raw-timings-table}). +In order to determine the relative perfomance of the SignalGP and SignalGP-Lite across cache levels, we benchmarked over different orders of magnitude of memory loads by varying agent counts between from 1 and 32768 (Supplementary Table \ref{raw-timings-table}). Supplementary \autoref{fig:raw-timings} shows raw wall-clock timings. We performed five microbenchmark experiments, reported below, in order to isolate how different aspects of the library design influenced performance. @@ -70,35 +70,35 @@ The 1x wall speedup (\autoref{fig:bench-wall}) confirms that further results are ### nop -The `nop` instruction is benchmarked directly, being the only call measured inside the benchmarking loop. This isolates the relative performance impact of `SignalGP-Lite`'s byte-code interpreter compared to SignalGP's lambda-based instructions. +The `nop` instruction is benchmarked directly, being the only call measured inside the benchmarking loop. This isolates the relative performance impact of SignalGP-Lite's byte-code interpreter compared to SignalGP's lambda-based instructions. -We observe an 8x to 30x speedup under `SignalGP-Lite` (\autoref{fig:bench-wall}). +We observe an 8x to 30x speedup under SignalGP-Lite (\autoref{fig:bench-wall}). ### arithmetic -Benchmarking arithmetic operations incorporates the performance effect of `SignalGP-Lite`'s fixed-length array registers compared to SignalGP's variable-length vector registers. +Benchmarking arithmetic operations incorporates the performance effect of SignalGP-Lite's fixed-length array registers compared to SignalGP's variable-length vector registers. This compile-time optimization strimlines register access at the cost of the ability to change the number of registers on the fly. \autoref{fig:bench-wall} shows that incorporating this trade-off increases speedup to 20x to 50x. ### complete -In order to improve performance, `SignalGP-Lite` omits a function stack and implements inner loops and conditionals in terms of tagged `jump` instructions. +In order to improve performance, SignalGP-Lite omits a function stack and implements inner loops and conditionals in terms of tagged `jump` instructions. The complete benchmark adds control flow to the prior benchmarks' instruction set. -`SignalGP-Lite`'s stripped-down control flow model increases speedup to 30x to 55x compared to vanilla SignalGP. +SignalGP-Lite's stripped-down control flow model increases speedup to 30x to 55x compared to vanilla SignalGP. ### sans_regulation -Regulation operations allow SignalGP and `SignalGP-Lite` programs to adjust which program modules are expressed. Since this invalidates tag-match caches, we wanted to measure changes both with and without regulation. +Regulation operations allow SignalGP and SignalGP-Lite programs to adjust which program modules are expressed. Since this invalidates tag-match caches, we wanted to measure changes both with and without regulation. This benchmark measures the complete instruction set without regulation-specific instructions ([list them here]). -As shown on \autoref{fig:bench-wall}, this yields a 35x to 47x speed-up. This means that `SignalGP-Lite` offers performance improvements even on simulations that do not heavily depend on regulation. +As shown on \autoref{fig:bench-wall}, this yields a 35x to 47x speed-up. This means that SignalGP-Lite offers performance improvements even on simulations that do not heavily depend on regulation. # Test Problem Benchmarking -Since `SignalGP-Lite` is intended to serve as a specialized alternative to the original SignalGP for certain artifical life applications, it must match SignalGP performance on benchmarks measuring responsitivy and plasticity. -To verify `SignalGP-Lite`'s aptitude, we replicated two experiments from two SignalGP papers, reported below [@lalejini2018evolving][@lalejini_tag-based_2021]. +Since SignalGP-Lite is intended to serve as a specialized alternative to the original SignalGP for certain artifical life applications, it must match SignalGP performance on benchmarks measuring responsitivy and plasticity. +To verify SignalGP-Lite's aptitude, we replicated two experiments from two SignalGP papers, reported below [@lalejini2018evolving][@lalejini_tag-based_2021]. ## Changing Enviroment Problem @@ -107,7 +107,7 @@ The Changing Enviroment Problem dispatched K = 2, 4, 8, or 16 mutually-exclusive A total of 100 replicates were evolved for up to 10,000 generations. All 100 organisms were chosen using the elite and roulette selection schemes. \autoref{fig:tts-changing} shows the number of generations elapsed before a full solution was found. -`SignalGP-Lite` evolved full solutions to each problem within 3,500 updates in all 100 tested replicates. +SignalGP-Lite evolved full solutions to each problem within 3,500 updates in all 100 tested replicates. In the `K=16` case, we achieved a superior 100% signal reproduction rate compared to an average of 32% on the original SignalGP implementation (Figure 2 in @lalejini2018evolving). We suspect this improvement occured due to differences in how mutation, tag matching, and program initialization were performed, rather than an intrinsic difference between the libraries. @@ -129,12 +129,12 @@ In order to save resources and computing time, as soon as a replicate evolved a All RNG instructions were disabled to ensure that a solution was not reached by chance. \autoref{fig:tts-context} shows the number of generations elapsed before a full solution was found. -`SignalGP-Lite` evolved full solutions in half as many generations compared to SignalGP when regulation was enabled. -Moreover, fewer replicates failed to reach a full solution in 10000 generations under `SignalGP-Lite`. With regulation disabled, however, the performance of both libraries was similar. +SignalGP-Lite evolved full solutions in half as many generations compared to SignalGP when regulation was enabled. +Moreover, fewer replicates failed to reach a full solution in 10000 generations under SignalGP-Lite. With regulation disabled, however, the performance of both libraries was similar. # Projects Using the Sofrware -`SignalGP-Lite` is used in DISHTINY, a digital framework for studying organism multicelularity. +SignalGP-Lite is used in DISHTINY, a digital framework for studying organism multicelularity. # Figures From 4d8232bdb28a96df5b7ff5e45840de7192ae3d5f Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Fri, 16 Jul 2021 12:49:01 -0400 Subject: [PATCH 069/113] Environmental has an n apparently --- joss/paper.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index b9a93de9..b996109a 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -24,7 +24,7 @@ bibliography: paper.bib # Abstract Event-driven genetic programming representations have been shown to outperform traditional imperative representations on interaction-intensive problems. -These representations organize genome content into modules that are triggered in response to enviromental signals, simplifying simulation design and implementation. +These representations organize genome content into modules that are triggered in response to environmental signals, simplifying simulation design and implementation. Existing work developing event-driven genetic programming methodology has largely used the SignalGP library, which caters to traditional program synthesis applications. The SignalGP-Lite library enables larger-scale artificial life experiments with streamlined agents by reducing control flow overhead and trading run-time flexibility for better performance due to compile-time configuration. Here, we report benchmarking experiments that show an 8x to 30x speedup. @@ -61,7 +61,7 @@ Supplementary \autoref{fig:raw-timings} shows raw wall-clock timings. We performed five microbenchmark experiments, reported below, in order to isolate how different aspects of the library design influenced performance. -All benchmarks in this section were realized using Google Benchmark version 1.5.2-1. +All benchmarks in this section were performed using Google Benchmark version 1.5.2-1. ### control From 0d5548594dd447dc2ccdbdc20df056c87aadb48a Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Fri, 16 Jul 2021 13:01:10 -0400 Subject: [PATCH 070/113] Spellcheck --- joss/paper.md | 37 ++++++++++++++++++------------------- 1 file changed, 18 insertions(+), 19 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index b996109a..ccf02dac 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -34,17 +34,17 @@ We also report solution quality equivalent to SignalGP on two benchmark problems SignalGP-Lite is a C++ library for event-driven genetic programming. Unlike the traditional imperative genetic programming paradigm, where a single chain of execution directly manages every aspect of the program, event-driven genetic programs trigger event handlers (i.e., program modules) in response to signals that are generated internally, externally from other agents, or externally from the environment. -Event-driven representation outperforms traditional imperative genetic programming on interaction intensive problems where the simulation must handle inputs from the enviroment or other organisms, as is the case in some genetic programming contexts and many artifical life simulations. +Event-driven representation outperforms traditional imperative genetic programming on interaction intensive problems where the simulation must handle inputs from the environment or other organisms, as is the case in some genetic programming contexts and many artificial life simulations. # Statement of need -Despite being able to simulate evolution with much faster generational turnover than is possible in biological experiements [@ofria2004avida], the scale of artifical life populations is profoundly limited by available computational resources [@Moreno_2020]. -Large population sizes are essential to studying phenomena such as ecologies, the transition to multicelularity, and rare events in evolution. -In conjunction with parallel and distributied computing, computational efficiency is crucial to enabling larger-scale artificial life situations. +[introduce artificial life] Despite being able to simulate evolution with much faster generational turnover than is possible in biological experiments [@ofria2004avida], the scale of artificial life populations is profoundly limited by available computational resources [@Moreno_2020]. +Large population sizes are essential to studying fundamental evolutionary phenomena such as ecologies, the transition to multicelularity, and rare events in evolution. +In conjunction with parallel and distributed computing, computational efficiency is crucial to achieving larger-scale artificial life situations. -SignalGP is a genetic programming framework that sets out to simplify and streamline genetic programming development by the use of an event-driven paradigm where program modules are triggered in response to signals from the enviroment. +SignalGP is a genetic programming framework that sets out to simplify and streamline genetic programming development by the use of an event-driven paradigm where program modules are triggered in response to signals from the environment. In comparison to SignalGP, which was designed to target generic genetic programming problems, SignalGP-Lite fills a niche for interaction-heavy genetic programming applications that can tolerate trading flexible customization at runtime for a considerable speedup. Since the simulation parameters of many artificial life experiments need not change during execution, they are a clear candidate for using SignalGP-Lite. @@ -53,20 +53,17 @@ The library has enabled order-of-magnitude scale-up of existing artificial life ## Execution Speed Benchmarking -We performed a set of microbenchmarks to quantify the effectiveness of SignalGP-Lite's optimizations in accelerating execution of event-driven genetic programs. +We performed a set of microbenchmarks to quantify the effectiveness of SignalGP-Lite's optimizations in accelerating evaluation of event-driven genetic programs. Hardware caching size profoundly affects memory access time, which is key to computational performance [@skadron1999branch]. -In order to determine the relative perfomance of the SignalGP and SignalGP-Lite across cache levels, we benchmarked over different orders of magnitude of memory loads by varying agent counts between from 1 and 32768 (Supplementary Table \ref{raw-timings-table}). -Supplementary \autoref{fig:raw-timings} shows raw wall-clock timings. +In order to determine the relative performance of the SignalGP and SignalGP-Lite across cache levels, we benchmarked over different orders of magnitude of memory loads by varying agent counts between from 1 and 32768 (Supplementary Table \ref{raw-timings-table}). -We performed five microbenchmark experiments, reported below, in order to isolate how different aspects of the library design influenced performance. - -All benchmarks in this section were performed using Google Benchmark version 1.5.2-1. +We performed five microbenchmark experiments, reported below, in order to isolate how different aspects of the library design influenced performance. Supplementary \autoref{fig:raw-timings} shows raw wall-clock timings. ### control The control involves importing the library to benchmark and measuring the execution time of an empty loop. This experiment verifies the validity of our benchmarking process. -The 1x wall speedup (\autoref{fig:bench-wall}) confirms that further results are not unduely skewed by our experimental aparatus. +The 1x wall speedup (\autoref{fig:bench-wall}) confirms that further results are not unduly skewed by our experimental apparatus. ### nop @@ -77,7 +74,7 @@ We observe an 8x to 30x speedup under SignalGP-Lite (\autoref{fig:bench-wall}). ### arithmetic Benchmarking arithmetic operations incorporates the performance effect of SignalGP-Lite's fixed-length array registers compared to SignalGP's variable-length vector registers. -This compile-time optimization strimlines register access at the cost of the ability to change the number of registers on the fly. +This compile-time optimization streamlines register access at the cost of the ability to change the number of registers on the fly. \autoref{fig:bench-wall} shows that incorporating this trade-off increases speedup to 20x to 50x. @@ -97,12 +94,12 @@ As shown on \autoref{fig:bench-wall}, this yields a 35x to 47x speed-up. This me # Test Problem Benchmarking -Since SignalGP-Lite is intended to serve as a specialized alternative to the original SignalGP for certain artifical life applications, it must match SignalGP performance on benchmarks measuring responsitivy and plasticity. +Since SignalGP-Lite is intended to serve as a specialized alternative to the original SignalGP for certain artificial life applications, it must match SignalGP performance on benchmarks measuring responsivity and plasticity. To verify SignalGP-Lite's aptitude, we replicated two experiments from two SignalGP papers, reported below [@lalejini2018evolving][@lalejini_tag-based_2021]. -## Changing Enviroment Problem +## Changing Environment Problem -The Changing Enviroment Problem dispatched K = 2, 4, 8, or 16 mutually-exclusive enviromental signals with randomly genereated tags. Organisms were tasked to respond to each signal with a unique response instruction. +The Changing Environment Problem dispatched K = 2, 4, 8, or 16 mutually-exclusive environmental signals with randomly generated tags. Organisms were tasked to respond to each signal with a unique response instruction. A total of 100 replicates were evolved for up to 10,000 generations. All 100 organisms were chosen using the elite and roulette selection schemes. @@ -110,12 +107,12 @@ All 100 organisms were chosen using the elite and roulette selection schemes. SignalGP-Lite evolved full solutions to each problem within 3,500 updates in all 100 tested replicates. In the `K=16` case, we achieved a superior 100% signal reproduction rate compared to an average of 32% on the original SignalGP implementation (Figure 2 in @lalejini2018evolving). -We suspect this improvement occured due to differences in how mutation, tag matching, and program initialization were performed, rather than an intrinsic difference between the libraries. +We suspect this improvement occurred due to differences in how mutation, tag matching, and program initialization were performed, rather than an intrinsic difference between the libraries. ## Contextual Signal Problem The contextual signal problem assesses the ability of evolving programs to maintain memory of previously encountered signals. -Programs must remember an initial signal (i.e., the "context") in order to respond appropiately to a second signal. +Programs must remember an initial signal (i.e., the "context") in order to respond appropriately to a second signal. Each possible unordered input signal pair was assigned a unique response that then had to be performed by the organism. A total of 16 input signal pairs and 4 response signals were tested. @@ -132,7 +129,7 @@ All RNG instructions were disabled to ensure that a solution was not reached by SignalGP-Lite evolved full solutions in half as many generations compared to SignalGP when regulation was enabled. Moreover, fewer replicates failed to reach a full solution in 10000 generations under SignalGP-Lite. With regulation disabled, however, the performance of both libraries was similar. -# Projects Using the Sofrware +# Projects Using the Software SignalGP-Lite is used in DISHTINY, a digital framework for studying organism multicelularity. @@ -169,6 +166,8 @@ Any opinions, findings, and conclusions or recommendations expressed in this mat # Supplementary Material +All benchmarks reported in this section were performed using Google Benchmark version 1.5.2-1. + ![Maximum fitness wrt. updates, with standard deviation confidence intervals. This is because, due to large number of datapoints, computing 95% CI takes a non-insignificant amount of time.\label{fig:max-fitness-sd}](figures/max-fitness-sd.png) ![Filtered maximum fitness wrt. updates, with 95% confidence intervals. Data has been filtered logarithmically.\label{fig:max-fitness-sd}](figures/max-fitness-log2.png) From eb6f13f3324c64d4934e58fdab45c48aecb9e584 Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Fri, 16 Jul 2021 13:05:06 -0400 Subject: [PATCH 071/113] uncapitalize GP and alife --- joss/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss/paper.md b/joss/paper.md index ccf02dac..b52ef37d 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -48,7 +48,7 @@ SignalGP is a genetic programming framework that sets out to simplify and stream In comparison to SignalGP, which was designed to target generic genetic programming problems, SignalGP-Lite fills a niche for interaction-heavy genetic programming applications that can tolerate trading flexible customization at runtime for a considerable speedup. Since the simulation parameters of many artificial life experiments need not change during execution, they are a clear candidate for using SignalGP-Lite. -The library has enabled order-of-magnitude scale-up of existing artificial life experiments studying the evolution of multicelularity; we anticipate it will also enable novel work in other artificial life and Genetic Programming contexts. +The library has enabled order-of-magnitude scale-up of existing artificial life experiments studying the evolution of multicelularity; we anticipate it will also enable novel work in other artificial life and genetic programming contexts. ## Execution Speed Benchmarking From 93ddf579ad9f594a9357ad937f56125c3faf2452 Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Fri, 16 Jul 2021 13:24:41 -0400 Subject: [PATCH 072/113] Small changes --- joss/paper.md | 53 ++++++++++++++++++++++++++------------------------- 1 file changed, 27 insertions(+), 26 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index b52ef37d..98df906b 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -32,6 +32,8 @@ We also report solution quality equivalent to SignalGP on two benchmark problems # Summary +SignalGP is a genetic programming framework that sets out to simplify and streamline genetic programming development by the use of an event-driven paradigm where program modules are triggered in response to signals from the environment. + SignalGP-Lite is a C++ library for event-driven genetic programming. Unlike the traditional imperative genetic programming paradigm, where a single chain of execution directly manages every aspect of the program, event-driven genetic programs trigger event handlers (i.e., program modules) in response to signals that are generated internally, externally from other agents, or externally from the environment. Event-driven representation outperforms traditional imperative genetic programming on interaction intensive problems where the simulation must handle inputs from the environment or other organisms, as is the case in some genetic programming contexts and many artificial life simulations. @@ -41,10 +43,9 @@ Event-driven representation outperforms traditional imperative genetic programmi # Statement of need [introduce artificial life] Despite being able to simulate evolution with much faster generational turnover than is possible in biological experiments [@ofria2004avida], the scale of artificial life populations is profoundly limited by available computational resources [@Moreno_2020]. -Large population sizes are essential to studying fundamental evolutionary phenomena such as ecologies, the transition to multicelularity, and rare events in evolution. +Large population sizes are essential to studying fundamental evolutionary phenomena such as ecologies, the transition to multicelularity, and rare events. In conjunction with parallel and distributed computing, computational efficiency is crucial to achieving larger-scale artificial life situations. -SignalGP is a genetic programming framework that sets out to simplify and streamline genetic programming development by the use of an event-driven paradigm where program modules are triggered in response to signals from the environment. In comparison to SignalGP, which was designed to target generic genetic programming problems, SignalGP-Lite fills a niche for interaction-heavy genetic programming applications that can tolerate trading flexible customization at runtime for a considerable speedup. Since the simulation parameters of many artificial life experiments need not change during execution, they are a clear candidate for using SignalGP-Lite. @@ -56,74 +57,74 @@ The library has enabled order-of-magnitude scale-up of existing artificial life We performed a set of microbenchmarks to quantify the effectiveness of SignalGP-Lite's optimizations in accelerating evaluation of event-driven genetic programs. Hardware caching size profoundly affects memory access time, which is key to computational performance [@skadron1999branch]. -In order to determine the relative performance of the SignalGP and SignalGP-Lite across cache levels, we benchmarked over different orders of magnitude of memory loads by varying agent counts between from 1 and 32768 (Supplementary Table \ref{raw-timings-table}). +In order to determine the relative performance of the SignalGP and SignalGP-Lite across cache levels, we benchmarked over different orders of magnitude of memory load by varying agent counts between from 1 and 32768 (Supplementary Table \ref{raw-timings-table}). We performed five microbenchmark experiments, reported below, in order to isolate how different aspects of the library design influenced performance. Supplementary \autoref{fig:raw-timings} shows raw wall-clock timings. ### control -The control involves importing the library to benchmark and measuring the execution time of an empty loop. This experiment verifies the validity of our benchmarking process. -The 1x wall speedup (\autoref{fig:bench-wall}) confirms that further results are not unduly skewed by our experimental apparatus. +The control involves importing the library to benchmark, initializing agents, and then measuring the execution time of an empty loop. This experiment verifies the validity of our benchmarking process. +The 1x wall speedup (\autoref{fig:bench-wall}) confirms that further results are not inadvertently skewed by our experimental apparatus. ### nop -The `nop` instruction is benchmarked directly, being the only call measured inside the benchmarking loop. This isolates the relative performance impact of SignalGP-Lite's byte-code interpreter compared to SignalGP's lambda-based instructions. +[generated program with all NOPs] The `nop` instruction is benchmarked directly, being the only call measured inside the benchmarking loop. This isolates the relative performance impact of SignalGP-Lite's byte-code interpreter compared to SignalGP's lambda-based instructions. We observe an 8x to 30x speedup under SignalGP-Lite (\autoref{fig:bench-wall}). ### arithmetic -Benchmarking arithmetic operations incorporates the performance effect of SignalGP-Lite's fixed-length array registers compared to SignalGP's variable-length vector registers. +Adding arithmetic operations such as `add`, `subtract`, `multiply`, and `divide`, to our benchmark incorporates the performance effect of SignalGP-Lite's fixed-length array registers compared to SignalGP's variable-length vector registers. This compile-time optimization streamlines register access at the cost of the ability to change the number of registers on the fly. \autoref{fig:bench-wall} shows that incorporating this trade-off increases speedup to 20x to 50x. ### complete -In order to improve performance, SignalGP-Lite omits a function stack and implements inner loops and conditionals in terms of tagged `jump` instructions. +In order to improve performance, SignalGP-Lite omits a function stack and implements inner loops and conditionals in terms of tagged `jump` instructions instead of nested code blocks. The complete benchmark adds control flow to the prior benchmarks' instruction set. -SignalGP-Lite's stripped-down control flow model increases speedup to 30x to 55x compared to vanilla SignalGP. +SignalGP-Lite's stripped-down control flow model increases speedup to 30x to 55x compared to vanilla SignalGP (\autoref{fig:bench-wall}). ### sans_regulation -Regulation operations allow SignalGP and SignalGP-Lite programs to adjust which program modules are expressed. Since this invalidates tag-match caches, we wanted to measure changes both with and without regulation. -This benchmark measures the complete instruction set without regulation-specific instructions ([list them here]). +Regulation operations allow SignalGP and SignalGP-Lite programs to adjust which program modules are expressed. Since this invalidates tag-match caches, we wanted to measure timings without regulation enabled. +This benchmark measures the complete instruction set with regulation-related instructions excluded. -As shown on \autoref{fig:bench-wall}, this yields a 35x to 47x speed-up. This means that SignalGP-Lite offers performance improvements even on simulations that do not heavily depend on regulation. +As shown on \autoref{fig:bench-wall}, this yields a 35x to 47x speed-up. As such, SignalGP-Lite offers performance improvements even on simulations that do not heavily depend on regulation. # Test Problem Benchmarking -Since SignalGP-Lite is intended to serve as a specialized alternative to the original SignalGP for certain artificial life applications, it must match SignalGP performance on benchmarks measuring responsivity and plasticity. -To verify SignalGP-Lite's aptitude, we replicated two experiments from two SignalGP papers, reported below [@lalejini2018evolving][@lalejini_tag-based_2021]. +In order to successfully server as a specialized alternative to the original SignalGP for certain artificial life applications, SignalGP-Lite must match SignalGP's performance on benchmarks measuring responsivity and plasticity. +To verify SignalGP-Lite's aptitude on these tests, we replicated two canonical SignalGP experiments, reported below [@lalejini2018evolving, @lalejini_tag-based_2021]. ## Changing Environment Problem -The Changing Environment Problem dispatched K = 2, 4, 8, or 16 mutually-exclusive environmental signals with randomly generated tags. Organisms were tasked to respond to each signal with a unique response instruction. +The Changing Environment Problem dispatched K = 2, 4, 8, or 16 mutually-exclusive environmental signals with randomly generated tags. Organisms were tasked to respond to each signal with a unique response instruction [@lalejini2018evolving]. -A total of 100 replicates were evolved for up to 10,000 generations. +A total of 100 replicate populations of [xxxx] individuals were evolved for up to 10,000 generations. All 100 organisms were chosen using the elite and roulette selection schemes. \autoref{fig:tts-changing} shows the number of generations elapsed before a full solution was found. SignalGP-Lite evolved full solutions to each problem within 3,500 updates in all 100 tested replicates. -In the `K=16` case, we achieved a superior 100% signal reproduction rate compared to an average of 32% on the original SignalGP implementation (Figure 2 in @lalejini2018evolving). +In the `K=16` case, we achieved a superior 100% signal reproduction rate compared to an average of 32% on the original SignalGP implementation (@lalejini2018evolving Figure 2). We suspect this improvement occurred due to differences in how mutation, tag matching, and program initialization were performed, rather than an intrinsic difference between the libraries. ## Contextual Signal Problem -The contextual signal problem assesses the ability of evolving programs to maintain memory of previously encountered signals. -Programs must remember an initial signal (i.e., the "context") in order to respond appropriately to a second signal. +The Contextual Signal Problem assesses the ability of evolving programs to maintain memory of previously encountered signals. +Programs must remember an initial signal (i.e., ots "context") in order to respond appropriately to a second signal [@lalejini_tag-based_2021]. -Each possible unordered input signal pair was assigned a unique response that then had to be performed by the organism. -A total of 16 input signal pairs and 4 response signals were tested. -Table 2 in @lalejini2018evolving provides a visual representation of these sequences. +We assigned each possible unordered input signal pair a unique response to then be performed by the organism. +We tested with 16 input signal pairs and 4 response signals. +Table 2 in @lalejini_tag-based_2021 provides a visual representation of these sequences. -A total of XX replicates were evolved for up to 10,000 generations using a 16-way lexicase selection scheme, with each of the input signal pairs serving as a test case. -To evaluate the specific test case, each program was sent the first signal of each test case and given 128 updates to process it. +A total of XX replicates were evolved for up to 10,000 generations using a 16-way lexicase selection scheme, with each of the input signal pairs serving as a test case. [cite OG lexicase paper] +To evaluate each test case, programs were sent the first signal of each test case and given 128 virtual CPU cycles to process it. After this, their internal running modules were reset and the second signal was sent. -After another 128 updates, their response was evaluated. +After another 128 virtual CPU cycles, their response was recorded. In order to save resources and computing time, as soon as a replicate evolved a fully-correct solution, their evolution was halted. -All RNG instructions were disabled to ensure that a solution was not reached by chance. +We excluded RNG operations from the instruction set to ensure that solutions were not reached by chance. \autoref{fig:tts-context} shows the number of generations elapsed before a full solution was found. SignalGP-Lite evolved full solutions in half as many generations compared to SignalGP when regulation was enabled. From a8100b7c57867ad3bdacf522393bc55c171ca83b Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Fri, 16 Jul 2021 17:14:48 -0400 Subject: [PATCH 073/113] Further changes --- joss/paper.md | 40 ++++++++++++++++++++++++++-------------- 1 file changed, 26 insertions(+), 14 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index 98df906b..c5598a54 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -32,12 +32,19 @@ We also report solution quality equivalent to SignalGP on two benchmark problems # Summary -SignalGP is a genetic programming framework that sets out to simplify and streamline genetic programming development by the use of an event-driven paradigm where program modules are triggered in response to signals from the environment. +SignalGP is an imperative genetic programming framework that sets out to simplify and streamline genetic programming development by the use of an event-driven paradigm where program modules are triggered in response to signals from the environment. SignalGP-Lite is a C++ library for event-driven genetic programming. Unlike the traditional imperative genetic programming paradigm, where a single chain of execution directly manages every aspect of the program, event-driven genetic programs trigger event handlers (i.e., program modules) in response to signals that are generated internally, externally from other agents, or externally from the environment. Event-driven representation outperforms traditional imperative genetic programming on interaction intensive problems where the simulation must handle inputs from the environment or other organisms, as is the case in some genetic programming contexts and many artificial life simulations. +In this paper, we will present the performance advantages that SignalGP-Lite has over SignalGP when it comes to artificial life applications, as well as some scenarios where the original implementation has the edge. + +The Execution Speed Benchmarking section sets forth the results of profiling both SignalGP and SignalGP-Lite using synthetic benchmarks. +The Test Problem Benchmarking section compares the solution quality of SignalGP and SignalGP-Lite on real-world genetic programming problems. +[should i talk about the other sections? they are very short!] + + # Statement of need @@ -46,13 +53,13 @@ Event-driven representation outperforms traditional imperative genetic programmi Large population sizes are essential to studying fundamental evolutionary phenomena such as ecologies, the transition to multicelularity, and rare events. In conjunction with parallel and distributed computing, computational efficiency is crucial to achieving larger-scale artificial life situations. -In comparison to SignalGP, which was designed to target generic genetic programming problems, SignalGP-Lite fills a niche for interaction-heavy genetic programming applications that can tolerate trading flexible customization at runtime for a considerable speedup. -Since the simulation parameters of many artificial life experiments need not change during execution, they are a clear candidate for using SignalGP-Lite. +In comparison to SignalGP, which was designed to target generic genetic programming problems, SignalGP-Lite fills a niche for interaction-heavy genetic programming applications that can tolerate less runtime configuration, flexibility, and pared back control flow. +Since the simulation parameters of many artificial life experiments need not change during execution and a more rudimentary approach to control flow is tolerated, they are a clear candidate for using SignalGP-Lite. The library has enabled order-of-magnitude scale-up of existing artificial life experiments studying the evolution of multicelularity; we anticipate it will also enable novel work in other artificial life and genetic programming contexts. -## Execution Speed Benchmarking +# Execution Speed Benchmarking We performed a set of microbenchmarks to quantify the effectiveness of SignalGP-Lite's optimizations in accelerating evaluation of event-driven genetic programs. @@ -61,37 +68,42 @@ In order to determine the relative performance of the SignalGP and SignalGP-Lite We performed five microbenchmark experiments, reported below, in order to isolate how different aspects of the library design influenced performance. Supplementary \autoref{fig:raw-timings} shows raw wall-clock timings. -### control +## control The control involves importing the library to benchmark, initializing agents, and then measuring the execution time of an empty loop. This experiment verifies the validity of our benchmarking process. The 1x wall speedup (\autoref{fig:bench-wall}) confirms that further results are not inadvertently skewed by our experimental apparatus. -### nop +## nop -[generated program with all NOPs] The `nop` instruction is benchmarked directly, being the only call measured inside the benchmarking loop. This isolates the relative performance impact of SignalGP-Lite's byte-code interpreter compared to SignalGP's lambda-based instructions. +A program consisting of just `nop` instructions is generated. This benchmarks the instruction directly, as it is the only call measured inside the benchmarking loop. With this approach, the relative performance impact of SignalGP-Lite's byte-code interpreter can be compared to SignalGP's lambda-based instructions. We observe an 8x to 30x speedup under SignalGP-Lite (\autoref{fig:bench-wall}). +The greatest speedup occurred at 1024 agents. -### arithmetic +## arithmetic Adding arithmetic operations such as `add`, `subtract`, `multiply`, and `divide`, to our benchmark incorporates the performance effect of SignalGP-Lite's fixed-length array registers compared to SignalGP's variable-length vector registers. This compile-time optimization streamlines register access at the cost of the ability to change the number of registers on the fly. \autoref{fig:bench-wall} shows that incorporating this trade-off increases speedup to 20x to 50x. +The greatest speedup increase occurred at 1024 agents. -### complete +## complete In order to improve performance, SignalGP-Lite omits a function stack and implements inner loops and conditionals in terms of tagged `jump` instructions instead of nested code blocks. The complete benchmark adds control flow to the prior benchmarks' instruction set. SignalGP-Lite's stripped-down control flow model increases speedup to 30x to 55x compared to vanilla SignalGP (\autoref{fig:bench-wall}). +The greatest speedup occurred at 32 agents. -### sans_regulation +## sans_regulation Regulation operations allow SignalGP and SignalGP-Lite programs to adjust which program modules are expressed. Since this invalidates tag-match caches, we wanted to measure timings without regulation enabled. This benchmark measures the complete instruction set with regulation-related instructions excluded. -As shown on \autoref{fig:bench-wall}, this yields a 35x to 47x speed-up. As such, SignalGP-Lite offers performance improvements even on simulations that do not heavily depend on regulation. +As shown on \autoref{fig:bench-wall}, this yields a 35x to 47x speed-up. +The biggest speedup occured at 32 agents. +As such, SignalGP-Lite offers performance improvements even on simulations that do not heavily depend on regulation. # Test Problem Benchmarking @@ -113,13 +125,13 @@ We suspect this improvement occurred due to differences in how mutation, tag mat ## Contextual Signal Problem The Contextual Signal Problem assesses the ability of evolving programs to maintain memory of previously encountered signals. -Programs must remember an initial signal (i.e., ots "context") in order to respond appropriately to a second signal [@lalejini_tag-based_2021]. +Programs must remember an initial signal (i.e., its "context") in order to respond appropriately to a second signal [@lalejini_tag-based_2021]. We assigned each possible unordered input signal pair a unique response to then be performed by the organism. We tested with 16 input signal pairs and 4 response signals. Table 2 in @lalejini_tag-based_2021 provides a visual representation of these sequences. -A total of XX replicates were evolved for up to 10,000 generations using a 16-way lexicase selection scheme, with each of the input signal pairs serving as a test case. [cite OG lexicase paper] +A total of XX replicates were evolved for up to 10,000 generations using a 16-way lexicase selection scheme, with each of the input signal pairs serving as a test case [@spector2012assessment]. To evaluate each test case, programs were sent the first signal of each test case and given 128 virtual CPU cycles to process it. After this, their internal running modules were reset and the second signal was sent. After another 128 virtual CPU cycles, their response was recorded. @@ -132,7 +144,7 @@ Moreover, fewer replicates failed to reach a full solution in 10000 generations # Projects Using the Software -SignalGP-Lite is used in DISHTINY, a digital framework for studying organism multicelularity. +SignalGP-Lite is used in DISHTINY, a digital framework for studying organism multicelularity. [cite] # Figures From a4ab9ef20e59f70a62ba18ed2f748d5f2dac9ebb Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Fri, 16 Jul 2021 17:16:02 -0400 Subject: [PATCH 074/113] Add references --- joss/paper.bib | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/joss/paper.bib b/joss/paper.bib index 95d13bb0..d4781679 100644 --- a/joss/paper.bib +++ b/joss/paper.bib @@ -130,4 +130,12 @@ @misc{Moreno_2020 author={Moreno, Matthew A}, year={2020}, month={Jul} -} \ No newline at end of file +} + +@inproceedings{spector2012assessment, + title={Assessment of problem modality by differential performance of lexicase selection in genetic programming: a preliminary report}, + author={Spector, Lee}, + booktitle={Proceedings of the 14th annual conference companion on Genetic and evolutionary computation}, + pages={401--408}, + year={2012} +} From c5b7e56e0788149993fdbb896c3cedc1395bfb5a Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Fri, 16 Jul 2021 17:21:01 -0400 Subject: [PATCH 075/113] More changes --- joss/paper.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index c5598a54..ae3978d4 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -140,11 +140,14 @@ We excluded RNG operations from the instruction set to ensure that solutions wer \autoref{fig:tts-context} shows the number of generations elapsed before a full solution was found. SignalGP-Lite evolved full solutions in half as many generations compared to SignalGP when regulation was enabled. -Moreover, fewer replicates failed to reach a full solution in 10000 generations under SignalGP-Lite. With regulation disabled, however, the performance of both libraries was similar. +Moreover, fewer replicates failed to reach a full solution in 10000 generations under SignalGP-Lite. +With regulation disabled, however, the performance of both libraries was similar. +These results mean that SignalGP-Lite is a valid alternative to SignalGP when it comes to artificial life applications. + # Projects Using the Software -SignalGP-Lite is used in DISHTINY, a digital framework for studying organism multicelularity. [cite] +SignalGP-Lite is used in DISHTINY, a digital framework for studying organism multicelularity [@moreno2019toward]. # Figures From 1cef6919ebfad20d458000c202ac5e69a172e830 Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Fri, 16 Jul 2021 19:03:42 -0400 Subject: [PATCH 076/113] Fix citation --- joss/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss/paper.md b/joss/paper.md index ae3978d4..5daca52a 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -108,7 +108,7 @@ As such, SignalGP-Lite offers performance improvements even on simulations that # Test Problem Benchmarking In order to successfully server as a specialized alternative to the original SignalGP for certain artificial life applications, SignalGP-Lite must match SignalGP's performance on benchmarks measuring responsivity and plasticity. -To verify SignalGP-Lite's aptitude on these tests, we replicated two canonical SignalGP experiments, reported below [@lalejini2018evolving, @lalejini_tag-based_2021]. +To verify SignalGP-Lite's aptitude on these tests, we replicated two canonical SignalGP experiments, reported below [@lalejini2018evolving; @lalejini_tag-based_2021]. ## Changing Environment Problem From a49514774eff30ed67d7798e7767b6229a2a5837 Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Sat, 31 Jul 2021 01:49:10 -0400 Subject: [PATCH 077/113] Alex's feedback --- joss/paper.md | 83 ++++++++++++++++++++++++++++++--------------------- 1 file changed, 49 insertions(+), 34 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index 5daca52a..c4535e76 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -24,7 +24,7 @@ bibliography: paper.bib # Abstract Event-driven genetic programming representations have been shown to outperform traditional imperative representations on interaction-intensive problems. -These representations organize genome content into modules that are triggered in response to environmental signals, simplifying simulation design and implementation. +The event-driven approach organizes genome content into modules that are triggered in response to environmental signals, simplifying simulation design and implementation. Existing work developing event-driven genetic programming methodology has largely used the SignalGP library, which caters to traditional program synthesis applications. The SignalGP-Lite library enables larger-scale artificial life experiments with streamlined agents by reducing control flow overhead and trading run-time flexibility for better performance due to compile-time configuration. Here, we report benchmarking experiments that show an 8x to 30x speedup. @@ -32,41 +32,45 @@ We also report solution quality equivalent to SignalGP on two benchmark problems # Summary -SignalGP is an imperative genetic programming framework that sets out to simplify and streamline genetic programming development by the use of an event-driven paradigm where program modules are triggered in response to signals from the environment. +SignalGP is an existing event-driven genetic programming C++ library well-suited for interaction-heavy program synthesis problems. +SignalGP was instrumental in introducing the event-driven genetic programming paradigm where program modules are triggered in response to signals from the environment. -SignalGP-Lite is a C++ library for event-driven genetic programming. Unlike the traditional imperative genetic programming paradigm, where a single chain of execution directly manages every aspect of the program, event-driven genetic programs trigger event handlers (i.e., program modules) in response to signals that are generated internally, externally from other agents, or externally from the environment. -Event-driven representation outperforms traditional imperative genetic programming on interaction intensive problems where the simulation must handle inputs from the environment or other organisms, as is the case in some genetic programming contexts and many artificial life simulations. +Event-driven representation outperforms traditional imperative genetic programming on interaction intensive problems where the programs must handle inputs from the environment or other organisms, as is the case in some traditional genetic programming contexts and many artificial life simulations. + +SignalGP-Lite is a C++ library for event-driven genetic programming. +In comparison to SignalGP, which is intended for general event-driven genetic programming, SignalGP-Lite is tailored for use in artificial life experiments. -In this paper, we will present the performance advantages that SignalGP-Lite has over SignalGP when it comes to artificial life applications, as well as some scenarios where the original implementation has the edge. +Here, we present the compute time and solution quality comparison of SignalGP-Lite and SignalGP on some scenarios where the original implementation has the edge. -The Execution Speed Benchmarking section sets forth the results of profiling both SignalGP and SignalGP-Lite using synthetic benchmarks. -The Test Problem Benchmarking section compares the solution quality of SignalGP and SignalGP-Lite on real-world genetic programming problems. -[should i talk about the other sections? they are very short!] +In "Execution Speed Benchmarking," we report compute times for both SignalGP and SignalGP-Lite using synthetic benchmarks---benchmarks that designed with reproducibility and accuracy in mind, but that might not reflect real-world problems. +In "Test Problem Benchmarking," we compare solution quality of SignalGP and SignalGP-Lite on synthetic genetic programming problems designed to test responsivity and plasticity. # Statement of need -[introduce artificial life] Despite being able to simulate evolution with much faster generational turnover than is possible in biological experiments [@ofria2004avida], the scale of artificial life populations is profoundly limited by available computational resources [@Moreno_2020]. +[introduce artificial life; Adami] Despite being able to simulate evolution with much faster generational turnover than is possible in biological experiments [@ofria2004avida], the scale of artificial life populations is profoundly limited by available computational resources [@Moreno_2020]. Large population sizes are essential to studying fundamental evolutionary phenomena such as ecologies, the transition to multicelularity, and rare events. In conjunction with parallel and distributed computing, computational efficiency is crucial to achieving larger-scale artificial life situations. -In comparison to SignalGP, which was designed to target generic genetic programming problems, SignalGP-Lite fills a niche for interaction-heavy genetic programming applications that can tolerate less runtime configuration, flexibility, and pared back control flow. -Since the simulation parameters of many artificial life experiments need not change during execution and a more rudimentary approach to control flow is tolerated, they are a clear candidate for using SignalGP-Lite. +SignalGP-Lite is the first implementation of SignalGP accessible to the general public. +Its simplified API is complemented by extensive documentation, continuous integration tests, and benchmarks that measure both real-world workloads as well as individual instructions. +In comparison to SignalGP---which was designed to target generic genetic programming problems---SignalGP-Lite fills a niche for interaction-heavy genetic programming applications that can tolerate less runtime configuration flexibility, and pared-back control flow. +SignalGP-Lite is designed with artificial life experiments in mind, where simulation parameters need not change during execution and a more rudimentary approach to control flow can often be tolerated. The library has enabled order-of-magnitude scale-up of existing artificial life experiments studying the evolution of multicelularity; we anticipate it will also enable novel work in other artificial life and genetic programming contexts. # Execution Speed Benchmarking -We performed a set of microbenchmarks to quantify the effectiveness of SignalGP-Lite's optimizations in accelerating evaluation of event-driven genetic programs. +We performed a set of microbenchmarks---a type of synthetic benchmark that measures individual functions---to quantify the effectiveness of SignalGP-Lite's optimizations in accelerating evaluation of event-driven genetic programs. Hardware caching size profoundly affects memory access time, which is key to computational performance [@skadron1999branch]. -In order to determine the relative performance of the SignalGP and SignalGP-Lite across cache levels, we benchmarked over different orders of magnitude of memory load by varying agent counts between from 1 and 32768 (Supplementary Table \ref{raw-timings-table}). +In order to determine the relative performance of SignalGP and SignalGP-Lite at different cache levels, we benchmarked over different orders of magnitude of memory load by varying the number of virtual CPUs (agent counts) between from 1 and 32768 (Supplementary Table \ref{raw-timings-table}). -We performed five microbenchmark experiments, reported below, in order to isolate how different aspects of the library design influenced performance. Supplementary \autoref{fig:raw-timings} shows raw wall-clock timings. +We performed five microbenchmark experiments, reported below, to isolate how specific aspects of the library design influenced performance. Analysis below focuses on wall time speedup. However, supplementary \autoref{fig:raw-timings} shows raw wall-clock timings for these experiments. ## control @@ -75,47 +79,57 @@ The 1x wall speedup (\autoref{fig:bench-wall}) confirms that further results are ## nop -A program consisting of just `nop` instructions is generated. This benchmarks the instruction directly, as it is the only call measured inside the benchmarking loop. With this approach, the relative performance impact of SignalGP-Lite's byte-code interpreter can be compared to SignalGP's lambda-based instructions. +A program consisting of 100 `nop` instructions is randomly generated. +None of these instructions advance the pRNG engine. +This benchmarks the instruction directly, as it is the only call measured inside the benchmarking loop. +With this approach, the relative performance impact of SignalGP-Lite's byte-code interpreter can be compared to SignalGP's lambda-based instructions. We observe an 8x to 30x speedup under SignalGP-Lite (\autoref{fig:bench-wall}). -The greatest speedup occurred at 1024 agents. +The greatest speedup occurred at a relatively light memory footprint of 1024 agents. ## arithmetic -Adding arithmetic operations such as `add`, `subtract`, `multiply`, and `divide`, to our benchmark incorporates the performance effect of SignalGP-Lite's fixed-length array registers compared to SignalGP's variable-length vector registers. +A program consisting of 100 randomly-chosen arithmetic instructions (`add`, `subtract`, `multiply`, and `divide`) is generated. +This measures the performance impact of SignalGP-Lite's fixed-length array registers compared to SignalGP's variable-length vector registers. This compile-time optimization streamlines register access at the cost of the ability to change the number of registers on the fly. +Since our aim is to only measure the performance effect of this optimization, no `nop` instructions are present in the generated program. \autoref{fig:bench-wall} shows that incorporating this trade-off increases speedup to 20x to 50x. -The greatest speedup increase occurred at 1024 agents. +The greatest speedup increase occurred at a relatively light memory footprint of 1024 agents. ## complete -In order to improve performance, SignalGP-Lite omits a function stack and implements inner loops and conditionals in terms of tagged `jump` instructions instead of nested code blocks. -The complete benchmark adds control flow to the prior benchmarks' instruction set. +The complete benchmark adds control flow instructions to the prior benchmarks' instruction set. +Bitwise and logical operators, comparison instructions, and RNG operations, are also included. +From this complete instruction set, a 100-instruction program is randomly generated. + +The main goal of this benchmark is to determine the performance impact of omitting a function stack and implementing inner loops and conditionals in terms of `jump` instructions instead of nested code blocks. SignalGP-Lite's stripped-down control flow model increases speedup to 30x to 55x compared to vanilla SignalGP (\autoref{fig:bench-wall}). -The greatest speedup occurred at 32 agents. +The greatest speedup occurred at a light memory footprint of 32 agents. ## sans_regulation -Regulation operations allow SignalGP and SignalGP-Lite programs to adjust which program modules are expressed. Since this invalidates tag-match caches, we wanted to measure timings without regulation enabled. +Regulation operations allow SignalGP and SignalGP-Lite programs to adjust which program modules are expressed in response to environmental signals. Since this invalidates tag-match caches (which help lower the performance impact of tag-matching), we wanted to measure timings without regulation enabled. + This benchmark measures the complete instruction set with regulation-related instructions excluded. -As shown on \autoref{fig:bench-wall}, this yields a 35x to 47x speed-up. -The biggest speedup occured at 32 agents. -As such, SignalGP-Lite offers performance improvements even on simulations that do not heavily depend on regulation. +As shown on \autoref{fig:bench-wall}, this yields a 35x to 47x speed-up with respect to SignalGP. +The greatest speedup occurred at a light memory footprint of 32 agents. +From this, we can conclude that SignalGP-Lite offers performance improvements even on simulations that do not heavily depend on regulation. # Test Problem Benchmarking -In order to successfully server as a specialized alternative to the original SignalGP for certain artificial life applications, SignalGP-Lite must match SignalGP's performance on benchmarks measuring responsivity and plasticity. +In order to viably serve as a specialized alternative to the original SignalGP for certain artificial life applications, SignalGP-Lite must match SignalGP's performance on benchmarks measuring responsivity and plasticity (the ability of organisms to adapt to changes in their environment). To verify SignalGP-Lite's aptitude on these tests, we replicated two canonical SignalGP experiments, reported below [@lalejini2018evolving; @lalejini_tag-based_2021]. ## Changing Environment Problem -The Changing Environment Problem dispatched K = 2, 4, 8, or 16 mutually-exclusive environmental signals with randomly generated tags. Organisms were tasked to respond to each signal with a unique response instruction [@lalejini2018evolving]. +The Changing Environment Problem dispatched K = 2, 4, 8, or 16 mutually-exclusive environmental signals with randomly generated labels. +Organisms were tasked to respond to each signal with a unique response instruction [@lalejini2018evolving]. -A total of 100 replicate populations of [xxxx] individuals were evolved for up to 10,000 generations. -All 100 organisms were chosen using the elite and roulette selection schemes. +A total of 100 replicate populations of 100 individuals were evolved for up to 10,000 generations. +Elite selection was used to choose the best-fit individual; roulette selection was used for the other 99. \autoref{fig:tts-changing} shows the number of generations elapsed before a full solution was found. SignalGP-Lite evolved full solutions to each problem within 3,500 updates in all 100 tested replicates. @@ -125,15 +139,16 @@ We suspect this improvement occurred due to differences in how mutation, tag mat ## Contextual Signal Problem The Contextual Signal Problem assesses the ability of evolving programs to maintain memory of previously encountered signals. -Programs must remember an initial signal (i.e., its "context") in order to respond appropriately to a second signal [@lalejini_tag-based_2021]. +In previous work, this problem was used to demonstrate an important use case of regulation instructions. +To solve this problem, programs must remember an initial signal (i.e., its "context") in order to respond appropriately to a second signal [@lalejini_tag-based_2021]. We assigned each possible unordered input signal pair a unique response to then be performed by the organism. -We tested with 16 input signal pairs and 4 response signals. -Table 2 in @lalejini_tag-based_2021 provides a visual representation of these sequences. +We tested with 16 input signal pairs and 4 output responses. +Table 2 in @lalejini_tag-based_2021 enumerates these sequences and responses. -A total of XX replicates were evolved for up to 10,000 generations using a 16-way lexicase selection scheme, with each of the input signal pairs serving as a test case [@spector2012assessment]. +A total of XX replicates were evolved for up to 10,000 generations using a 16-way lexicase selection scheme [@spector2012assessment], with each of the input signal pairs serving as a test case. To evaluate each test case, programs were sent the first signal of each test case and given 128 virtual CPU cycles to process it. -After this, their internal running modules were reset and the second signal was sent. +After this, their internal running modules were killed and the second signal was sent. After another 128 virtual CPU cycles, their response was recorded. In order to save resources and computing time, as soon as a replicate evolved a fully-correct solution, their evolution was halted. We excluded RNG operations from the instruction set to ensure that solutions were not reached by chance. From daf9ef3e2a25944fc5c64c1aff4e708bdc370c04 Mon Sep 17 00:00:00 2001 From: Matthew Andres Moreno Date: Sat, 31 Jul 2021 22:39:22 -0800 Subject: [PATCH 078/113] Add Alex as an author --- joss/paper.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/joss/paper.md b/joss/paper.md index c4535e76..f69f8473 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -12,11 +12,16 @@ authors: - name: Santiago Rodriguez Papa orcid: 0000-0002-6028-2105 affiliation: 1 + - name: Alexander Lalejini + orcid: 0000-0003-0994-2718 + affiliation: 1 - name: Charles Ofria orcid: 0000-0003-2924-1732 affiliations: - name: Michigan State University index: 1 + - name: University of Michigan + index: 2 date: 31 May 2021 bibliography: paper.bib --- From f64868bfc457996f23f1d17a19b41a4c5404babc Mon Sep 17 00:00:00 2001 From: Matthew Andres Moreno Date: Sat, 31 Jul 2021 22:41:32 -0800 Subject: [PATCH 079/113] Comment out todo --- joss/paper.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/joss/paper.md b/joss/paper.md index f69f8473..850cb0aa 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -56,7 +56,8 @@ In "Test Problem Benchmarking," we compare solution quality of SignalGP and Sign # Statement of need -[introduce artificial life; Adami] Despite being able to simulate evolution with much faster generational turnover than is possible in biological experiments [@ofria2004avida], the scale of artificial life populations is profoundly limited by available computational resources [@Moreno_2020]. + +Despite being able to simulate evolution with much faster generational turnover than is possible in biological experiments [@ofria2004avida], the scale of artificial life populations is profoundly limited by available computational resources [@Moreno_2020]. Large population sizes are essential to studying fundamental evolutionary phenomena such as ecologies, the transition to multicelularity, and rare events. In conjunction with parallel and distributed computing, computational efficiency is crucial to achieving larger-scale artificial life situations. From c13c7dfe894e5d9c888c2a73cb941e9df78fb2a0 Mon Sep 17 00:00:00 2001 From: Matthew Andres Moreno Date: Sat, 31 Jul 2021 22:55:41 -0800 Subject: [PATCH 080/113] Update paper.md --- joss/paper.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/joss/paper.md b/joss/paper.md index 850cb0aa..3d8a86e9 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -152,7 +152,7 @@ We assigned each possible unordered input signal pair a unique response to then We tested with 16 input signal pairs and 4 output responses. Table 2 in @lalejini_tag-based_2021 enumerates these sequences and responses. -A total of XX replicates were evolved for up to 10,000 generations using a 16-way lexicase selection scheme [@spector2012assessment], with each of the input signal pairs serving as a test case. +A total of 20 replicates were evolved for up to 10,000 generations using a 16-way lexicase selection scheme [@spector2012assessment], with each of the input signal pairs serving as a test case. To evaluate each test case, programs were sent the first signal of each test case and given 128 virtual CPU cycles to process it. After this, their internal running modules were killed and the second signal was sent. After another 128 virtual CPU cycles, their response was recorded. @@ -204,6 +204,7 @@ Any opinions, findings, and conclusions or recommendations expressed in this mat # Supplementary Material All benchmarks reported in this section were performed using Google Benchmark version 1.5.2-1. + ![Maximum fitness wrt. updates, with standard deviation confidence intervals. This is because, due to large number of datapoints, computing 95% CI takes a non-insignificant amount of time.\label{fig:max-fitness-sd}](figures/max-fitness-sd.png) From 8e5e155dcb009c2b2104f0d700ea7682fb7023a4 Mon Sep 17 00:00:00 2001 From: Matthew Andres Moreno Date: Sat, 31 Jul 2021 23:17:54 -0800 Subject: [PATCH 081/113] Fix Alex's affiliation --- joss/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss/paper.md b/joss/paper.md index 3d8a86e9..c4e3c01d 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -14,7 +14,7 @@ authors: affiliation: 1 - name: Alexander Lalejini orcid: 0000-0003-0994-2718 - affiliation: 1 + affiliation: 2 - name: Charles Ofria orcid: 0000-0003-2924-1732 affiliations: From d3be2b4527215dfbfb277c028fadaf44112f2a5f Mon Sep 17 00:00:00 2001 From: Matthew Andres Moreno Date: Mon, 2 Aug 2021 02:21:47 -0400 Subject: [PATCH 082/113] Add SignalGP citation to summary --- joss/paper.md | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index c4e3c01d..dbdf1d9f 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -37,7 +37,7 @@ We also report solution quality equivalent to SignalGP on two benchmark problems # Summary -SignalGP is an existing event-driven genetic programming C++ library well-suited for interaction-heavy program synthesis problems. +SignalGP is an existing event-driven genetic programming C++ library well-suited for interaction-heavy program synthesis problems [@lalejini2018evolving]. SignalGP was instrumental in introducing the event-driven genetic programming paradigm where program modules are triggered in response to signals from the environment. Unlike the traditional imperative genetic programming paradigm, where a single chain of execution directly manages every aspect of the program, event-driven genetic programs trigger event handlers (i.e., program modules) in response to signals that are generated internally, externally from other agents, or externally from the environment. @@ -51,9 +51,6 @@ Here, we present the compute time and solution quality comparison of SignalGP-Li In "Execution Speed Benchmarking," we report compute times for both SignalGP and SignalGP-Lite using synthetic benchmarks---benchmarks that designed with reproducibility and accuracy in mind, but that might not reflect real-world problems. In "Test Problem Benchmarking," we compare solution quality of SignalGP and SignalGP-Lite on synthetic genetic programming problems designed to test responsivity and plasticity. - - - # Statement of need From a6a49a132e76dce0d9cd7d72c2ecd8c010279d2c Mon Sep 17 00:00:00 2001 From: Matthew Andres Moreno Date: Mon, 2 Aug 2021 02:27:00 -0400 Subject: [PATCH 083/113] Add oee4 paper --- joss/paper.bib | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/joss/paper.bib b/joss/paper.bib index d4781679..58dc43f2 100644 --- a/joss/paper.bib +++ b/joss/paper.bib @@ -139,3 +139,11 @@ @inproceedings{spector2012assessment pages={401--408}, year={2012} } + +@inproceedings{moreno2021case, + author = {Moreno, Matthew A and Rodriguez Papa, Santiago and Ofria, Charles}, + title = {Case Study of Novelty, Complexity, and Adaptation in a Multicellular System}, + year = {2021}, + maintitle = {The 2021 Conference on Artificial Life}, + booktitle = {OEE4: The Fourth Workshop on Open-Ended Evolution}, +} From 38c54aab8f8b8e5f4b5330622503c9f1611b8e84 Mon Sep 17 00:00:00 2001 From: Matthew Andres Moreno Date: Mon, 2 Aug 2021 02:28:22 -0400 Subject: [PATCH 084/113] Add more DISHTINY citations --- joss/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss/paper.md b/joss/paper.md index dbdf1d9f..1acbc998 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -165,7 +165,7 @@ These results mean that SignalGP-Lite is a valid alternative to SignalGP when it # Projects Using the Software -SignalGP-Lite is used in DISHTINY, a digital framework for studying organism multicelularity [@moreno2019toward]. +SignalGP-Lite is used in DISHTINY, a digital framework for studying organism multicelularity [@moreno2019toward; @moreno2021exploring; @moreno2021case]. # Figures From 9a68a4987f0c5b7960eaed0fe7099bb51ab3e927 Mon Sep 17 00:00:00 2001 From: Matthew Andres Moreno Date: Mon, 2 Aug 2021 03:58:37 -0400 Subject: [PATCH 085/113] Add artificial life blurb --- joss/paper.md | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index 1acbc998..028d7fb2 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -53,8 +53,13 @@ In "Test Problem Benchmarking," we compare solution quality of SignalGP and Sign # Statement of need - -Despite being able to simulate evolution with much faster generational turnover than is possible in biological experiments [@ofria2004avida], the scale of artificial life populations is profoundly limited by available computational resources [@Moreno_2020]. +Mathematical and computational constructs devised to depict hypothesised biological reality play a critical role in advancing our by translating assumptions underlying those hypotheses into falsifiable predictions [@gunawardena2014models]. +Such models traditionally represent mechanisms of biology in direct, often physical, sense --- explicitly modeling, for example, biochemical oscillations or population counts within a species [@mogilner2006quantitative, @schuster2011mathematical]. +In contrast, artificial life systems ply unfamiliar substrates such as genetic programs or set-theoretic artificial chemistries that lack a direct analog in the natural world [@ofria2004avida; @dittrich2001artificial]. +Although artificial life substrates are not directly descriptive of physical reality *per se*, research interest stems instead from their capacities to instantiate fundamental abstract processes core to biological life, such as evolution, and their capacity to exhibit fundamental abstract properties ofy biological life, such as plasticity. +(Whether artificial life systems are best conceived of as models or as alternate instances of "life as it could be" haunts perennial philosophical interest [@shanken1998life; @pennock2007models].) + +Despite being able to simulate evolution with much faster generational turnover than is possible in biological experiments [@ofria2004avida], the scale of digital artificial life populations is profoundly limited by available computational resources [@Moreno_2020]. Large population sizes are essential to studying fundamental evolutionary phenomena such as ecologies, the transition to multicelularity, and rare events. In conjunction with parallel and distributed computing, computational efficiency is crucial to achieving larger-scale artificial life situations. From 286aee1b83f549633828a77073bb09fcfdf6a5b9 Mon Sep 17 00:00:00 2001 From: Matthew Andres Moreno Date: Mon, 2 Aug 2021 04:00:17 -0400 Subject: [PATCH 086/113] Add new citations --- joss/paper.bib | 75 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 75 insertions(+) diff --git a/joss/paper.bib b/joss/paper.bib index 58dc43f2..a7cb2b5d 100644 --- a/joss/paper.bib +++ b/joss/paper.bib @@ -147,3 +147,78 @@ @inproceedings{moreno2021case maintitle = {The 2021 Conference on Artificial Life}, booktitle = {OEE4: The Fourth Workshop on Open-Ended Evolution}, } + +@article{gunawardena2014models, + title={Models in biology:‘accurate descriptions of our pathetic thinking’}, + author={Gunawardena, Jeremy}, + journal={BMC biology}, + volume={12}, + number={1}, + pages={1--11}, + year={2014}, + publisher={BioMed Central} +} + +@article{mogilner2006quantitative, + title={Quantitative modeling in cell biology: what is it good for?}, + author={Mogilner, Alex and Wollman, Roy and Marshall, Wallace F}, + journal={Developmental cell}, + volume={11}, + number={3}, + pages={279--287}, + year={2006}, + publisher={Elsevier} +} + +@article{schuster2011mathematical, + title={Mathematical modeling of evolution. Solved and open problems}, + author={Schuster, Peter}, + journal={Theory in Biosciences}, + volume={130}, + number={1}, + pages={71--89}, + year={2011}, + publisher={Springer} +} + +@article{pennock2007models, + title={Models, simulations, instantiations, and evidence: the case of digital evolution}, + author={Pennock, Robert T}, + journal={Journal of Experimental \& Theoretical Artificial Intelligence}, + volume={19}, + number={1}, + pages={29--42}, + year={2007}, + publisher={Taylor \& Francis} +} + +@article{shanken1998life, + title={Life as we know it and/or life as it could be: Epistemology and the ontology/ontogeny of artificial life}, + author={Shanken, Edward A}, + journal={Leonardo}, + pages={383--388}, + year={1998}, + publisher={JSTOR} +} + +@article{dittrich2001artificial, + title={Artificial chemistries—a review}, + author={Dittrich, Peter and Ziegler, Jens and Banzhaf, Wolfgang}, + journal={Artificial life}, + volume={7}, + number={3}, + pages={225--275}, + year={2001}, + publisher={MIT Press} +} + +@article{ofria2004avida, + title={Avida: A software platform for research in computational evolutionary biology}, + author={Ofria, Charles and Wilke, Claus O}, + journal={Artificial life}, + volume={10}, + number={2}, + pages={191--229}, + year={2004}, + publisher={MIT Press} +} From 36cf329f5e9448d61d3d62935652483577eb8ff9 Mon Sep 17 00:00:00 2001 From: Matthew Andres Moreno Date: Mon, 2 Aug 2021 04:12:35 -0400 Subject: [PATCH 087/113] Add a conclusion section --- joss/paper.md | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index 028d7fb2..4a041e0c 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -167,10 +167,18 @@ Moreover, fewer replicates failed to reach a full solution in 10000 generations With regulation disabled, however, the performance of both libraries was similar. These results mean that SignalGP-Lite is a valid alternative to SignalGP when it comes to artificial life applications. - # Projects Using the Software -SignalGP-Lite is used in DISHTINY, a digital framework for studying organism multicelularity [@moreno2019toward; @moreno2021exploring; @moreno2021case]. +SignalGP-Lite is used in DISHTINY, a digital framework for studying evolution of multicelularity [@moreno2019toward; @moreno2021exploring; @moreno2021case]. + +# Conclusion + +In this work, we introduced a specialized reimplementation of the SignalGP library for event-driven genetic programming called SignalGP-Lite. +Designed to cater to artificial life applications, the SignalGP-Lite streamlines agents by reducing control flow overhead and trading run-time flexibility for better performance due to compile-time configuration. +We showed through benchmarking experiments how these distinctions, stemming from fundamental aspects of the re-implementation, enable an order-of-magnitude speedup. +Further, through two benchmark problems from foundational work with SignalGP, we demonstrate solution quality equivalent to SignalGP. +SignalGP-Lite has already been adopted in an ongoing artificial life project, the DISHTINY digital multicellularity framework. +Because of its distinctive capabilities, free distribution of this software enables new opportunities within artificial life and other specialized applications of genetic programming. # Figures From a684bb0b1b170c88b602268b618c0be38394f49d Mon Sep 17 00:00:00 2001 From: Alexander Lalejini Date: Tue, 3 Aug 2021 16:22:26 -0400 Subject: [PATCH 088/113] 2021-08-03 joss edits and comments --- joss/paper.md | 60 +++++++++++++++++++++++++++------------------------ 1 file changed, 32 insertions(+), 28 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index 4a041e0c..b52e061a 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -30,45 +30,47 @@ bibliography: paper.bib Event-driven genetic programming representations have been shown to outperform traditional imperative representations on interaction-intensive problems. The event-driven approach organizes genome content into modules that are triggered in response to environmental signals, simplifying simulation design and implementation. -Existing work developing event-driven genetic programming methodology has largely used the SignalGP library, which caters to traditional program synthesis applications. -The SignalGP-Lite library enables larger-scale artificial life experiments with streamlined agents by reducing control flow overhead and trading run-time flexibility for better performance due to compile-time configuration. -Here, we report benchmarking experiments that show an 8x to 30x speedup. +Existing work developing event-driven genetic programming methodology has largely used the SignalGP framework, which caters to traditional program synthesis applications. +The SignalGP-Lite library provides a new, [developer friendly/usable/well documented/etc] implementation of the SignalGP framework, targeting artificial life and [multi-agent/computational evolution] applications. +SignalGP-Lite enables larger-scale artificial life experiments with streamlined agents by reducing control flow overhead and trading run-time flexibility for better performance due to compile-time configuration. +Here, we report benchmarking experiments that show an 8x to 30x speedup over original SignalGP implementations. We also report solution quality equivalent to SignalGP on two benchmark problems originally developed to test the ability of evolved programs to respond to a large number of signals and to modulate signal response based on context. # Summary -SignalGP is an existing event-driven genetic programming C++ library well-suited for interaction-heavy program synthesis problems [@lalejini2018evolving]. -SignalGP was instrumental in introducing the event-driven genetic programming paradigm where program modules are triggered in response to signals from the environment. - -Unlike the traditional imperative genetic programming paradigm, where a single chain of execution directly manages every aspect of the program, event-driven genetic programs trigger event handlers (i.e., program modules) in response to signals that are generated internally, externally from other agents, or externally from the environment. -Event-driven representation outperforms traditional imperative genetic programming on interaction intensive problems where the programs must handle inputs from the environment or other organisms, as is the case in some traditional genetic programming contexts and many artificial life simulations. +Genetic programming applies the principles of evolution to automatically synthesize computer programs rather than writing them by hand [CITE (a nice introduction to GP)]. +Often genetic programming is used to synthesize _imperative_ programs where a single chain of execution directly manages every aspect of the program. +SignalGP is an existing genetic programming framework for evolving _event-driven_ computer programs where programs trigger event handlers (i.e., program modules) in response to signals that are generated internally, externally from other agents, or externally from the environment [@lalejini2018evolving]. +Such event-driven representations outperform traditional imperative genetic programming on interaction intensive problems where the evolved programs must handle inputs from the environment or from other agents, as is the case in distributed computing problem domains and many artificial life simulations. SignalGP-Lite is a C++ library for event-driven genetic programming. -In comparison to SignalGP, which is intended for general event-driven genetic programming, SignalGP-Lite is tailored for use in artificial life experiments. +In comparison to existing implementations of SignalGP, which are intended for general event-driven genetic programming, SignalGP-Lite is tailored for use in artificial life experiments. -Here, we present the compute time and solution quality comparison of SignalGP-Lite and SignalGP on some scenarios where the original implementation has the edge. +Here, we benchmark the compute time and solution quality of SignalGP-Lite on some scenarios where the original implementation has the edge. -SignalGP-Lite is the first implementation of SignalGP accessible to the general public. -Its simplified API is complemented by extensive documentation, continuous integration tests, and benchmarks that measure both real-world workloads as well as individual instructions. +The existing C++ implementation of SignalGP (hereafter, referred to as SignalGP) is proof-of-concept, lacking substantial documentation, rigorous software testing, and optimizations for large-scale use. +The SignalGP-Lite library addresses these issues, providing the first easily accessible implementation of the SignalGP framework. + +SignalGP-Lite's simplified API is complemented by extensive documentation, continuous integration tests, and benchmarks that measure both real-world workloads as well as individual instructions. In comparison to SignalGP---which was designed to target generic genetic programming problems---SignalGP-Lite fills a niche for interaction-heavy genetic programming applications that can tolerate less runtime configuration flexibility, and pared-back control flow. SignalGP-Lite is designed with artificial life experiments in mind, where simulation parameters need not change during execution and a more rudimentary approach to control flow can often be tolerated. -The library has enabled order-of-magnitude scale-up of existing artificial life experiments studying the evolution of multicelularity; we anticipate it will also enable novel work in other artificial life and genetic programming contexts. +The library has enabled order-of-magnitude scale-up of existing artificial life experiments studying the evolution of multicellularity (e.g., [CITATIONS]); we anticipate it will also enable novel work in other artificial life and genetic programming contexts. # Execution Speed Benchmarking @@ -118,7 +120,9 @@ The greatest speedup occurred at a light memory footprint of 32 agents. ## sans_regulation -Regulation operations allow SignalGP and SignalGP-Lite programs to adjust which program modules are expressed in response to environmental signals. Since this invalidates tag-match caches (which help lower the performance impact of tag-matching), we wanted to measure timings without regulation enabled. +Regulation operations allow SignalGP and SignalGP-Lite programs to adjust which program modules are expressed in response to environmental signals. +Since regulation can invalidate some of SignalGP-Lite's caching optimizations, we wanted to measure timings without regulation enabled. + This benchmark measures the complete instruction set with regulation-related instructions excluded. @@ -128,18 +132,18 @@ From this, we can conclude that SignalGP-Lite offers performance improvements ev # Test Problem Benchmarking -In order to viably serve as a specialized alternative to the original SignalGP for certain artificial life applications, SignalGP-Lite must match SignalGP's performance on benchmarks measuring responsivity and plasticity (the ability of organisms to adapt to changes in their environment). +In order to viably serve as a specialized alternative to the original SignalGP for certain artificial life applications, SignalGP-Lite must match SignalGP's performance on benchmarks measuring responsivity and plasticity (the ability of programs to adapt to changes in their environment). To verify SignalGP-Lite's aptitude on these tests, we replicated two canonical SignalGP experiments, reported below [@lalejini2018evolving; @lalejini_tag-based_2021]. ## Changing Environment Problem The Changing Environment Problem dispatched K = 2, 4, 8, or 16 mutually-exclusive environmental signals with randomly generated labels. -Organisms were tasked to respond to each signal with a unique response instruction [@lalejini2018evolving]. +Programs were tasked to respond to each signal with a unique response instruction [@lalejini2018evolving]. A total of 100 replicate populations of 100 individuals were evolved for up to 10,000 generations. Elite selection was used to choose the best-fit individual; roulette selection was used for the other 99. \autoref{fig:tts-changing} shows the number of generations elapsed before a full solution was found. -SignalGP-Lite evolved full solutions to each problem within 3,500 updates in all 100 tested replicates. +SignalGP-Lite evolved full solutions to each problem within 3,500 generations in all 100 tested replicates. In the `K=16` case, we achieved a superior 100% signal reproduction rate compared to an average of 32% on the original SignalGP implementation (@lalejini2018evolving Figure 2). We suspect this improvement occurred due to differences in how mutation, tag matching, and program initialization were performed, rather than an intrinsic difference between the libraries. @@ -159,13 +163,13 @@ To evaluate each test case, programs were sent the first signal of each test cas After this, their internal running modules were killed and the second signal was sent. After another 128 virtual CPU cycles, their response was recorded. In order to save resources and computing time, as soon as a replicate evolved a fully-correct solution, their evolution was halted. -We excluded RNG operations from the instruction set to ensure that solutions were not reached by chance. +We excluded RNG operations from the instruction set to ensure that programs could not solve the Contextual Signal Problem by chance. \autoref{fig:tts-context} shows the number of generations elapsed before a full solution was found. SignalGP-Lite evolved full solutions in half as many generations compared to SignalGP when regulation was enabled. -Moreover, fewer replicates failed to reach a full solution in 10000 generations under SignalGP-Lite. +Moreover, fewer replicates failed to reach a full solution in 10,000 generations under SignalGP-Lite. With regulation disabled, however, the performance of both libraries was similar. -These results mean that SignalGP-Lite is a valid alternative to SignalGP when it comes to artificial life applications. +These results suggest that SignalGP-Lite is a valid alternative to SignalGP when it comes to artificial life applications. # Projects Using the Software @@ -174,7 +178,7 @@ SignalGP-Lite is used in DISHTINY, a digital framework for studying evolution of # Conclusion In this work, we introduced a specialized reimplementation of the SignalGP library for event-driven genetic programming called SignalGP-Lite. -Designed to cater to artificial life applications, the SignalGP-Lite streamlines agents by reducing control flow overhead and trading run-time flexibility for better performance due to compile-time configuration. +Designed to cater to artificial life applications, the SignalGP-Lite library streamlines agents by reducing control flow overhead and trading run-time flexibility for better performance due to compile-time configuration. We showed through benchmarking experiments how these distinctions, stemming from fundamental aspects of the re-implementation, enable an order-of-magnitude speedup. Further, through two benchmark problems from foundational work with SignalGP, we demonstrate solution quality equivalent to SignalGP. SignalGP-Lite has already been adopted in an ongoing artificial life project, the DISHTINY digital multicellularity framework. From 0b2d241b101a2001df06f5eb6b74f8d6db75682a Mon Sep 17 00:00:00 2001 From: Matthew Andres Moreno Date: Tue, 3 Aug 2021 19:04:51 -0400 Subject: [PATCH 089/113] Add GP intro citation --- joss/paper.bib | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/joss/paper.bib b/joss/paper.bib index a7cb2b5d..a7cadbeb 100644 --- a/joss/paper.bib +++ b/joss/paper.bib @@ -222,3 +222,10 @@ @article{ofria2004avida year={2004}, publisher={MIT Press} } + +@book{banzhaf1998genetic, + title={Genetic programming: an introduction: on the automatic evolution of computer programs and its applications}, + author={Banzhaf, Wolfgang and Nordin, Peter and Keller, Robert E and Francone, Frank D}, + year={1998}, + publisher={Morgan Kaufmann Publishers Inc.} +} From f3cfe04b13b9d29948225b05d9a460e0aefa2076 Mon Sep 17 00:00:00 2001 From: Matthew Andres Moreno Date: Tue, 3 Aug 2021 19:41:53 -0400 Subject: [PATCH 090/113] Incorporate in-text comments from @amlalejini --- joss/paper.md | 34 +++++++++++++++------------------- 1 file changed, 15 insertions(+), 19 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index b52e061a..26dc1bc0 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -31,29 +31,27 @@ bibliography: paper.bib Event-driven genetic programming representations have been shown to outperform traditional imperative representations on interaction-intensive problems. The event-driven approach organizes genome content into modules that are triggered in response to environmental signals, simplifying simulation design and implementation. Existing work developing event-driven genetic programming methodology has largely used the SignalGP framework, which caters to traditional program synthesis applications. -The SignalGP-Lite library provides a new, [developer friendly/usable/well documented/etc] implementation of the SignalGP framework, targeting artificial life and [multi-agent/computational evolution] applications. -SignalGP-Lite enables larger-scale artificial life experiments with streamlined agents by reducing control flow overhead and trading run-time flexibility for better performance due to compile-time configuration. +The SignalGP-Lite library provides a specialized implementation of the SignalGP framework that caters to multi-agent applications, especially those characteristic of artificial life research. +SignalGP-Lite enables larger-scale artificial life experiments by streamlining agents through simplified control flow and reduced run-time configuration flexibility (in favor of better-performing compile-time configuration). Here, we report benchmarking experiments that show an 8x to 30x speedup over original SignalGP implementations. We also report solution quality equivalent to SignalGP on two benchmark problems originally developed to test the ability of evolved programs to respond to a large number of signals and to modulate signal response based on context. # Summary -Genetic programming applies the principles of evolution to automatically synthesize computer programs rather than writing them by hand [CITE (a nice introduction to GP)]. +Genetic programming applies the principles of evolution to automatically synthesize computer programs rather than writing them by hand [@banzhaf1998genetic]. Often genetic programming is used to synthesize _imperative_ programs where a single chain of execution directly manages every aspect of the program. SignalGP is an existing genetic programming framework for evolving _event-driven_ computer programs where programs trigger event handlers (i.e., program modules) in response to signals that are generated internally, externally from other agents, or externally from the environment [@lalejini2018evolving]. Such event-driven representations outperform traditional imperative genetic programming on interaction intensive problems where the evolved programs must handle inputs from the environment or from other agents, as is the case in distributed computing problem domains and many artificial life simulations. SignalGP-Lite is a C++ library for event-driven genetic programming. In comparison to existing implementations of SignalGP, which are intended for general event-driven genetic programming, SignalGP-Lite is tailored for use in artificial life experiments. - -Here, we benchmark the compute time and solution quality of SignalGP-Lite on some scenarios where the original implementation has the edge. +In conjunction with parallel and distributed computing, computational efficiency is crucial to achieving larger-scale artificial life simulations. -The existing C++ implementation of SignalGP (hereafter, referred to as SignalGP) is proof-of-concept, lacking substantial documentation, rigorous software testing, and optimizations for large-scale use. -The SignalGP-Lite library addresses these issues, providing the first easily accessible implementation of the SignalGP framework. - -SignalGP-Lite's simplified API is complemented by extensive documentation, continuous integration tests, and benchmarks that measure both real-world workloads as well as individual instructions. -In comparison to SignalGP---which was designed to target generic genetic programming problems---SignalGP-Lite fills a niche for interaction-heavy genetic programming applications that can tolerate less runtime configuration flexibility, and pared-back control flow. +In comparison to SignalGP --- which was designed to target generic genetic programming problems --- SignalGP-Lite fills a niche for interaction-heavy genetic programming applications that can tolerate less runtime configuration flexibility and pared-back control flow. SignalGP-Lite is designed with artificial life experiments in mind, where simulation parameters need not change during execution and a more rudimentary approach to control flow can often be tolerated. -The library has enabled order-of-magnitude scale-up of existing artificial life experiments studying the evolution of multicellularity (e.g., [CITATIONS]); we anticipate it will also enable novel work in other artificial life and genetic programming contexts. +In addition, SignalGP-Lite specially emphasizes accessibility for re-use and extension by the broader research community. +To ensure reliability and usability, and we provide documentation via ReadTheDocs, test source code via continuous integration, benchmark performance critical components, and cater to custom extensions of the instruction set and virtual hardware. +The library has enabled order-of-magnitude scale-up of existing artificial life experiments studying the evolution of multicellularity (e.g., [@moreno2021exploring; @moreno2021case]); we anticipate it will also enable novel work in other artificial life and genetic programming contexts. # Execution Speed Benchmarking -We performed a set of microbenchmarks---a type of synthetic benchmark that measures individual functions---to quantify the effectiveness of SignalGP-Lite's optimizations in accelerating evaluation of event-driven genetic programs. +We performed a set of microbenchmarks --- a type of synthetic benchmark that measures execution time of software subcomponents --- to quantify the effectiveness of SignalGP-Lite's optimizations in accelerating evaluation of event-driven genetic programs. Hardware caching size profoundly affects memory access time, which is key to computational performance [@skadron1999branch]. In order to determine the relative performance of SignalGP and SignalGP-Lite at different cache levels, we benchmarked over different orders of magnitude of memory load by varying the number of virtual CPUs (agent counts) between from 1 and 32768 (Supplementary Table \ref{raw-timings-table}). -We performed five microbenchmark experiments, reported below, to isolate how specific aspects of the library design influenced performance. Analysis below focuses on wall time speedup. However, supplementary \autoref{fig:raw-timings} shows raw wall-clock timings for these experiments. +We performed five microbenchmark experiments, reported below, to isolate how specific aspects of the library design influenced performance. Analysis below focuses on wall time speedup. +However, supplementary \autoref{fig:raw-timings} shows raw wall-clock timings for these experiments. ## control @@ -121,14 +118,13 @@ The greatest speedup occurred at a light memory footprint of 32 agents. ## sans_regulation Regulation operations allow SignalGP and SignalGP-Lite programs to adjust which program modules are expressed in response to environmental signals. -Since regulation can invalidate some of SignalGP-Lite's caching optimizations, we wanted to measure timings without regulation enabled. - +Since regulation can invalidate some of SignalGP and SignalGP-Lite's caching optimizations, we wanted to measure timings without regulation enabled. This benchmark measures the complete instruction set with regulation-related instructions excluded. As shown on \autoref{fig:bench-wall}, this yields a 35x to 47x speed-up with respect to SignalGP. The greatest speedup occurred at a light memory footprint of 32 agents. -From this, we can conclude that SignalGP-Lite offers performance improvements even on simulations that do not heavily depend on regulation. +From this, we can conclude that SignalGP-Lite offers performance improvements even on simulations that do not depend on regulation. # Test Problem Benchmarking From 58ff882164e51b16b1685deaa3e1cdcc9d056250 Mon Sep 17 00:00:00 2001 From: Matthew Andres Moreno Date: Tue, 3 Aug 2021 19:50:27 -0400 Subject: [PATCH 091/113] Clarify rng -> random number generator --- joss/paper.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index 26dc1bc0..f4fd7eaa 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -76,7 +76,8 @@ We performed a set of microbenchmarks --- a type of synthetic benchmark that mea Hardware caching size profoundly affects memory access time, which is key to computational performance [@skadron1999branch]. In order to determine the relative performance of SignalGP and SignalGP-Lite at different cache levels, we benchmarked over different orders of magnitude of memory load by varying the number of virtual CPUs (agent counts) between from 1 and 32768 (Supplementary Table \ref{raw-timings-table}). -We performed five microbenchmark experiments, reported below, to isolate how specific aspects of the library design influenced performance. Analysis below focuses on wall time speedup. +We performed five microbenchmark experiments, reported below, to isolate how specific aspects of the library design influenced performance. +Analysis below focuses on wall time speedup. However, supplementary \autoref{fig:raw-timings} shows raw wall-clock timings for these experiments. ## control @@ -87,8 +88,8 @@ The 1x wall speedup (\autoref{fig:bench-wall}) confirms that further results are ## nop A program consisting of 100 `nop` instructions is randomly generated. -None of these instructions advance the pRNG engine. -This benchmarks the instruction directly, as it is the only call measured inside the benchmarking loop. +(Although some `nop` operation variants advance the random number generator engine in order to minimize unintended side effects when substituted for another operation that itself advances the random number generator, no `nop` instructions used in this experiment did so.) +This benchmarks instruction execution overhead directly, as it successive `nop`'ss are the only call measured inside the benchmarking loop. With this approach, the relative performance impact of SignalGP-Lite's byte-code interpreter can be compared to SignalGP's lambda-based instructions. We observe an 8x to 30x speedup under SignalGP-Lite (\autoref{fig:bench-wall}). @@ -107,7 +108,7 @@ The greatest speedup increase occurred at a relatively light memory footprint of ## complete The complete benchmark adds control flow instructions to the prior benchmarks' instruction set. -Bitwise and logical operators, comparison instructions, and RNG operations, are also included. +Bitwise and logical operators, comparison instructions, and random number generation operations, are also included. From this complete instruction set, a 100-instruction program is randomly generated. The main goal of this benchmark is to determine the performance impact of omitting a function stack and implementing inner loops and conditionals in terms of `jump` instructions instead of nested code blocks. @@ -159,7 +160,7 @@ To evaluate each test case, programs were sent the first signal of each test cas After this, their internal running modules were killed and the second signal was sent. After another 128 virtual CPU cycles, their response was recorded. In order to save resources and computing time, as soon as a replicate evolved a fully-correct solution, their evolution was halted. -We excluded RNG operations from the instruction set to ensure that programs could not solve the Contextual Signal Problem by chance. +We excluded random number generating operations from the instruction set to ensure that programs could not solve the Contextual Signal Problem by chance. \autoref{fig:tts-context} shows the number of generations elapsed before a full solution was found. SignalGP-Lite evolved full solutions in half as many generations compared to SignalGP when regulation was enabled. From f08b27afbfd79089bcf09b93970e73f9f1b546ee Mon Sep 17 00:00:00 2001 From: Matthew Andres Moreno Date: Tue, 3 Aug 2021 19:57:38 -0400 Subject: [PATCH 092/113] Add details about google benchmark methodology --- joss/paper.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/joss/paper.md b/joss/paper.md index f4fd7eaa..628243bd 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -79,6 +79,7 @@ In order to determine the relative performance of SignalGP and SignalGP-Lite at We performed five microbenchmark experiments, reported below, to isolate how specific aspects of the library design influenced performance. Analysis below focuses on wall time speedup. However, supplementary \autoref{fig:raw-timings} shows raw wall-clock timings for these experiments. +For each microbenchmark experiment, the Google Benchmark library used an insturmented burn-in process to dynamically determine the number of successive calls to the focal code snippet necessary to take a stable measurement of its run time. ## control @@ -216,6 +217,7 @@ Any opinions, findings, and conclusions or recommendations expressed in this mat All benchmarks reported in this section were performed using Google Benchmark version 1.5.2-1. + ![Maximum fitness wrt. updates, with standard deviation confidence intervals. This is because, due to large number of datapoints, computing 95% CI takes a non-insignificant amount of time.\label{fig:max-fitness-sd}](figures/max-fitness-sd.png) From 09ee770e2f0db03f52d7689bc219a30f254ef01f Mon Sep 17 00:00:00 2001 From: Matthew Andres Moreno Date: Tue, 3 Aug 2021 19:58:08 -0400 Subject: [PATCH 093/113] Fix copy/paste sentence --- joss/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss/paper.md b/joss/paper.md index 628243bd..a522dc79 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -215,7 +215,7 @@ Any opinions, findings, and conclusions or recommendations expressed in this mat # Supplementary Material -All benchmarks reported in this section were performed using Google Benchmark version 1.5.2-1. +All benchmarks reported were performed using Google Benchmark version 1.5.2-1. From 53b3277c3adba2acf50127170ba4f86ff2a73f53 Mon Sep 17 00:00:00 2001 From: Matthew Andres Moreno Date: Tue, 3 Aug 2021 21:01:54 -0400 Subject: [PATCH 094/113] Explain contextual signal solutions w/o regulation --- joss/paper.md | 20 ++++++++++++++++---- 1 file changed, 16 insertions(+), 4 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index a522dc79..4a00d165 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -158,16 +158,28 @@ Table 2 in @lalejini_tag-based_2021 enumerates these sequences and responses. A total of 20 replicates were evolved for up to 10,000 generations using a 16-way lexicase selection scheme [@spector2012assessment], with each of the input signal pairs serving as a test case. To evaluate each test case, programs were sent the first signal of each test case and given 128 virtual CPU cycles to process it. -After this, their internal running modules were killed and the second signal was sent. +After this, the second signal was sent. After another 128 virtual CPU cycles, their response was recorded. + In order to save resources and computing time, as soon as a replicate evolved a fully-correct solution, their evolution was halted. We excluded random number generating operations from the instruction set to ensure that programs could not solve the Contextual Signal Problem by chance. \autoref{fig:tts-context} shows the number of generations elapsed before a full solution was found. - SignalGP-Lite evolved full solutions in half as many generations compared to SignalGP when regulation was enabled. Moreover, fewer replicates failed to reach a full solution in 10,000 generations under SignalGP-Lite. -With regulation disabled, however, the performance of both libraries was similar. -These results suggest that SignalGP-Lite is a valid alternative to SignalGP when it comes to artificial life applications. +This may be due to the original SignalGP experiment purging running modules between signals, while our replication did not. + +The primary object of interest this benchmark, however, is the extent to which each library's problem solving capability improves with the enablement of regulation instructions. +As expected, wth regulation disabled the performance of both libraries is significantly degraded. +SignalGP0-Lite's median (or mean, depending on what's reported in the other paper) time to solution discovery with regulation disabled was XX generations. +SignalGP's was YY generations. +Indeed, withouth regulation, both libraries achieved similarly slow generations to solution discovery. +^[ +We were surprised to see SignalGP-Lite evolve full solutions in the absence of regulation. +SignalGP reached solutions sans regulation through global memory operations that allow coordination between modules. +Although SignalGP-Lite's baseline instruction set does include global memory, manual inspection revealed that solutions appear to have arisen through intricately timed busy loops. +] + +These results confirm that SignalGP-Lite's module regulation system functions comparably to SignalGP's in terms of facilitating context-dependent behavior. # Projects Using the Software From 9b31a74fda9c243c2254c6b63cc4bea9ca471822 Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Wed, 4 Aug 2021 15:24:07 -0400 Subject: [PATCH 095/113] Fix graphs --- joss/figures/raw-timings.png | Bin 121480 -> 122347 bytes joss/figures/wall-time-speedup.jpeg | Bin 128884 -> 0 bytes joss/figures/wall-time-speedup.png | Bin 0 -> 72283 bytes joss/paper.md | 2 +- 4 files changed, 1 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"Library" refers to the set of instructions tested (see Benchmarking Results section).\label{fig:bench-wall}](figures/wall-time-speedup.jpeg) +![Benchmarking results of 20 replicates shown as a times-speedup of wall time. "Library" refers to the set of instructions tested (see Benchmarking Results section).\label{fig:bench-wall}](figures/wall-time-speedup.png) # Acknowledgements From d13698849b83fa1b0157a34c3f89ee5756ef16c1 Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Wed, 4 Aug 2021 16:32:17 -0400 Subject: [PATCH 096/113] Capitalize graphs --- .../changing-enviroment.ipynb | 621 + ...x-lineplot+x=update+y=max-fitness+ext=.pdf | Bin 0 -> 773218 bytes ...x-lineplot+x=update+y=max-fitness+ext=.png | Bin 0 -> 156311 bytes ...x-lineplot+x=update+y=max-fitness+ext=.pdf | Bin 0 -> 384779 bytes ...x-lineplot+x=update+y=max-fitness+ext=.png | Bin 0 -> 143962 bytes .../viz=boxplot+x=k+y=solution-time+ext=.pdf | Bin 0 -> 384025 bytes .../viz=boxplot+x=k+y=solution-time+ext=.png | Bin 0 -> 42237 bytes ...tee-swarmplot+x=k+y=solution-time+ext=.pdf | Bin 0 -> 388215 bytes ...tee-swarmplot+x=k+y=solution-time+ext=.png | Bin 0 -> 198801 bytes .../contextual-signal/contextual-signal.ipynb | 38512 ++++++++++++++++ ...x-lineplot+x=update+y=max-fitness+ext=.pdf | Bin 0 -> 574034 bytes ...x-lineplot+x=update+y=max-fitness+ext=.png | Bin 0 -> 76700 bytes ...plot+x=regulation+y=solution-time+ext=.pdf | Bin 0 -> 382800 bytes ...warm+x=regulation+y=solution-time+ext=.pdf | Bin 0 -> 383899 bytes ...warm+x=regulation+y=solution-time+ext=.png | Bin 0 -> 65130 bytes ...plot+x=regulation+y=solution-time+ext=.pdf | Bin 0 -> 384036 bytes microbenchmarks/script/graph.py | 10 +- microbenchmarks/script/graph_speedup.py | 19 +- tests/operations/binary/Add.cpp | 13 + 19 files changed, 39164 insertions(+), 11 deletions(-) create mode 100644 binder/results/changing-enviroment/changing-enviroment.ipynb create mode 100644 binder/results/changing-enviroment/teeplots/ci=sd+hue=k+viz=logx-lineplot+x=update+y=max-fitness+ext=.pdf create mode 100644 binder/results/changing-enviroment/teeplots/ci=sd+hue=k+viz=logx-lineplot+x=update+y=max-fitness+ext=.png create mode 100644 binder/results/changing-enviroment/teeplots/hue=k+viz=logx-lineplot+x=update+y=max-fitness+ext=.pdf create mode 100644 binder/results/changing-enviroment/teeplots/hue=k+viz=logx-lineplot+x=update+y=max-fitness+ext=.png create mode 100644 binder/results/changing-enviroment/teeplots/viz=boxplot+x=k+y=solution-time+ext=.pdf create mode 100644 binder/results/changing-enviroment/teeplots/viz=boxplot+x=k+y=solution-time+ext=.png create mode 100644 binder/results/changing-enviroment/teeplots/viz=tee-swarmplot+x=k+y=solution-time+ext=.pdf create mode 100644 binder/results/changing-enviroment/teeplots/viz=tee-swarmplot+x=k+y=solution-time+ext=.png create mode 100644 binder/results/contextual-signal/contextual-signal.ipynb create mode 100644 binder/results/contextual-signal/teeplots/ci=sd+hue=regulation+viz=logx-lineplot+x=update+y=max-fitness+ext=.pdf create mode 100644 binder/results/contextual-signal/teeplots/ci=sd+hue=regulation+viz=logx-lineplot+x=update+y=max-fitness+ext=.png create mode 100644 binder/results/contextual-signal/teeplots/viz=boxplot+x=regulation+y=solution-time+ext=.pdf create mode 100644 binder/results/contextual-signal/teeplots/viz=tee-barswarm+x=regulation+y=solution-time+ext=.pdf create mode 100644 binder/results/contextual-signal/teeplots/viz=tee-barswarm+x=regulation+y=solution-time+ext=.png create mode 100644 binder/results/contextual-signal/teeplots/viz=tee-swarmplot+x=regulation+y=solution-time+ext=.pdf diff --git a/binder/results/changing-enviroment/changing-enviroment.ipynb b/binder/results/changing-enviroment/changing-enviroment.ipynb new file mode 100644 index 00000000..91691f68 --- /dev/null +++ b/binder/results/changing-enviroment/changing-enviroment.ipynb @@ -0,0 +1,621 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 14, + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from teeplot import teeplot as tp\n", + "import numpy as np" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "# get data" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 15, + "source": [ + "df2 = pd.read_csv(\"86bf42/K2/concat=100+replicate=1-100+ext=.csv\")\n", + "df4 = pd.read_csv(\"86bf42/K4/concat=100+replicate=1-100+ext=.csv\")\n", + "df8 = pd.read_csv(\"86bf42/K8/concat=100+replicate=1-100+ext=.csv\")\n", + "df16 = pd.read_csv(\"86bf42/K16/concat=100+replicate=1-100+ext=.csv\")\n", + "\n" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "# preprocess data" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 16, + "source": [ + "df2[\"k\"] = 2\n", + "df4[\"k\"] = 4\n", + "df8[\"k\"] = 8\n", + "df16[\"k\"] = 16\n", + "\n", + "df = pd.concat([df2, df4, df8, df16])" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "# define teeplot functions" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 17, + "source": [ + "def logx_lineplot(*args, **kwargs):\n", + " g = sns.lineplot(\n", + " *args,\n", + " **kwargs\n", + " )\n", + " g.set_xscale(\"log\")\n", + " plt.axhline(256, c=\"grey\", linestyle=\"dotted\", zorder=-1)\n", + " y = np.array([16*i for i in range(17)])\n", + " plt.yticks(y)\n", + " # todo: change names here" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "# fitness over time" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## process data" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 18, + "source": [ + "new_rows = []\n", + "for (replicate, k), filtered in df.groupby([\"replicate\", \"k\"]):\n", + " max_update = filtered[\"update\"].max()\n", + "\n", + " for update in range(max_update, 10000):\n", + " new_rows.append({\n", + " \"update\": update,\n", + " \"max_fitness\": 256,\n", + " \"replicate\": replicate,\n", + " \"k\": k\n", + " })\n", + "\n", + "df = df.append(new_rows)\n", + "df" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " update mean_fitness min_fitness max_fitness inferiority \\\n", + "0 0 46.316 0.0 256 0.180922 \n", + "1 0 45.954 0.0 256 0.179508 \n", + "2 0 47.909 0.0 256 0.187145 \n", + "3 0 47.056 0.0 256 0.183812 \n", + "4 0 47.067 0.0 256 0.183855 \n", + "... ... ... ... ... ... \n", + "3842933 9995 NaN NaN 256 NaN \n", + "3842934 9996 NaN NaN 256 NaN \n", + "3842935 9997 NaN NaN 256 NaN \n", + "3842936 9998 NaN NaN 256 NaN \n", + "3842937 9999 NaN NaN 256 NaN \n", + "\n", + " replicate k \n", + "0 100 2 \n", + "1 10 2 \n", + "2 11 2 \n", + "3 12 2 \n", + "4 13 2 \n", + "... ... .. \n", + "3842933 100 16 \n", + "3842934 100 16 \n", + "3842935 100 16 \n", + "3842936 100 16 \n", + "3842937 100 16 \n", + "\n", + "[4000400 rows x 7 columns]" + ], + "text/html": [ + "
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    " + ] + }, + "metadata": {}, + "execution_count": 18 + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## graph results (ci: SD)" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 19, + "source": [ + "df[\"Update\"] = df[\"update\"]\n", + "\n", + "df[\"Max Fitness\"] = df[\"max_fitness\"]\n", + "\n", + "tp.tee(\n", + " logx_lineplot,\n", + " data=df,\n", + " x=\"Update\",\n", + " y=\"Max Fitness\",\n", + " hue=\"k\",\n", + " ci=\"sd\"\n", + ")" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "teeplots/ci=sd+hue=k+viz=logx-lineplot+x=update+y=max-fitness+ext=.pdf\n", + "teeplots/ci=sd+hue=k+viz=logx-lineplot+x=update+y=max-fitness+ext=.png\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## graph results (ci: 95%)" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 20, + "source": [ + "data = df[np.log2(df[\"update\"]+1) % 1.0 < 0.0001]\n", + "len(data)" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "5703" + ] + }, + "metadata": {}, + "execution_count": 20 + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 21, + "source": [ + "data[\"Update\"] = data[\"update\"]\n", + "\n", + "data[\"Max Fitness\"] = data[\"max_fitness\"]\n", + "\n", + "\n", + "tp.tee(\n", + " logx_lineplot,\n", + " data=data,\n", + " x=\"Update\",\n", + " y=\"Max Fitness\",\n", + " hue=\"k\"\n", + ")" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data[\"Update\"] = data[\"update\"]\n", + ":3: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data[\"Max Fitness\"] = data[\"max_fitness\"]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "teeplots/hue=k+viz=logx-lineplot+x=update+y=max-fitness+ext=.pdf\n", + "teeplots/hue=k+viz=logx-lineplot+x=update+y=max-fitness+ext=.png\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
    " + ], + "image/png": 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" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "# time to solution over number of states (k)" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### process data" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 22, + "source": [ + "box_df = df[df[\"max_fitness\"] == 256]\n", + "\n", + "res_df = []\n", + "for (replicate, k), filtered in box_df.groupby([\"replicate\", \"k\"]):\n", + " sol_time = filtered[\"update\"].min()\n", + "\n", + " res_df.append({\n", + " \"solution_time\": sol_time,\n", + " \"replicate\": replicate,\n", + " \"k\": k\n", + " })\n", + "\n", + "res_df = df.from_records(res_df)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### graph" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 24, + "source": [ + "res_df[\"Solution Time\"] = res_df[\"solution_time\"]\n", + "\n", + "tp.tee(\n", + " sns.boxplot,\n", + " data=res_df,\n", + " y=\"Solution Time\",\n", + " x=\"k\"\n", + ")" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "teeplots/viz=boxplot+x=k+y=solution-time+ext=.pdf\n", + "teeplots/viz=boxplot+x=k+y=solution-time+ext=.png\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 24 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## time to solution (swarm plot)" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 25, + "source": [ + "def tee_swarmplot(*args, **kwargs):\n", + " sns.swarmplot(\n", + " linewidth=0.5,\n", + " s=4,\n", + " edgecolor='white',\n", + " *args,\n", + " **kwargs\n", + " )\n", + " sns.boxplot(\n", + " *args,\n", + " **kwargs\n", + " )" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 26, + "source": [ + "tp.tee(\n", + " tee_swarmplot,\n", + " data=res_df,\n", + " x=\"k\",\n", + " y=\"Solution Time\"\n", + ")" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/home/zasz/.local/lib/python3.9/site-packages/seaborn/categorical.py:1296: UserWarning: 85.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n", + " warnings.warn(msg, UserWarning)\n", + "/home/zasz/.local/lib/python3.9/site-packages/seaborn/categorical.py:1296: UserWarning: 59.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n", + " warnings.warn(msg, UserWarning)\n", + "/home/zasz/.local/lib/python3.9/site-packages/seaborn/categorical.py:1296: UserWarning: 11.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n", + " warnings.warn(msg, UserWarning)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "teeplots/viz=tee-swarmplot+x=k+y=solution-time+ext=.pdf\n", + "teeplots/viz=tee-swarmplot+x=k+y=solution-time+ext=.png\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
    " + ], + "image/png": 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a/binder/results/contextual-signal/contextual-signal.ipynb b/binder/results/contextual-signal/contextual-signal.ipynb new file mode 100644 index 00000000..37d57251 --- /dev/null +++ b/binder/results/contextual-signal/contextual-signal.ipynb @@ -0,0 +1,38512 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from teeplot import teeplot as tp\n", + "import numpy as np\n", + "import scipy.stats as stats" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "# get data" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 2, + "source": [ + "reg = pd.read_csv(\"356848/regulation.csv\")\n", + "no_reg = pd.read_csv(\"356848/no-regulation.csv\")\n", + "\n", + "reg['regulation'] = \"enabled\"\n", + "no_reg['regulation'] = \"disabled\"\n", + "\n", + "df = pd.concat([reg, no_reg])\n", + "df" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " update mean_fitness min_fitness max_fitness inferiority replicate \\\n", + "0 0 3.195 0 7 0.456429 10 \n", + "1 1 4.838 3 7 0.691143 10 \n", + "2 2 5.201 2 8 0.650125 10 \n", + "3 3 5.673 3 9 0.630333 10 \n", + "4 4 5.958 2 9 0.662000 10 \n", + "... ... ... ... ... ... ... \n", + "31468 279 12.820 2 15 0.854667 9 \n", + "31469 280 12.898 2 15 0.859867 9 \n", + "31470 281 12.921 3 15 0.861400 9 \n", + "31471 282 13.237 3 15 0.882467 9 \n", + "31472 283 13.363 2 16 0.835187 9 \n", + "\n", + " regulation \n", + "0 enabled \n", + "1 enabled \n", + "2 enabled \n", + "3 enabled \n", + "4 enabled \n", + "... ... \n", + "31468 disabled \n", + "31469 disabled \n", + "31470 disabled \n", + "31471 disabled \n", + "31472 disabled \n", + "\n", + "[37626 rows x 7 columns]" + ], + "text/html": [ + "

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    " + ] + }, + "metadata": {}, + "execution_count": 2 + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "# define teeplot functions" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 3, + "source": [ + "def logx_lineplot(*args, **kwargs):\n", + " g = sns.lineplot(\n", + " *args,\n", + " **kwargs\n", + " )\n", + " g.set_xscale(\"log\")\n", + " plt.axhline(256, c=\"grey\", linestyle=\"dotted\", zorder=-1)\n", + " y = np.array([16*i for i in range(17)])\n", + " plt.yticks(y)\n", + " # todo: change names here" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "# fitness over time" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## process data" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 4, + "source": [ + "\n", + "'''\n", + "new_rows = []\n", + "for (replicate, regulation), filtered in df.groupby([\"replicate\", \"regulation\"]):\n", + " max_update = filtered[\"update\"].max()\n", + "\n", + " for update in range(max_update, 10000):\n", + " new_rows.append({\n", + " \"update\": update,\n", + " \"max_fitness\": 16,\n", + " \"replicate\": replicate,\n", + " \"regulation\": regulation\n", + "})\n", + "\n", + "df = df.append(new_rows)\n", + "df\n", + "'''" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'\\nnew_rows = []\\nfor (replicate, regulation), filtered in df.groupby([\"replicate\", \"regulation\"]):\\n max_update = filtered[\"update\"].max()\\n\\n for update in range(max_update, 10000):\\n new_rows.append({\\n \"update\": update,\\n \"max_fitness\": 16,\\n \"replicate\": replicate,\\n \"regulation\": regulation\\n})\\n\\ndf = df.append(new_rows)\\ndf\\n'" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## graph results (ci: SD)" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 5, + "source": [ + "tp.tee(\n", + " logx_lineplot,\n", + " data=df,\n", + " x=\"update\",\n", + " y=\"max_fitness\",\n", + " ci=\"sd\",\n", + " hue=\"regulation\"\n", + ")" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "teeplots/ci=sd+hue=regulation+viz=logx-lineplot+x=update+y=max-fitness+ext=.pdf\n", + "teeplots/ci=sd+hue=regulation+viz=logx-lineplot+x=update+y=max-fitness+ext=.png\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
    " + ], + "image/png": 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" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## graph results (ci: 95%)" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 6, + "source": [ + "data = df[np.log2(df[\"update\"]+1) % 1.0 < 0.0001]\n", + "len(data)" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "376" + ] + }, + "metadata": {}, + "execution_count": 6 + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 7, + "source": [ + "'''\n", + "tp.tee(\n", + " logx_lineplot,\n", + " data=df,\n", + " x=\"update\",\n", + " y=\"max_fitness\",\n", + " hue=\"regulation\"\n", + ")\n", + "'''" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'\\ntp.tee(\\n logx_lineplot,\\n data=df,\\n x=\"update\",\\n y=\"max_fitness\",\\n hue=\"regulation\"\\n)\\n'" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "# time to solution over number of states (k)" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### process data" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 8, + "source": [ + "x = (df[\"max_fitness\"] != df[\"max_fitness\"].shift()).cumsum()\n", + "for a, b in zip(x, df[\"max_fitness\"]):\n", + " print(a, b)" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1 7\n", + "1 7\n", + "2 8\n", + "3 9\n", + "3 9\n", + "3 9\n", + "3 9\n", + "3 9\n", + "4 10\n", + "4 10\n", + "5 11\n", + "5 11\n", + "5 11\n", + "5 11\n", + "5 11\n", + "5 11\n", + "5 11\n", + "5 11\n", + "5 11\n", + "5 11\n", + "5 11\n", + "5 11\n", + "5 11\n", + "6 12\n", + "6 12\n", + "7 13\n", + "7 13\n", + "7 13\n", + "7 13\n", + "7 13\n", + "7 13\n", + "7 13\n", + "7 13\n", + "7 13\n", + "7 13\n", + "8 15\n", + "8 15\n", + "8 15\n", + "8 15\n", + "8 15\n", + "8 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    " + ], + "image/png": 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+ }, + "metadata": { + "needs_background": "light" + } + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 11, + "source": [ + "box_df = df[(df[\"max_fitness\"] == 16)]\n", + "\n", + "res_df = []\n", + "for (replicate, regulation), filtered in box_df.groupby([\"replicate\", \"regulation\"]):\n", + " sol_time = filtered[\"update\"].min()\n", + " print(replicate, sol_time, \"reg:\", regulation)\n", + " res_df.append({\n", + " \"solution_time\": sol_time,\n", + " \"replicate\": replicate,\n", + " \"regulation\": regulation\n", + " })\n", + "\n", + "all_pairs = {item for item, filtered in df.groupby([\"replicate\", \"regulation\"])}\n", + "sol_pairs = {item for item, filtered in box_df.groupby([\"replicate\", \"regulation\"])}\n", + "\n", + "for (replicate, regulation) in all_pairs - sol_pairs:\n", + " sol_time = 10000\n", + " print(replicate, sol_time, \"reg:\", regulation)\n", + " res_df.append({\n", + " \"solution_time\": sol_time,\n", + " \"replicate\": replicate,\n", + " \"regulation\": regulation\n", + " })\n", + "\n", + "res_df = df.from_records(res_df)" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1 405 reg: disabled\n", + "1 370 reg: enabled\n", + "2 2506 reg: disabled\n", + "2 381 reg: enabled\n", + "3 5028 reg: disabled\n", + "3 92 reg: enabled\n", + "4 803 reg: disabled\n", + "4 452 reg: enabled\n", + "5 347 reg: disabled\n", + "5 509 reg: enabled\n", + "6 6494 reg: disabled\n", + "6 102 reg: enabled\n", + "7 358 reg: disabled\n", + "7 313 reg: enabled\n", + "8 411 reg: disabled\n", + "8 174 reg: enabled\n", + "9 283 reg: disabled\n", + "9 185 reg: enabled\n", + "10 273 reg: disabled\n", + "10 470 reg: enabled\n", + "11 360 reg: disabled\n", + "11 171 reg: enabled\n", + "12 245 reg: disabled\n", + "12 470 reg: enabled\n", + "13 1918 reg: disabled\n", + "13 279 reg: enabled\n", + "14 3045 reg: disabled\n", + "14 227 reg: enabled\n", + "15 1314 reg: disabled\n", + "15 144 reg: enabled\n", + "16 613 reg: disabled\n", + "16 332 reg: enabled\n", + "17 3017 reg: disabled\n", + "17 457 reg: enabled\n", + "18 324 reg: disabled\n", + "18 186 reg: enabled\n", + "19 734 reg: disabled\n", + "19 471 reg: enabled\n", + "20 2975 reg: disabled\n", + "20 348 reg: enabled\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### graph" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 12, + "source": [ + "tp.tee(\n", + " sns.boxplot,\n", + " data=res_df,\n", + " y=\"solution_time\",\n", + " x=\"regulation\"\n", + ")" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "teeplots/viz=boxplot+x=regulation+y=solution-time+ext=.pdf\n", + "teeplots/viz=boxplot+x=regulation+y=solution-time+ext=.png\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 12 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ 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" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## time to solution (swarm plot)" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 13, + "source": [ + "def tee_swarmplot(*args, **kwargs):\n", + " sns.swarmplot(\n", + " linewidth=0.5,\n", + " s=4,\n", + " edgecolor='white',\n", + " *args,\n", + " **kwargs\n", + " )\n", + " sns.boxplot(\n", + " *args,\n", + " **kwargs\n", + " )" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 14, + "source": [ + "tp.tee(\n", + " tee_swarmplot,\n", + " data=res_df,\n", + " y=\"solution_time\",\n", + " x=\"regulation\"\n", + ")" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "teeplots/viz=tee-swarmplot+x=regulation+y=solution-time+ext=.pdf\n", + "teeplots/viz=tee-swarmplot+x=regulation+y=solution-time+ext=.png\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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EpKVqzHoYx4ySMrM/A680eUQiItIincq8DkOBHk0ViIiItGyNWUDpALEnuy143Ql8N0FxRW727NkUFBREHUaLUPfvcOONN0YcScswZMgQZs2aFXUYIs2uMU1SGSev1XYUFBTw1up11HTKjjqUyCVVxmZZWbZxV8SRRC+5rDjqEEQiE+bBvXEnOu7uy5sunJalplM2h4dfFnUYkbrgjNMY1q8nKUnwyppNrNy0O+qQIpX+zvyoQxCJTJg7jP85wbG6qcqlDerfPYPs7Gx+9uwGAH5wyVA27NjHoQot4y7SHoV5cG9qcwQiLU/f7pks21J6ZH/dzoPkZHRUwhBpp0KPkjKzDmb2DTN7LPj5upl1SGRwEq11W/fysdE9SE4yOqUmk98/k+37DkUdlohEpDEP7t1FbMW83wX7XwjK/qWpg5KWYd+hcp5+fQ3fmTqEGnceWLCc6praqMMSkYg0JmGMD9bmrvO8mb3d1AFJy7KlqJTfP9tmxzWISCM05sG9GjMbXLdjZoMATTEuItJONOYO49vAC2a2MdgfAHypySMSEZEWqTEJIwc4g1iiuIrYUqslTR+SiIi0RI1pkvqhu5cSW2lvKnAnsU5vERFpBxrVhxG8Xg783t3/DqQ2fUgiItISNSZhFJrZ3cDVwHwzS2vk+SIi0oo15hf+Z4BngIvdfT+QDXwnEUGJiEjL05jZasuAx+P2dwA7EhGUiIi0PGpSEhGRUBKaMMysn5m9YGZrzWyNmd0YlGeb2QIzWx+8ZgXlZmZ3mFmBma2Mn1rdzGYE9deb2YxExi0iIh+U6DuMauDf3X0kMBG4wcxGAt8DFrr7UGBhsA9wKbGlX4cCMwmG7ZpZNnATcA4wAbipLsmIiEjzSGjCcPcddQssufsBYB2QB1wJzA2qzSX2ICBB+YMe8zrQzcx6AxcDC9y92N33AQuASxIZu4iIHKsxT3qfEjMbAJwFvAH0DDrNIbY2eM9gOw/YGnfatqCsofLj32MmsTsT+vfv34TRt18j+uYw+YyBGM7TS96lcO/BqEMSkYg0S6e3mXUB/gp8M3ha/Ah3d2Ir950yd5/j7vnunp+bm9sUl2zXcjPTOXf0EH624H1uW7iZT08+k7SU5KjDEpGIJDxhBIss/RX4k7vXDcvdFTQ1EbzWLRRdCPSLO71vUNZQuSTQ0D7ZLFi3B4DqWmf51lJ6ZXWOOCoRiUqiR0kZcC+wzt1/FXdoHlA30mkG8FRc+bXBaKmJQEnQdPUMMN3MsoLO7ulBmSTQ+7v2M3lINgBmcGafDHbtL4s4KhGJSqL7MM4jtjLfKjN7Kyj7AfBz4FEzuw7YTOwpcoD5wGVAAVBGMH26uxeb2S3AkqDeze5enODY270d+w7xzvvb+MH0QSSZ8Y+l6yivqo46LBGJSEIThru/AlgDhy+sp74DNzRwrfuA+5ouOglj2YYdLNugB/pFRE96i4hISEoYIiISihKGiIiEooQhIiKhKGGIiEgoShgiIhKKEoaIiISihCEiIqEoYYiISChKGCIiEooShoiIhKKEISIioShhyEmlJCeRnNTQHJIi0l402xKt0jr98wVnkNqxEx2Sja079/CP5RuiDklEIqKEIQ0a3jeHrQfgyVdiSWLWlIF065zG/kMVEUcmIlFQk5Q0qFunNAr3lx/Z31laQZeOHSKMSESipIQhDXrr/d18ckwPBnbvzKjeGZzZuzOFxQejDktEIqImKWlQeVU1d81fwtTRp1FZXcOdT7+Be9RRiUhUlDDkhA5VVPH00oKowxCRFkBNUiIiEooShoiIhKKEISIioSQ0YZjZfWa228xWx5Vlm9kCM1sfvGYF5WZmd5hZgZmtNLNxcefMCOqvN7MZiYxZRETql+g7jAeAS44r+x6w0N2HAguDfYBLgaHBz0zgLoglGOAm4BxgAnBTXZIREZHmk9CE4e4vAcXHFV8JzA225wJXxZU/6DGvA93MrDdwMbDA3YvdfR+wgA8mIRERSbAo+jB6uvuOYHsn0DPYzgO2xtXbFpQ1VP4BZjbTzJaa2dKioqKmjVpEpJ2LtNPb3R1oskfB3H2Ou+e7e35ubm5TXVZERIgmYewKmpoIXncH5YVAv7h6fYOyhspFRKQZRZEw5gF1I51mAE/FlV8bjJaaCJQETVfPANPNLCvo7J4elEkzys1MJ39wL7I6d4w6FBGJSKKH1f4ZeA0YZmbbzOw64OfARWa2HvhosA8wH9gIFAC/B74G4O7FwC3AkuDn5qBMmsnYgT25bOJoSiyDz0wZy9A+GqQm0h4ldC4pd/9sA4curKeuAzc0cJ37gPuaMDQ5iU5pKWSmp7G7pIyJw/tz67OxNTHe2FTM9y8azPrtSyOOUESamyYflA845/Q+jB7cj83FhzmzTxcqqmqOHEs2Q4u1irRPShjyAWef3p9b/m89AAu7pHHDR/L4f5cMZcW2Uiac1o0nX10VcYQiEgUlDPmAyuraI9sHKqo4UF7JY4vX0jurC/ese4/DldURRiciUVHCaEBhYSHJZSWkvzM/6lCa3b5BcPVZI1izu5zPju7KrvWrmJy5n5dfehpqakiPOsAIJZftpbBQCVPaJ81WKx8wf/58dr36V8aUraBTivP30r6s7Diamf/6NczUgyHSXukOowF5eXnsrEjh8PDLog4lEquATtm9+fPqQ2woiq3jPbxXH7LP/TTb9rbfdb3T35lPXl7Pk1cUaYN0hyENKi2rpHfXow/q5XZJ42B5VYQRiUiUdIchDVq3bS+fG9yHb04bRFpKEtt372X/oYqowxIJZ98WeOuPUHEA8r8M3YdGHVGrp4QhJ/SnRatITUmittaprm2yeSKlDZg9ezYFBQUf6txrLpnE0LS91HTI4Jm1+1mzsZDrLj+HrOqdHErryX3PvE1JSckx50wcM4zh/XLYUnSQ599YCcQGp0CsCbl7ThbjRw5kZ/FBNm7bxbfPMTKX/AaAqr2buWvPBDZsfB+AHrk5fGHqCNIritid3Iv7//YqVVWndvc8ZMgQZs2adUrXaOmUMOSk4ofZflhmkJfdhbKKaooPljdBVNJajR4+hLOLHiN14wIALp32G4adNo7TX/sWHN5Hbkoan536W/73yVeOnDP57JFcnvIq6YufZFTfSXSZ8jnmvbiUw4cPA9AzN4evnp9Hzps/obLHGN664AvYodeOnJ9UU05y0tEW+GvOH07/hV+F2hpyMvrwsfP/kycWvtlM/wKtlxKGnNAnJg4nJ6srKUnGus07WLR6c6OvkZxkfP1jE1i9s4yczqkcPnSAv735XgKibR9O5S/7liA7szOpezcc2U8p20VG6iA4vC9WUF1BWs2xAyuG9+5M+uInAUjd9hoDBlxNeno6I0aMYOfOnUw5exg5i74ONZWkHlxA/74X8W7HcYwe8WmSqg6w4bTPUvD4oiPX61BVCrWxGQySDmyna/qp/yosKCjgxhtvPOXrnIpE3+UoYbQTKclJ/NOk4WR06siygkJWbNx10nOG9M6itDaVe56N/XL65rRBZG7YQenhymOvnWT0z81k/6GKI3cPU87oz9A+Oby9aRfpqSk8uWoPy7fsB+DfPzqY1JSkJrlzaY8KCgpYv2YF/bvUnLxyC/Ta/gLO/fKN9FryU6o792ZLbU/efPVV+o2dScaaP1I24CI27CylR1IJhZs3Ultbw949A6nN7EtS6Tbo2JWK2iS+eclQum+Zz76LJrGpupyarv1ILo4loko6cM/vbue0IcPp0CGLDY//D1OnTGHisF54UgrFVan0GHAhHbe/zv6x/8qLzz9Hxea1Ef/LnJotB5MT/h5KGO3EV6aPY+6SnWzdt4vrzu1PdU0t2/cd5OKxgyirqOLvSwuoqjn2F3huZjob9pQd2d+67zBdO6cdkzA6paXwtcsmsPj9EibmpLOzaA+ZndIo2O/8deEWPjGmF6N7pbNu5Z4j51RW1wbNA0oYH1b/LjX8YFxp1GF8SKVUL5vL/pGfp/ZwKVnLbudir6Fs72g451skdenOtL3ruHBiLZWf/TK7n70dNv6R8qn/Rkr1IWqS0zjNKun83Pegtprcjc+RcdHPqci/ng7le/FO3en4zkv88sZPkZyUREXpHsr6ZdPztAqSX/sBAH0uvJnSjIFUjZ9I+cY3+Kf092BcxP8sp+inyzMT/h5KGCeQXFbcZp70rpwygq37Yu2997+2hR+ek0aHkafz3y/vIqdTCrOmDOSu384+5pz3Nqfzpetv5P09h+iUmsz4nGqWPPQoPdI70ad3Twp37OTiiy/hNy9uYldpbPTUf07tRVXFYe5dH+uwfOLtnZzRuQtfHN2Zx9NS6NklhY77NsDKea3yifHksmKOriosH1btwT2ULv3LMWUVhauo3LWeXpM+TfKSuwFIK9lK5xEfpXO/UaTsWI53G8ChzW+T3ndUrGMsYO7sffkesCS84iA5U6+n8xu/hoO7SRt4Aclnfozkdx45Uj9pzztU7NjCoTXPNs8HbiOUMBowZMiQqENoUr0zUkhJMqprnbP7ZdAjM5lfv7WfksNVlByuYktFV66aOoFBZ0+hpBK6VO1n8bPzWLXgUWYOG01VZQVL/7GaS88bR78Jl/DypjKuvaoTXar2U7n18JH3SU1JISs5iU6pyZRV1tC9SypdkmtYMv9hzh80mMO7ynhvyxbOHtRaf+n2jPy7UVhYyKEDyc3yF2VzS03ryI/GQXaw77W1bOs2gTGLfwwlWwGonHwbtz/0HF+75jZytj5Lac9z2FGWSd6Yq/CkFN7YVsWog1V0PhhbzDPp/UVs6H4VvYb8M712rIQO6WzNOo/b/riG2pq282+4+UAynQsTuxipEkYD2trwuJpaZ/CggRQdqGBYrwxyM9JYWLSKgt2xzsWxg/rQa+yX+NTdr1FT65zdvxu/v/UXlFXWMH/pejKTk/juNdews6Scj/92Me7w7LvwfzdOZs6Qan7z3HuM65/FxeNPo2dmR4aNKGFL8SEG52YwrFcGV18+LeJ/AWkNKivKWb7bmDjmOtIObef9PldQvHsXVBxtfkuqraJ7djYb9lTyWmU+7y8q4PqzCui0+o8AnHf2LLYl94XOuXCoiIr+5/Pexk38ZdVqrrjkZiqranjynj9SW9M6+4CipITRgiV6NMzEaZdxxkf7kp5iFCz+O9sHj6MmeNZixdb9vLHsbRZuT+Kxt2Id5Acqa+jTsRqPexxj9Zp1vPGPx5jYvz+lS0r56d931/dWTaI9jHMPIy8vj4rqHa24D6MeSSl0yDmNmrL91G56kNKcgSR17ELHV3/J0PSuVE77MalvzaU2dziZXTrx9YnpJK+8iZqhF1N2yXmkbzq6anNG5S5ydhZwMP/rpCRBxYFiJi5/gol9gdWxafu/PWkAltyLqt3rgbbxfNFPl2eSlpeX0PdQwmjHXn/+2P6ZCwYNZ3D3TmzYU8YN5/Vh/+5NbN53tOmosKSK1G2r+NH08SzceJALB3Vhw7JnqaysbNXDPCVa1iGd3I9+ndTNi6jNGk9p8R4OvvcS2ZM+R8qQCVRWlLN70X2k9R1DddFuumUfIHn5/QAkr3uKDr3zqRh+FWlleyG5AxV5k6hcezvFO9aQ1CmLnEn/TM8LvkxldS37Xn2QrEmfp1NZIVZ5gPIzZ7Fn4Z3gGoARhhJGCxbFX9MfmVTOoYoaemWmkZ56FsO37OObj7xFl7QUvnjBcIb1Gg/AF+tamKaNbvYYJTaEsq30YVzx8Su5/JXboGQrSUDF5F+w7fTB9H37bpJKtpDSfRSv9/gEjz/yJABfzElhYnpW7LmNlI6sKenE/bPvZ/w553LOhAlklGyl5txv8tC857hq/DT6LvohVB4iKfcM3h38L0w4vPNIwkkt28/jFZN5Z/WK6P4BmsiWg8kkevITJQw5Ro+MjpBxdP+s/lk8cf25JCcbXdNTowtMjoi6072ppWXlwd6jT/8nJafQI6sjSWu2AJCyZw19h3+NCz7+OUb2y6K43Fh/7q/oVrWTAx1yWfzmJjKHTiI9dwCD3ptD6rZXwYxrPzMHykuh8hAAHYpW02f0aVjp0WZT81rSep9O2oHEP8OQaENJ/HdDCUNOKrtLWtQhSJw22Y+zZQi88BPIG0/PcZfTs6YC1p0Oe9+DfhMZOe5cRh76NSx+EHqMhPMegtTOpD//K76R8QZc8X3oMQLu/GHseu70yzAYMQ3eGQFF6yD/OkaNPx/Kz4Skw1C2j+Tzv8P1PYZH+9lbEXNvGx0+x8vPz/elS5dGHYZIm9WUgzJSU1MZcFp/SkoPsGvXLpKSkrhq6tn07ORsKamlZ2YaYxZ/9Uj9zZN/RXKS0XfRv8UKklNZPeV+8irWk7X4ZmqyBrF61Pd54OnFXDZ5HLkZHVi9aQ9Lgk7v3r1706FDB7Zu3UpT/Q5sK4MyzGyZu+fXd6xV3WGY2SXA7UAycI+7/zzikESkCVRWVvLe+qPJp7a2lscXLjmyf9nkcYzKHUVK0RpIy+BQUiZdkuKm2k/tzP7Sgzy7ppzJE+9m74HDPPP0Ympra3l60Qf/cNyxY0dCP09b1WruMMwsGXgPuAjYBiwBPuvu9U4AozsMkTameBPsLYDM3tBzVKx/ouA5KFwOZ3wS8lr53B4tRFu5w5gAFLj7RgAzexi4EmjdM4aJSDjZA2I/dTpmxhLFGZ+MKqJ2pzUt0ZoHbI3b3xaUHWFmM81sqZktLSoqatbgRETautaUME7K3ee4e7675+fm5kYdjohIm9KaEkYh0C9uv29QJiIizaA1JYwlwFAzG2hmqcA1wLyIYxIRaTdaTae3u1eb2deBZ4gNq73P3ddEHJaISLvRahIGgLvPB9rGikYiIq1Ma2qSEhGRCLWaB/cay8yKgM1Rx9GGdAf2nLSWSPPTd7Npnebu9Q4zbbMJQ5qWmS1t6OlPkSjpu9l81CQlIiKhKGGIiEgoShgS1pyoAxBpgL6bzUR9GCIiEoruMEREJBQlDBERCUUJox0wsx+Z2bfN7GYz++iHPb+e8gFmtrqR13rAzD7V2BhE6qPvZvNqVVODyKlx9/+KOgYRab10h9FGmdn/M7P3zOwVYFhQduQvKDP7uZmtNbOVZvbLoOwKM3vDzFaY2XNm1jPukmPM7DUzW29mX6nn/ZLN7L/NbElwza8G5WZmd5rZu2b2HNAj4R9eWgUz+7yZvWlmb5nZ3cF36KCZ3Wpmb5vZ63XfQX03WwYljDbIzM4mNv37WOAyYPxxx3OATwCj3P1M4CfBoVeAie5+FvAw8B9xp50JTAMmAf9lZn2Oe9vrgBJ3Hx+831fMbGDwPsOAkcC1wLlN9DGlFTOzEcDVwHnuPhaoAT4HdAZed/cxwEtAXQLQd7MFUJNU2zQZeMLdywDM7Ph1Q0qAcuBeM3saeDoo7ws8Yma9gVTg/bhznnL3w8BhM3uB2Brrb8Udnw6cGdcG3BUYCpwP/Nnda4DtZvZ8E31Gad0uBM4GlpgZQDqwG6jk6PdxGXBRsK3vZgugO4x2yN2rif2negz4GPCP4NBs4E53Hw18FegYf9rxlzlu34BZ7j42+Bno7s82ffTSRhgwN+77MszdfwRU+dGHw2o4+ketvpstgBJG2/QScJWZpZtZBnBF/EEz6wJ0DdYX+TdgTHCoK0eXvZ1x3DWvNLOOQXPWFGIrIMZ7BrjezDoE73G6mXUOYrk6aEfuDUxtkk8ord1C4FNm1gPAzLLN7LQT1Nd3swVQk1Qb5O7LzewR4G1it/nH/wfKAJ4ys47E/vr6VlD+I+AvZrYPeB4YGHfOSuAFYlNJ3+Lu281sQNzxe4ABwHKLtTEUAVcBTxBrX14LbAFea5IPKa2au681s/8EnjWzJKAKuOEEp/wIfTcjp6lBREQkFDVJiYhIKEoYIiISihKGiIiEooQhIiKhKGGIiEgoShgizcTMpgRP1p+ozlgzuyxu/+Nm9r3ERydyckoYIicQTFDXnP9PxhKb/wsAd5/n7j9vxvcXaZAShshxgrUU3jWzB4HVwA/jZjr9cVy9Hwb1XjGzP1uwLoOZvWhm+cF2dzPbVM97TAhmWF1hZq+a2TAzSwVuJvb08VtmdrWZfdHM7oyL6/kgjoVm1j8of8DM7gius9G0poMkiBKGSP2GAr8jNnVKHrG5t8YCZ5vZ+WY2HvgnYtOqXArkN/L67wCTg9lX/wv4qbtXBtuPBHMePXLcObOJzb90JvAn4I64Y72BjxCbG0x3JJIQmhpEpH6b3f11i60VMh1YEZR3IZZMMojNkloOlJvZ3xp5/a7AXDMbSmyyvA4hzpkEfDLY/gNwW9yxJ929Flh73FoRIk1Gdxgi9TsUvBrws7iZToe4+70nObeao/+3OjZQ5xbgBXc/g9jkkA3VC6sibttO8Voi9VLCEDmxZ4AvBzP8YmZ5wQyri4ErgllSuxBrCqqzidhaDwAN9SfEz776xbjyA8TuXurzKrGFsSC22NDL4T+GyKlTwhA5gWDdhIeA18xsFbE1RDLcfQkwj9hMqf8HrCK2MBXAL4lNp72C2Ayq9bkN+FlQJ75p+AVgZF2n93HnzAK+ZGYrgS8AN57yBxRpBM1WK/IhmVkXdz9oZp2Ira0w092XRx2XSKKo01vkw5tjZiOJ9T/MVbKQtk53GCIiEor6MEREJBQlDBERCUUJQ0REQlHCEBGRUJQwREQklP8PFlebVpOlNZoAAAAASUVORK5CYII=" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 15, + "source": [ + "def tee_barswarm(*args, **kwargs):\n", + " sns.swarmplot(\n", + " linewidth=0.5,\n", + " s=4,\n", + " edgecolor='white',\n", + " *args,\n", + " **kwargs\n", + " )\n", + " sns.barplot(\n", + " capsize=0.4,\n", + " errwidth=2,\n", + " *args,\n", + " **kwargs\n", + " )" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 16, + "source": [ + "tp.tee(\n", + " tee_barswarm,\n", + " data=res_df,\n", + " y=\"solution_time\",\n", + " x=\"regulation\"\n", + ")" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "teeplots/viz=tee-barswarm+x=regulation+y=solution-time+ext=.pdf\n", + "teeplots/viz=tee-barswarm+x=regulation+y=solution-time+ext=.png\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
    " + ], + "image/png": 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an optional second parameter: ![Number of generations elapsed before a perfect solution was observed on the Contextual Signal problem. Replicates that did not find a solution are on a dashed line at 10,000 generations.\label{fig:tts-context}](figures/solution-time-contextual-signal.png) + + -![Benchmarking results of 20 replicates shown as a times-speedup of wall time. "Library" refers to the set of instructions tested (see Benchmarking Results section).\label{fig:bench-wall}](figures/wall-time-speedup.png) +![Benchmarking results of 20 replicates shown as a times-speedup of wall time. "Benchmark" refers to the set of instructions tested (see Benchmarking Results section).\label{fig:bench-wall}](figures/wall-time-speedup.png) # Acknowledgements From e8fa7c6a48f51ee65a0cd93357573737ac45a07c Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Mon, 23 Aug 2021 16:18:02 -0400 Subject: [PATCH 099/113] Update figure --- .../solution-time-contextual-signal.png | Bin 62724 -> 67788 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/joss/figures/solution-time-contextual-signal.png b/joss/figures/solution-time-contextual-signal.png index 5faf9541cdebe92aa2d215d8a19d22d0b429dc26..721431895b4ff1d912b202bf1b67f51cf906ffef 100644 GIT binary patch literal 67788 zcmeFZd010dyFQFVwbp_5wGJpltsBxf0E#k0YOUgCR1lFcmq`edVUn3@l{x^r6J#b@ z3?L9tARuF?RB$szLBb3wfe2x!!30AXerpHYx8FJE`u*`;-yi3k>q{?d%-(zLwbp*t zv!3U^?`K~>ZEm#ri(OyH$jEGlPyJ{qBeO0*MrN(i=j*^P4ekfGfWLHtPMi<2^7jY| zbs@XUn7IT6`1l9;c>VNEh&wsZ%im8!75sPLn+rif0fD+|YQBG;pz2TdRC}IWkpUXn 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z?dgW_$0ybYKtm)2-g{`8(-j|oX?%4ar)ks)=365`Sij*6>z`I@m-+cK#=H3QJSH8F9>x4XLlO4b(9t3s0ho8%lQ5T$;i{P|&l&q?-Soqcz9ntaicX{3+85SfVTt`d z7I;uHHfvrOs0U@DrNVKt?!0&wb{m9qL64Hp!{lh`;ZP=jA`fCAhG%!M^;9$&HPobY z@otY&+Cw9KPOd73hqtnaa=r;DtI_JYxRn)+pMt;>UaGGAbmWPOS&Yit7i4(VM`zf; zhC5mf>$>B%un2~Oy+mK+%y$_#WZRZD^9^QZN%35wHMVo~Xc9ukZ2{_p z=*Yf1Bao#gi2QJMQQw=)*4p$x=>Cfl;#xk6$hKY0i_HIVs@MX$Ex%0l zFayV<_v{iXdkFy_isVsEggtdy(&g)yc+DhDW49v78eaV4(BCKg{9zV%TKSUbQI@Oux~u%c<25g{Tpnj_#r=G?MQ7#- zJts4G$DV>Mjh7vTdy>&6cBzkq!4pn?8z(;tRENx3YT|0)LRG7U)8SAZ$)hqCm#HmI zlQaT8cT3mT^`aMF0&?$}Az*lySLz~xS^_>L+@=Hn_d!eJPlLR3+nEtMs}f-M0G0q> zHlt^WXC0o1;Z0|y7#~lCSEjLI+Z3&m;P-kv$A=Jr39RdQQ-gRNyzZliPJvZdg9Qdj zTGJ+hNI_5mVJ#J%K|7%QbQD-i({I&X&PJBDn$I5utb5a0+3RY^DTk}-bhSL8zcd$g zEAL;w?-0!{!7S9}@BZ;gQk4b^3Q6H<;yhUpiI< NI!+Gf|0(6Ge*-;k*{J{k From 022fc5cf1516c308d168e8472f263ec1fc21bf4d Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Mon, 23 Aug 2021 16:20:35 -0400 Subject: [PATCH 100/113] Update plots --- .../contextual-signal/contextual-signal.ipynb | 74092 ++++++++-------- ...x-lineplot+x=update+y=max-fitness+ext=.pdf | Bin 574034 -> 440284 bytes ...x-lineplot+x=update+y=max-fitness+ext=.png | Bin 76700 -> 77834 bytes ...plot+x=regulation+y=solution-time+ext=.pdf | Bin 382800 -> 382800 bytes ...plot+x=regulation+y=solution-time+ext=.png | Bin 0 -> 38969 bytes ...warm+x=regulation+y=solution-time+ext=.pdf | Bin 383899 -> 383899 bytes ...warm+x=regulation+y=solution-time+ext=.png | Bin 65130 -> 65192 bytes ...plot+x=regulation+y=solution-time+ext=.pdf | Bin 384036 -> 384060 bytes ...plot+x=regulation+y=solution-time+ext=.png | Bin 0 -> 67788 bytes 9 files changed, 36995 insertions(+), 37097 deletions(-) create mode 100644 binder/results/contextual-signal/teeplots/viz=boxplot+x=regulation+y=solution-time+ext=.png create mode 100644 binder/results/contextual-signal/teeplots/viz=tee-swarmplot+x=regulation+y=solution-time+ext=.png diff --git a/binder/results/contextual-signal/contextual-signal.ipynb b/binder/results/contextual-signal/contextual-signal.ipynb index 37d57251..6106ca1f 100644 --- a/binder/results/contextual-signal/contextual-signal.ipynb +++ b/binder/results/contextual-signal/contextual-signal.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 42, "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", @@ -23,10 +23,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 43, "source": [ - "reg = pd.read_csv(\"356848/regulation.csv\")\n", - "no_reg = pd.read_csv(\"356848/no-regulation.csv\")\n", + "reg = pd.read_csv(\"regulation.csv\")\n", + "no_reg = pd.read_csv(\"no-regulation.csv\")\n", "\n", "reg['regulation'] = \"enabled\"\n", "no_reg['regulation'] = \"disabled\"\n", @@ -39,33 +39,33 @@ "output_type": "execute_result", "data": { "text/plain": [ - " update mean_fitness min_fitness max_fitness inferiority replicate \\\n", - "0 0 3.195 0 7 0.456429 10 \n", - "1 1 4.838 3 7 0.691143 10 \n", - "2 2 5.201 2 8 0.650125 10 \n", - "3 3 5.673 3 9 0.630333 10 \n", - "4 4 5.958 2 9 0.662000 10 \n", - "... ... ... ... ... ... ... \n", - "31468 279 12.820 2 15 0.854667 9 \n", - "31469 280 12.898 2 15 0.859867 9 \n", - "31470 281 12.921 3 15 0.861400 9 \n", - "31471 282 13.237 3 15 0.882467 9 \n", - "31472 283 13.363 2 16 0.835187 9 \n", + " Unnamed: 0 update mean_fitness min_fitness max_fitness \\\n", + "0 0 0 3.127 0 6 \n", + "1 1 1 4.698 3 7 \n", + "2 2 2 4.947 3 7 \n", + "3 3 3 5.118 3 7 \n", + "4 4 4 5.112 3 9 \n", + "... ... ... ... ... ... \n", + "31468 31468 2971 13.404 2 15 \n", + "31469 31469 2972 13.384 2 15 \n", + "31470 31470 2973 13.235 1 15 \n", + "31471 31471 2974 13.365 2 15 \n", + "31472 31472 2975 13.266 2 16 \n", "\n", - " regulation \n", - "0 enabled \n", - "1 enabled \n", - "2 enabled \n", - "3 enabled \n", - "4 enabled \n", - "... ... \n", - "31468 disabled \n", - "31469 disabled \n", - "31470 disabled \n", - "31471 disabled \n", - "31472 disabled \n", + " inferiority replicate regulation \n", + "0 0.521167 1 enabled \n", + "1 0.671143 1 enabled \n", + "2 0.706714 1 enabled \n", + "3 0.731143 1 enabled \n", + "4 0.568000 1 enabled \n", + "... ... ... ... \n", + "31468 0.893600 20 disabled \n", + "31469 0.892267 20 disabled \n", + "31470 0.882333 20 disabled \n", + "31471 0.891000 20 disabled \n", + "31472 0.829125 20 disabled \n", "\n", - "[37626 rows x 7 columns]" + "[37626 rows x 8 columns]" ], "text/html": [ "

      \n", @@ -86,6 +86,7 @@ " \n", " \n", " \n", + " Unnamed: 0\n", " update\n", " mean_fitness\n", " min_fitness\n", @@ -99,51 +100,56 @@ " \n", " 0\n", " 0\n", - " 3.195\n", " 0\n", - " 7\n", - " 0.456429\n", - " 10\n", + " 3.127\n", + " 0\n", + " 6\n", + " 0.521167\n", + " 1\n", " enabled\n", " \n", " \n", " 1\n", " 1\n", - " 4.838\n", + " 1\n", + " 4.698\n", " 3\n", " 7\n", - " 0.691143\n", - " 10\n", + " 0.671143\n", + " 1\n", " enabled\n", " \n", " \n", " 2\n", " 2\n", - " 5.201\n", " 2\n", - " 8\n", - " 0.650125\n", - " 10\n", + " 4.947\n", + " 3\n", + " 7\n", + " 0.706714\n", + " 1\n", " enabled\n", " \n", " \n", " 3\n", " 3\n", - " 5.673\n", " 3\n", - " 9\n", - " 0.630333\n", - " 10\n", + " 5.118\n", + " 3\n", + " 7\n", + " 0.731143\n", + " 1\n", " enabled\n", " \n", " \n", " 4\n", " 4\n", - " 5.958\n", - " 2\n", + " 4\n", + " 5.112\n", + " 3\n", " 9\n", - " 0.662000\n", - " 10\n", + " 0.568000\n", + " 1\n", " enabled\n", " \n", " \n", @@ -155,65 +161,71 @@ " ...\n", " ...\n", " ...\n", + " ...\n", " \n", " \n", " 31468\n", - " 279\n", - " 12.820\n", + " 31468\n", + " 2971\n", + " 13.404\n", " 2\n", " 15\n", - " 0.854667\n", - " 9\n", + " 0.893600\n", + " 20\n", " disabled\n", " \n", " \n", " 31469\n", - " 280\n", - " 12.898\n", + " 31469\n", + " 2972\n", + " 13.384\n", " 2\n", " 15\n", - " 0.859867\n", - " 9\n", + " 0.892267\n", + " 20\n", " disabled\n", " \n", " \n", " 31470\n", - " 281\n", - " 12.921\n", - " 3\n", + " 31470\n", + " 2973\n", + " 13.235\n", + " 1\n", " 15\n", - " 0.861400\n", - " 9\n", + " 0.882333\n", + " 20\n", " disabled\n", " \n", " \n", " 31471\n", - " 282\n", - " 13.237\n", - " 3\n", + " 31471\n", + " 2974\n", + " 13.365\n", + " 2\n", " 15\n", - " 0.882467\n", - " 9\n", + " 0.891000\n", + " 20\n", " disabled\n", " \n", " \n", " 31472\n", - " 283\n", - " 13.363\n", + " 31472\n", + " 2975\n", + " 13.266\n", " 2\n", " 16\n", - " 0.835187\n", - " 9\n", + " 0.829125\n", + " 20\n", " disabled\n", " \n", " \n", "\n", - "

      37626 rows × 7 columns

      \n", + "

      37626 rows × 8 columns

      \n", "
      " ] }, "metadata": {}, - "execution_count": 2 + "execution_count": 43 } ], "metadata": {} @@ -227,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 44, "source": [ "def logx_lineplot(*args, **kwargs):\n", " g = sns.lineplot(\n", @@ -259,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 45, "source": [ "\n", "'''\n", @@ -288,7 +300,7 @@ ] }, "metadata": {}, - "execution_count": 4 + "execution_count": 45 } ], "metadata": {} @@ -302,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 46, "source": [ "tp.tee(\n", " logx_lineplot,\n", @@ -346,7 +358,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 47, "source": [ "data = df[np.log2(df[\"update\"]+1) % 1.0 < 0.0001]\n", "len(data)" @@ -360,14 +372,14 @@ ] }, "metadata": {}, - "execution_count": 6 + "execution_count": 47 } ], "metadata": {} }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 48, "source": [ "'''\n", "tp.tee(\n", @@ -388,7 +400,7 @@ ] }, "metadata": {}, - "execution_count": 7 + "execution_count": 48 } ], "metadata": {} @@ -409,7 +421,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 49, "source": [ "x = (df[\"max_fitness\"] != df[\"max_fitness\"].shift()).cumsum()\n", "for a, b in zip(x, df[\"max_fitness\"]):\n", @@ -420,9 +432,18 @@ "output_type": "stream", "name": "stdout", "text": [ - "1 7\n", - "1 7\n", - "2 8\n", + "1 6\n", + "2 7\n", + "2 7\n", + "2 7\n", + "3 9\n", + "3 9\n", + "3 9\n", + "3 9\n", + "3 9\n", + "3 9\n", + "3 9\n", + "3 9\n", "3 9\n", "3 9\n", "3 9\n", @@ -430,6 +451,42 @@ "3 9\n", "4 10\n", "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", + "4 10\n", "5 11\n", "5 11\n", "5 11\n", @@ -440,11 +497,47 @@ "5 11\n", "5 11\n", "5 11\n", - "5 11\n", - "5 11\n", - "5 11\n", "6 12\n", "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "6 12\n", + "7 13\n", + "7 13\n", + "7 13\n", + "7 13\n", + "7 13\n", "7 13\n", "7 13\n", "7 13\n", @@ -455,1452 +548,1328 @@ "7 13\n", "7 13\n", "7 13\n", - "8 15\n", - "8 15\n", - "8 15\n", - "8 15\n", - "8 15\n", - "8 15\n", - "8 15\n", - "8 15\n", - "8 15\n", - "8 15\n", - "8 15\n", - "8 15\n", - "8 15\n", - "8 15\n", - "8 15\n", - "8 15\n", - 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+ "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "312 14\n", + "313 16\n", + "314 6\n", "314 6\n", "314 6\n", "314 6\n", @@ -18496,6 +23512,10 @@ "315 7\n", "315 7\n", "315 7\n", + "315 7\n", + "315 7\n", + "315 7\n", + "315 7\n", "316 8\n", "316 8\n", "316 8\n", @@ -18506,11 +23526,6 @@ "316 8\n", "316 8\n", "316 8\n", - "316 8\n", - "316 8\n", - "316 8\n", - "316 8\n", - "317 9\n", "317 9\n", "317 9\n", "317 9\n", @@ -18520,14 +23535,11 @@ "317 9\n", "317 9\n", "317 9\n", - "317 9\n", - "317 9\n", - "318 10\n", - "318 10\n", - "318 10\n", - "318 10\n", - "318 10\n", - "318 10\n", + "317 9\n", + "317 9\n", + "317 9\n", + "317 9\n", + "317 9\n", "318 10\n", "318 10\n", "318 10\n", @@ -18543,16 +23555,11 @@ "319 11\n", "319 11\n", "319 11\n", - "319 11\n", - "319 11\n", - "319 11\n", - "319 11\n", - "319 11\n", - "319 11\n", - "319 11\n", - "319 11\n", - "319 11\n", - "319 11\n", + "320 12\n", + "320 12\n", + "320 12\n", + "320 12\n", + "320 12\n", "320 12\n", "320 12\n", "320 12\n", @@ -18648,116 +23655,12 @@ "321 13\n", "321 13\n", "321 13\n", - "322 14\n", - "322 14\n", - "322 14\n", - "322 14\n", - "322 14\n", - "322 14\n", - "322 14\n", - "322 14\n", - "322 14\n", - "322 14\n", - "322 14\n", - "322 14\n", - "322 14\n", - "322 14\n", - "322 14\n", - "322 14\n", - "322 14\n", - "322 14\n", - "322 14\n", - "322 14\n", - "322 14\n", - "322 14\n", - "322 14\n", - "322 14\n", - "322 14\n", - 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15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "406 15\n", + "407 16\n", "408 8\n", "408 8\n", "408 8\n", @@ -37908,6 +35101,41 @@ "409 9\n", "409 9\n", "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", + "409 9\n", "410 10\n", "410 10\n", "410 10\n", @@ -37960,61 +35188,27 @@ "411 11\n", "411 11\n", "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", - "411 11\n", + "412 12\n", + "412 12\n", + "412 12\n", + "412 12\n", + "412 12\n", + "412 12\n", + "412 12\n", + "412 12\n", + "412 12\n", + "412 12\n", + 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14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", - "414 14\n", "414 14\n", "414 14\n", "414 14\n", @@ -38165,6 +35237,2826 @@ "415 15\n", "415 15\n", "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + 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+ "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", + "415 15\n", "416 16\n" ] } @@ -38173,7 +38065,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 50, "source": [ "df = df.sort_values(by=[\"replicate\", \"regulation\", \"update\"])\n", "\n", @@ -38186,7 +38078,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 51, "source": [ "sns.lineplot(\n", " data=df[(df[\"replicate\"] == 7) & (df[\"regulation\"] == 1)],\n", @@ -38205,7 +38097,7 @@ ] }, "metadata": {}, - "execution_count": 10 + "execution_count": 51 }, { "output_type": "display_data", @@ -38224,7 +38116,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 52, "source": [ "box_df = df[(df[\"max_fitness\"] == 16)]\n", "\n", @@ -38311,7 +38203,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 53, "source": [ "tp.tee(\n", " sns.boxplot,\n", @@ -38337,7 +38229,7 @@ ] }, "metadata": {}, - "execution_count": 12 + "execution_count": 53 }, { "output_type": "display_data", @@ -38363,7 +38255,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 54, "source": [ "def tee_swarmplot(*args, **kwargs):\n", " sns.swarmplot(\n", @@ -38383,13 +38275,17 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 58, "source": [ + "res_df[\"Solution Time\"] = res_df[\"solution_time\"]\n", + "res_df[\"Regulation\"] = res_df[\"regulation\"]\n", + "\n", + "\n", "tp.tee(\n", " tee_swarmplot,\n", " data=res_df,\n", - " y=\"solution_time\",\n", - " x=\"regulation\"\n", + " y=\"Solution Time\",\n", + " x=\"Regulation\"\n", ")" ], "outputs": [ @@ -38418,7 +38314,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 59, "source": [ "def tee_barswarm(*args, **kwargs):\n", " sns.swarmplot(\n", @@ -38440,7 +38336,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 60, "source": [ "tp.tee(\n", " tee_barswarm,\n", @@ -38464,7 +38360,7 @@ "text/plain": [ "
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" 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z3#S6h(medz`jsX@0dcK)#`$hjqR9Cc>YXqpP$+xyO!|lhzt!U;4!2}tEjeQfYJU}z zd}Xm|XEHn?>~>Tg27JpT|L^__$m%j{tMo3tl!J-X#3QqM#ZLxngma}hLfGBnh@n68 zI>K976+c^Tlp<_htw;}}KR<~wU@K&^q5qh{;5*9VFGCfGOdfm&O-aap<-0gTi=rdH zlrV>9MQ;%ke)=-Y;$j{hhuDYR36Ba>6hEfEgg7Q0z!rd+=)QjRuR#9EWnUuDFWlp# z4y7HZ{W%&;%|gvj0R1g?vi4+1_kG3*eybd^ZZVm@SC?Z@c2FP+!kZ+rq{2$Yg)^{+ zE8<4COQl$)Q^u=3)&{Vuqmbfb3r98JAVx9Ox;jpjwF=GOjc^&S#>f}6fwQ;*1$!0W zKlKfi3j$G`@jQSfJz#D=W_>^&AY}IwpO~kA*+9InqHEq2;vsS;D!AU7R&CW&Jn6^d zS);z~-{%^ma2TphY!hnQ4A%)43MW#qw(z183sBp*n@buLN%qm20XaoRAy6E2b&g=^$o`ug8P1@qTOIGEnA35s0RTsV&cU{7O;@>xZ(-xZRd`>##wOE zPSJSYvez82;IW9cBYhsDT&KiR=H-1h7SIyF80YN$Vv;ppds_DQtgK!5_h8D{O@gU$wTSFgRaLxA5{{VzC1tI_d literal 0 HcmV?d00001 From 03a4640d36199479c95825cd9ee11afac7838e1e Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Mon, 23 Aug 2021 18:35:37 -0400 Subject: [PATCH 101/113] Update figure --- joss/paper.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/joss/paper.md b/joss/paper.md index 37cb81b7..e2e21e27 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -165,7 +165,7 @@ In order to save resources and computing time, as soon as a replicate evolved a We excluded random number generating operations from the instruction set to ensure that programs could not solve the Contextual Signal Problem by chance. \autoref{fig:tts-context} shows the number of generations elapsed before a full solution was found. SignalGP-Lite evolved full solutions in half as many generations compared to SignalGP when regulation was enabled. -Moreover, fewer replicates failed to reach a full solution in 10,000 generations under SignalGP-Lite. +Moreover---as opposed to SignalGP---no replicates failed to reach a full solution in 10,000 generations. This may be due to the original SignalGP experiment purging running modules between signals, while our replication did not. The primary object of interest this benchmark, however, is the extent to which each library's problem solving capability improves with the enablement of regulation instructions. @@ -205,7 +205,7 @@ Figure sizes can be customized by adding an optional second parameter: ![Number of generations elapsed before a perfect solution was observed on the Changing Enviroment problem. All replicates found a perfect solution.\label{fig:tts-changing}](figures/solution-time-swarmplot.png) -![Number of generations elapsed before a perfect solution was observed on the Contextual Signal problem. Replicates that did not find a solution are on a dashed line at 10,000 generations.\label{fig:tts-context}](figures/solution-time-contextual-signal.png) +![Number of generations elapsed before a perfect solution was observed on the Contextual Signal problem.\label{fig:tts-context}](figures/solution-time-contextual-signal.png) From a177b78d388b07ce12e166d5b7ea0db4c64dc006 Mon Sep 17 00:00:00 2001 From: Santiago Rodriguez Papa Date: Sun, 17 Oct 2021 21:03:33 -0400 Subject: [PATCH 102/113] Add .pdf figures These figures have been regenerated from scratch. As such, some of the data might differ from their .png counterparts (in particular, the microbenchmarks). Moreover, it looks like the control microbenchmark did not run. 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max-fitness-log2.png} | Bin ...=max-fitness+ext=.png => max-fitness-sd.png} | Bin ...-time+ext=.png => solution-time-boxplot.png} | Bin ...ime+ext=.png => solution-time-swarmplot.png} | Bin ...ogx-lineplot+x=update+y=max-fitness+ext=.png | Bin 52708 -> 0 bytes 5 files changed, 0 insertions(+), 0 deletions(-) rename joss/figures/{hue=k+viz=logx-lineplot+x=update+y=max-fitness+ext=.png => max-fitness-log2.png} (100%) rename joss/figures/{ci=sd+hue=k+viz=logx-lineplot+x=update+y=max-fitness+ext=.png => max-fitness-sd.png} (100%) rename joss/figures/{viz=boxplot+x=k+y=solution-time+ext=.png => solution-time-boxplot.png} (100%) rename joss/figures/{viz=tee-swarmplot+x=k+y=solution-time+ext=.png => solution-time-swarmplot.png} (100%) delete mode 100644 joss/figures/viz=logx-lineplot+x=update+y=max-fitness+ext=.png diff --git a/joss/figures/hue=k+viz=logx-lineplot+x=update+y=max-fitness+ext=.png b/joss/figures/max-fitness-log2.png similarity index 100% rename from joss/figures/hue=k+viz=logx-lineplot+x=update+y=max-fitness+ext=.png rename to joss/figures/max-fitness-log2.png diff --git a/joss/figures/ci=sd+hue=k+viz=logx-lineplot+x=update+y=max-fitness+ext=.png b/joss/figures/max-fitness-sd.png similarity index 100% rename from joss/figures/ci=sd+hue=k+viz=logx-lineplot+x=update+y=max-fitness+ext=.png rename to joss/figures/max-fitness-sd.png diff --git a/joss/figures/viz=boxplot+x=k+y=solution-time+ext=.png b/joss/figures/solution-time-boxplot.png similarity index 100% rename from joss/figures/viz=boxplot+x=k+y=solution-time+ext=.png rename to joss/figures/solution-time-boxplot.png diff --git a/joss/figures/viz=tee-swarmplot+x=k+y=solution-time+ext=.png b/joss/figures/solution-time-swarmplot.png similarity index 100% rename from joss/figures/viz=tee-swarmplot+x=k+y=solution-time+ext=.png rename to joss/figures/solution-time-swarmplot.png diff --git a/joss/figures/viz=logx-lineplot+x=update+y=max-fitness+ext=.png 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