This is a list of summer projects for OSS incubators and research
projects affiliated with CROSS. If you have any questions, please visit our Gitter channel: 
Popper is a workflow execution engine based on Github
actions (GHA). Popper workflows
are defined in a HCL syntax and
behave like GHA workflows.
|
|
| Title |
Popper 2.0 |
| Mentor(s) |
Ivo Jimenez |
| Skills |
Python (strong), Github Actions and HCL (familiar), Docker (familiar) |
| Description |
The goal of this project is to add new features to the workflow execution engine, which is based on Github actions (GHA). Please look at the project link to see a list of associated issues. |
| Link |
https://github.com/systemslab/popper/projects/12 |
|
|
| Title |
Actions Library |
| Mentor(s) |
Ivo Jimenez |
| Skills |
Python (strong), Bash (familiar), Docker (familiar) |
| Description |
The goal of this project is to create a catalog of re-usable actions used in the creation of experimentation pipelines. For example: a Zenodo action that downloads a dataset to be consumed by a later stage in a pipeline; a Spack action that installs the software stack required for an exploration. This has a list of endless possibilities. |
| Link |
https://github.com/systemslab/popper/projects/11 |
|
|
| Title |
Example Workflows |
| Mentor(s) |
Ivo Jimenez |
| Skills |
Python (strong), Bash (familiar), Docker (familiar) |
| Description |
The goal of this project is to create example workflows that showcase the distinct actions available for experimenters. This pipelines are relevant in multiple domains such as Genomics, Weather Prediction, Computational (Neuro/Math/etc) Science, among many other domains. |
| Link |
https://github.com/systemslab/popper/projects/7 |
|
|
| Title |
Workflow Viewer and Editor |
| Mentor(s) |
Ivo Jimenez |
| Skills |
GUI development (strong), Javascript (strong) |
| Description |
Create a javascript-based viewer of Popper pipelines that allows users to visually explore a pipeline and its contents (similar to Github's built-in workflow editor). As part of this project, a stage catalog will also be implemented, that allows users to search for available stages on Github. |
| Link |
https://github.com/systemslab/popper/projects/13 |
|
|
| Title |
Add Support for Popper Workflows in REANA |
| Mentor(s) |
Ivo Jimenez |
| Skills |
Python (strong), Bash (familiar), Docker (familiar), Go (familiar), Kubernetes (familiar) |
| Description |
REANA is a platform for running reproducible analysis. REANA currently supports CWL and Yadage workflows. The goal of this project is to add support for Popper workflows by creating a new reana workflow engine. |
| Link |
https://gitter.im/falsifiable-us/popper |
LGraph (https://github.com/masc-ucsc/lgraph) is the open source infrastructure for Synthesis and Simulation being developed by UCSC. The LGraph goal is to have a common LLVM-like format for synthesis and simulation with a focus on being very fast (Live) and compatible across languages.
|
|
| Title |
LGraph and FIRRTL Bidirectional Bridge |
| Mentor(s) |
Jose Renau (https://users.soe.ucsc.edu/~renau/) |
| Skills |
C++ |
| Description |
FIRRTL (https://github.com/freechipsproject/firrtl) is the also open source infrastructure as "a platform for writing circuit-level transformations.". Both FIRRTL and LGraph share many common goals. Creating a bridge between them allows to leverage features only existing in each one. There are many differences, for example LGraph has ABC synthesis integration and FIRRTL has CHISEL support. The goal of this project is to implement at C++17 import/export for LGraph that handles FIRRTL. |
| Difficulty |
medium |
|
|
| Title |
LGraph LLVM Code Generation |
| Mentor(s) |
Jose Renau (https://users.soe.ucsc.edu/~renau/) |
| Skills |
C++ |
| Description |
LGraph can output Verilog through a Yosys bridge. There is a work-in-progress custom Verilog and Pyrope output for LGraph. The goal of this project is to create an LLVM direct output bridge from LGraph to perform fast hardware simulations. |
| Difficulty |
medium |
|
|
| Title |
LGraph AWS Integration |
| Mentor(s) |
Jose Renau (https://users.soe.ucsc.edu/~renau/) |
| Skills |
C++/Verilog |
| Description |
The goal of this project is to setup a flow in AWS F1 instances to use LGraph for synthesis. LGraph can read Verilog and synthesize with ABC designs for Xilinx FPGAs. The end goal is to create an open source alternative to the Vivado flow used by AWS F1 instances. Since the open source tools are still not ready to handle place and route in AWS F1, for this project, the place and route will use Vivado, but the tools will be hidden behind LGraph. Future projects could target to replace these missing pieces while having a working system. |
| Difficulty |
high |
|
|
| Title |
LGraph FPGA NextPnr Integration |
| Mentor(s) |
Jose Renau (https://users.soe.ucsc.edu/~renau/) |
| Skills |
C++ |
| Description |
Nextpnr is the new Open-source FPGA flow that targets Lattice and potentially Xilinx devices. The goal is to interface between LGraph and nextpnr. Creating a bidirectional bridge. The goal is to allow to leverage the incremental synthesis in LGraph, and target FPGAS like Lattice ECP5 and iCE40. |
| Difficulty |
medium |
Skyhook extends object storage in the cloud with data management functionality. Skyhook formats database table partitions as Flatbuffers, and stores each partition as an object in Ceph distributed storage. Ceph object classes mechanism allow users to define custom read/write methods for objects. We have developed methods (C++) for data management including data processing and indexing.
|
|
| Title |
Database statistics collection on partitioned data |
| Mentor(s) |
Jeff LeFevre |
| Skills |
C++ |
| Description |
This project will develop object-class methods to compute data statistics (histograms) for each object and store them in a query-able format within each storage server’s local RocksDB, then write client code to accumulate all the object-local statistics into global statistics for a given database table. |
| Link |
https://github.com/uccross/skyhook-ceph |
| Difficulty |
high |
|
|
| Title |
Extend current processing methods with column-oriented processing via Arrow |
| Mentor(s) |
Jeff LeFevre |
| Skills |
C++ |
| Description |
This project will develop object-class methods to transform on-disk Flatbuffer data (row oriented) into Apache Arrow format (col-oriented) in memory, then work toward extending our row-oriented filters and aggregations to column oriented versions using the Arrow APIs. |
| Link |
https://github.com/uccross/skyhook-ceph |
| Difficulty |
high |
|
|
| Title |
Extend current aggregations to include sort/groupby for database partitions |
| Mentor(s) |
Jeff LeFevre |
| Skills |
C++ |
| Description |
We have developed methods (C++) for data management including data processing and indexing. This project will develop object-class methods methods to sort/group query result sets. This requires extending the current code (select/project/basic-aggregations - min/max/sum/count) to support groupby and/or orderby. |
| Link |
https://github.com/uccross/skyhook-ceph |
| Difficulty |
medium |
A comprehensive and scalable query answering system over inconsistent databases.
|
|
| Title |
CAvSAT (Consistent Answering via Satisfiability) |
| Mentor(s) |
Akhil Dixit, Phokion G. Kolaitis |
| Skills |
Java, SQL |
| Description |
Inconsistent relational databases are the ones that violate one or more integrity constraints defined over their schema. We are developing CAvSAT, which aims to be a scalable and comprehensive system for consistent query answering. In general, computing consistent answers over inconsistent databases is an intractable problem. However, for certain classes of queries and integrity constraints, there is an efficient algorithm to compute consistent answers. The goal of this project is two-fold. First is to get familiar with the literature and implement this algorithm. Second, the student will conduct experiments with both synthetic and real-world databases, to compare the performance of this algorithm with other methods. |
| Link |
https://github.com/uccross/cavsat |
| References |
https://dl.acm.org/citation.cfm?id=2371212, https://dl.acm.org/citation.cfm?id=303983, https://dl.acm.org/citation.cfm?id=3068334 |
| Difficulty |
Medium |
