From a5ac78eb208c1ac07553fe26e0b383c71d0606a1 Mon Sep 17 00:00:00 2001 From: Grigori Fursin Date: Thu, 13 Apr 2017 23:44:17 +0200 Subject: [PATCH] major update to align procedures with our ACM reviewing and badging policy --- wfe/artifact-evaluation/faq.html | 147 ++++++----- wfe/artifact-evaluation/index.html | 5 +- wfe/artifact-evaluation/pact2017.html | 110 +++++--- .../resources/_template.html | 2 +- wfe/artifact-evaluation/reviewing.html | 246 ++++++++++++------ wfe/artifact-evaluation/submission.html | 212 ++++++++------- wfe/artifact-evaluation/submission_extra.html | 40 ++- wfe/artifact-evaluation/templates/ae.aux | 14 - wfe/artifact-evaluation/templates/ae.log | 115 -------- wfe/artifact-evaluation/templates/ae.pdf | Bin 47624 -> 49839 bytes wfe/artifact-evaluation/templates/ae.tex | 40 ++- wfe/artifact-evaluation/templates/review.txt | 32 ++- 12 files changed, 510 insertions(+), 453 deletions(-) delete mode 100644 wfe/artifact-evaluation/templates/ae.aux delete mode 100644 wfe/artifact-evaluation/templates/ae.log diff --git a/wfe/artifact-evaluation/faq.html b/wfe/artifact-evaluation/faq.html index 6fe1fe0..ea7aa2c 100644 --- a/wfe/artifact-evaluation/faq.html +++ b/wfe/artifact-evaluation/faq.html @@ -4,7 +4,7 @@ -

Should my software artifacts be open-source?

+

Do I have to open source my software artifacts?

No, it is not strictly necessary and you can provide your software artifact as a binary. @@ -20,43 +20,16 @@

Is Artifact evaluation blind or double-blind?

AE chair is usually used as a proxy between the authors and the evaluators in case of questions and problems. -

-In the future, we would like to move to a fully open, community-driven evaluation -which was successfully validated at ADAPT'16 - -your comments and ideas are welcome! -

How to pack artifacts?

We do not have strict requirements at this stage. You can pack your artifacts simply in a tar ball, zip file, Virtual Machine or Docker image. -You can also share artifacts via public services such as GitHub or BitBucket. +You can also share artifacts via public services including GitHub, GitLab and BitBucket. + Please see our submission guide for more details. -

-However, from our past Artifact Evaluation, -the most challenging part is to automate and customize -experimental workflows. It is even worse, if you need -to validate experiments using latest software environment -and hardware (rather than quickly outdated VM and Docker -images). Currently, some ad-hoc scripts are used -to implement such workflows. They are difficult to change -and customize, particularly when an evaluator would like -to try other compilers, libraries and data sets. - -

-Therefore, we decided to develop Collective Knowledge Framework (CK) - -a small, portable and customizable framework to help researchers share their artifacts as reusable Python components -with a unified JSON API. This approach should help researchers quickly prototype experimental workflows -(such as multi-objective autotuning) from such components while automatically detecting and resolving -all required software or hardware dependencies. CK is also intended to reduce evaluators' burden -by unifying statistical analysis and predictive analytics (via scikit-learn, R, DNN), -and enabling interactive reports. Please see examples of a live repository, -interactive article -and PLDI'15 CLSmith artifact shared in CK format. -Feel free to contact us, if you would like to use it but need some help to convert your artifacts into CK format. -

Is it possible to provide a remote access to a machine with pre-installed artifacts?

@@ -70,66 +43,115 @@

Is it possible to provide a remote access to a machine with pre-installed ar

Can I share commercial benchmarks or software with evaluators?

Please check the license of your benchmarks, data sets and software. -In case of any doubts, try to find a free alternative. Note, that we have -a preliminary agreement with the EEMBC consortium to let authors -share their EEMBC benchmarks with the evaluators for Artifact Evaluation purposes. +In case of any doubts, try to find a free alternative. In fact, +we strongly suggest you to provide a small subset of free benchmarks +and data sets to simplify evaluation. + +Note, that we have a preliminary agreement with the EEMBC consortium +to let authors share their EEMBC benchmarks with the evaluators for Artifact Evaluation purposes. + + +

Can I engage with the community to evaluate my artifacts?

+ +

+Based on the community feedback, we provided an extra option of open evaluations +to let the community validate artifacts which are publicly available +at GitHub, GitLab, BitBuckets, etc, report issues and help the authors +fix them. + +Note, that at the end, these artifacts still go through traditional +evaluation process via AE committee. We successfully validated +at ADAPT'16 +and CGO/PPoPP'17 AE! -

Should I make my artifacts customizable? How can I plug in benchmarks and datasets from others?

+

How to automate and customize experiments?

+ +For our past AE experience, the major difficulty for evaluators is +that nearly every artifact pack has its own ad-hoc scripts and formats +(see our last AE CGO-PPoPP'17 presentation). + +Things get even worse, if someone would like to validate experiments +using latest software environment and hardware (rather than quickly +outdated VM and Docker images). Most of the submitted scripts are +not easy to change, customize or port, particularly when an evaluator +would like to try other compilers, libraries and data sets. -It is encouraged but not strictly necessary. For example, you can check how it's done in -this artifact (distinguished award winner) -from CGO'17 using an open-source Collective Knowledge framework (CK). -This framework allows you to assemble experimental workflows -from a growing number of artifacts shared in a customizable and reusable CK format -with a simple JSON API and meta information. You can also share your own artifacts -(benchmarks, data sets, models, tools) in the CK format. +

+Therefore, we strongly suggest to use portable workflow frameworks +with unified JSON API such as +Collective Knowledge (CK) +to reduce evaluators' burden. It helps automate and unify your experiments, +plug in different compilers, benchmarks, data sets, tools, predictive models to your workflows, +and unify aggregation and visualization of results. +Please, check out this CGO'17 article from the University of Cambridge ("Software Prefetching for Indirect Memory Accesses") +with CK-based experimental workflow which won distinguished artifact award: +

+ +We now provide free (voluntarily) service to help authors convert their artifacts +and ad-hoc scripts to unified and customizable workflows. Contact AE committee +for more details.

Do I have to make my artifacts public if they pass evaluation?

-You are not obliged to make your artifacts public (particularly in case of commercial artifacts). +No, you don't have to (it may be impossible in some cases of commercial artifacts). Nevertheless, we encourage you to make your artifacts publicly available upon publication -of the proceedings (for example, by including them as "source materials" -in the Digital Library) as a part of our vision for collaborative and reproducible +(for example, by including them as "source materials" in the Digital Library) +as a part of our vision for collaborative and reproducible computer engineering.

Furthermore, if you have your artifacts already publicly available at the time of submission, you may profit from the "public review" option, where you are engaged directly with the community to discuss, evaluate and use your software. See such -examples here (search for "example of public evaluation). +examples here +(search for "example of public evaluation").

How to report and compare empirical results?

-You should undoubtedly run empirical experiments more than once! +First of all, you should undoubtedly run empirical experiments more than once +(we still encounter many cases where researchers measure execution time only once)! There is no universal recipe how many times you should repeat your empirical experiment since it heavily depends on the type of your experiments, machine and environment. +You should then analyze distribution of execution time as shown in the figure below: -

-From our practical experience on collaborative and empirical autotuning -(example), -we usually perform -as many repetitions as needed to "stabilize" expected value -(by analyzing a histogram of the results). But even reporting -variation of the results (for example, standard deviation) is already a good start. +

+

If you have more than one expected value (b), it means that you have several +run-time states on your machine which may be switching during your experiments +(such as adaptive frequency scaling) and you can not reliably compare empirical results. + +However, if there is only one expected value for a given experiment (a), +then you can use it to compare multiple experiments (for example during +autotuning as described +here). +

+You should also report variation of empirical results together with expected values. Furthermore, we strongly suggest you to pre-record results from your platform and provide a script to automatically compare new results with the pre-recorded ones preferably using expected values. This will help evaluators avoid wasting time when trying to dig out and validate results in stdout. For example, see how new results are visualized and compared against the pre-recorded ones -using Collective Knowledge dashboard in this -CGO'17 distinguished artifact. +using CK dashboard +in the CGO'17 distinguished artifact.

How to deal with numerical accuracy and instability?

-If the accuracy of your results depends on a given machine, environment and optimizations (for example, -when optimizing BLAS, DNN, etc), you should provide a script/plugin to automatically report unexpected -loss in accuracy (above provided threshold) as well as any numerical instability. +If the accuracy of your results depends on a given machine, environment and optimizations +(for example, when optimizing BLAS, DNN, etc), you should provide a script/plugin to automatically +report unexpected loss in accuracy (above provided threshold) as well as any numerical instability.

How to validate models or algorithm scalability?

@@ -145,6 +167,9 @@

How to validate models or algorithm scalability?

Is there any page limit for my Artifact Evaluation Appendix?

-There is no limit for the AE Appendix at the time of the submission for Artifact Evaluation, -but there is a 2 page limit for the final AE Appendix in the camera-ready conference paper. -We expect to have a less strict limit in the journals willing to participate in our AE initiative. +There is no limit for the AE Appendix at the time of the submission for Artifact Evaluation. + +

There is a 2 page limit for the AE Appendix in the camera-ready CGO,PPoPP and PACT paper. +There is no page limit for the AE Appendix in the camera-ready SC paper. We also expect +that there will be no page limits for AE Appendices in the journals willing to participate +in our AE initiative. diff --git a/wfe/artifact-evaluation/index.html b/wfe/artifact-evaluation/index.html index 1c24396..881a6a3 100644 --- a/wfe/artifact-evaluation/index.html +++ b/wfe/artifact-evaluation/index.html @@ -128,6 +128,9 @@

Recently completed Artifact Evaluation

Recent events

-

- From our past Artifact Evaluation experience, - we have noticed that the most challenging part is to automate and customize - experimental workflows. It is even worse, if you need - to validate experiments using latest software environment - and hardware (rather than quickly outdated VM and Docker - images). Most of the time, some ad-hoc scripts are used - to implement these workflows. They are very difficult to change - and customize, particularly when an evaluator would like - to try other compilers, libraries and data sets. +
-

- These problems motivated us to develop Collective Knowledge Framework (CK) - - a small, portable and open-source infrastructure and repository to help researchers quickly prototype and share their experimental - workflows with all related artifacts as reusable Python components with a unified JSON API and JSON meta description. - CK supports Linux, MacOS, Windows, Android and reduces the burden of researchers and evaluators by automatically detecting - and resolving all required software dependencies across diverse hardware, - unifying autotuning, statistical analysis and predictive analytics (via scikit-learn, R, etc), - and enabling interactive reports.

- - Please see the distinguished artifact from CGO'17 with a Collective Knowledge Workflow: [ GitHub , Paper with AE appendix and CK workflow , CK concepts ]. - Also check out how ARM uses CK to crowdsource benchmarking of real workloads, - General Motors to collaboratively optimize Caffe framework, - and Imperial College (London) to crowdsource compiler bug detection - (see PLDI'15 artifacts in the CK format). - If you would like to share your artifacts in the reusable CK format, please read - Dr. Michel Steuwer's (who was submitting and evaluating CGO artifacts) informal blog about CK concepts, - Getting Started Guide, - CK portable workflows - and check out this list of already shared CK plugins. + Note that we now provide free service to help authors prepare their artifacts for submission and make them + more portable, reusable and customizable using above open-source frameworks. If you are interested, + feel free to contact AE chairs + for more details. +
+
-

  • - Write a brief artifact abstract to informally describe your artifact including minimal - hardware and software requirements, how it supports your paper, how it can be validated and + Write a brief artifact abstract with an informal check-list to informally describe your artifact + including minimal hardware and software requirements, how it supports your paper, how it can be validated and what the expected result is. It will be used to select appropriate reviewers.

  • Fill in and append AE template (download here) to the PDF of your accepted paper. - Though it should be relatively intuitive, you can check out extra notes about this template based on our past AE experience. + Though it should be relatively intuitive, we still strongly suggest you to + check out extra notes + about how to fill in this template based on our past AE experience.

    • @@ -213,49 +234,32 @@

    Preparing artifacts for submission

    If you encounter problems, find some ambiguities or have any questions, -do not hesitate to contact AE chairs of your conference -or AE steering committee! +do not hesitate to contact AE steering committee!

    If accepted

    - You can now add the following stamp to the final camera-ready version of your paper: - - - - While there are no strict formatting rules for the stamp, - please add it anywhere close to the title. For example, - see PPoPP'15 article - together with this LaTeX example. - You can change \hspace and \raisebox parameters to better fit stamp to your paper. - -

    - We strongly encourage you to submit your AE appendix (up to 2 pages) - as an auxiliary material for the Digital Library - (while removing all unnecessary or confidential information) - along with the final variant of your paper. This will help - readers better understand what was evaluated. + AE chairs will tell you how to add appropriate stamps to the final camera-ready version of your paper. + We also strongly encourage you to add up to 2 pages of your AE appendix + to your camera ready paper while removing all unnecessary or confidential information. + This will help readers better understand what was evaluated.

    Though you are not obliged to publicly release your artifacts (in fact, it is sometimes impossible due to various limitations), we also strongly encourage you to share them with the community - (even if they are not open-source). + even if they are not open-source. You can release them as an auxiliary material in Digital Libraries - together with your AE appendix or use your institutional repository + or use your institutional repository and various public services for code and data sharing.

    Even accepted artifacts may have some unforeseen behavior and limitations discovered during evaluation. Now you have a chance to add related notes - to your paper as a future work (if you wish).. + in the Artifact Appendix as a future work..

    @@ -263,7 +267,7 @@

    A few examples of accepted artifacts from the past con - + +

    Description

    +
    +

    How delivered

    Describe, how reviewers can access your artifact: @@ -209,7 +219,9 @@

    Experiment customization

    run-time systems), hardware, etc. Also, describe if it is possible to parameterize your workflow (whatever is applicable such as changing number of threads, optimizations, CPU/GPU frequency, - accuracy, autotuning scenario, etc). + accuracy, autotuning scenario, etc). See this + CGO'17 distinguished artifact + as an example of a portable, customizable and reusable workflow.

    Notes

    diff --git a/wfe/artifact-evaluation/templates/ae.aux b/wfe/artifact-evaluation/templates/ae.aux deleted file mode 100644 index b2e460b..0000000 --- a/wfe/artifact-evaluation/templates/ae.aux +++ /dev/null @@ -1,14 +0,0 @@ -\relax -\@writefile{toc}{\contentsline {section}{\numberline {A}Artifact description}{1}} -\@writefile{toc}{\contentsline {subsection}{\numberline {A.1}Abstract}{1}} -\@writefile{toc}{\contentsline {subsection}{\numberline {A.2}Description}{1}} -\@writefile{toc}{\contentsline {subsubsection}{\numberline {A.2.1}Check-list (artifact meta information)}{1}} -\@writefile{toc}{\contentsline {subsubsection}{\numberline {A.2.2}How delivered}{1}} -\@writefile{toc}{\contentsline {subsubsection}{\numberline {A.2.3}Hardware dependencies}{1}} -\@writefile{toc}{\contentsline {subsubsection}{\numberline {A.2.4}Software dependencies}{1}} -\@writefile{toc}{\contentsline {subsubsection}{\numberline {A.2.5}Datasets}{1}} -\@writefile{toc}{\contentsline {subsection}{\numberline {A.3}Installation}{1}} -\@writefile{toc}{\contentsline {subsection}{\numberline {A.4}Experiment workflow}{1}} -\@writefile{toc}{\contentsline {subsection}{\numberline {A.5}Evaluation and expected result}{1}} -\@writefile{toc}{\contentsline {subsection}{\numberline {A.6}Notes}{1}} -\newlabel{sigplanconf@finalpage}{{A.6}{1}} diff --git a/wfe/artifact-evaluation/templates/ae.log b/wfe/artifact-evaluation/templates/ae.log deleted file mode 100644 index 708068e..0000000 --- a/wfe/artifact-evaluation/templates/ae.log +++ /dev/null @@ -1,115 +0,0 @@ -This is pdfTeX, Version 3.1415926-2.5-1.40.14 (MiKTeX 2.9 64-bit) (preloaded format=pdflatex 2015.1.13) 16 OCT 2015 21:41 -entering extended mode -**ae.tex - 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Use just a few informal keywords in all fields applicable to your artifacts +and remove the rest. This information is needed to find appropriate reviewers and gradually +unify artifact meta information in Digital Libraries.} {\small \begin{itemize} @@ -47,10 +49,23 @@ \subsubsection{Check-list (artifact meta information)} \item {\bf Run-time state: } \item {\bf Execution: } \item {\bf Output: } - \item {\bf Experiment workflow: } + \item {\bf Experiments: } + \item {\bf Workflow frameworks used?: } \item {\bf Publicly available?: } \end{itemize} -} + +{\em Obligatory. Provide brief assessment of your artifacts +based on {\em https://www.acm.org/publications/policies/artifact-review-badging}}: + +\begin{itemize} + \item {\bf Artifacts publicly available?: } + \item {\bf Artifacts functional?:} + \item {\bf Artifacts reusable?:} + \item {\bf Results validated?:} +\end{itemize} + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\subsection{Description} \subsubsection{How delivered} @@ -60,7 +75,7 @@ \subsubsection{Hardware dependencies} \subsubsection{Software dependencies} -\subsubsection{Datasets} +\subsubsection{Data sets} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Installation} @@ -75,6 +90,9 @@ \subsection{Evaluation and expected result} {\em Obligatory} +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\subsection{Experiment customization} + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Notes} diff --git a/wfe/artifact-evaluation/templates/review.txt b/wfe/artifact-evaluation/templates/review.txt index ca17899..36b56bc 100644 --- a/wfe/artifact-evaluation/templates/review.txt +++ b/wfe/artifact-evaluation/templates/review.txt @@ -1,46 +1,56 @@ ### Review template for Artifact Evaluation V20151015 +Please, check details about scoring here: http://cTuning.org/ae/reviewing.html + Possible scores: -* +2: significantly exceeded expectations * +1: exceeded expectations -* 0: met expectations +* 0: met expectations (or inapplicable) * -1: fell below expectations -* -2: significantly fell below expectations ################################################################ -# Documentation: (add your score) +# Artifacts publicly available (if applicable): (add your score) + +(Describe your experience, success/failure, +encountered problems, and suggestions to authors) + +# Package complete: (add your score) (Describe your experience, success/failure, encountered problems, and suggestions to authors) -# Packaging: (add your score) +# Well documented: (add your score) (Describe your experience, success/failure, encountered problems, and suggestions to authors) -# Installation: (add your score) +# Exercisable: (add your score) (Describe your experience, success/failure, encountered problems, and suggestions to authors) -# Use case: (add your score) +# Consistent: (add your score) (Describe your experience, success/failure, encountered problems, and suggestions to authors) -# Expected behavior: (add your score) +# Artifacts functional: (add your overall score) (Describe your experience, success/failure, encountered problems, and suggestions to authors) -# Relevance: (add your score) +# Artifacts customizable and reusable: (add your score) (Describe your experience, success/failure, encountered problems, and suggestions to authors) -# Customization and reusability: (add your score) +# Results replicated (if applicable): (add your score) +(Describe your experience, success/failure, +encountered problems, and suggestions to authors) + + +# Results reproduced (if applicable): (add your score) (Describe your experience, success/failure, encountered problems, and suggestions to authors) @@ -49,5 +59,7 @@ encountered problems, and suggestions to authors) # Overall score: (add your score) +Did artifacts and results match authors' description? + Provide explanation of your score and what can be improved during rebuttal.