GSoC 2021 Ideas Page

1. LLVM Back-end for the Tensor Algebra Compiler (TACO)
   • Description: The TACO project does some JIT compilation in an ad-hoc manner by writing out *.c files, compiling them and dynamically linking them into the executable. We would like to have a back-end for TACO that produces LLVM bytecode using the LLVM C++ API, and also compiles it in-memory.
   • Skills: LLVM bytecode, LLVM C++ API
   • Difficulty Level: Hard
   • Related Readings/Links:
     • The research paper that moved to the current method of code generation.
     • Some partial work on the back-end so far.
     • A pull request with an old LLVM version.
   • Potential mentors: Guilherme Leobas (@guilhermeleobas), Hameer Abbasi (@hameerabbasi)
2. Completion of Python Bindings for the TACO compiler
   • Description: The TACO project has partial Python bindings, but these are missing tests and API coverage. We'd like to add some tests and more API coverage to the Python bindings.
   • Skills: C++/pybind11 knowledge
   • Difficulty Level: Medium
   • Related Readings/Links:
     • The documentation for TACO.
     • The documentation for pybind11.
     • The source you'll have to change.
   • Potential mentors: Dale Tovar (@daletovar), Hameer Abbasi (@hameerabbasi)
3. Creating a conda-forge package for TACO
   • Description: The TACO project has no conda-forge package. We'd like to have one so we can depend on it in PyData/Sparse
   • Skills: CMake/conda packaging knowledge
   • Difficulty Level: Medium
   • Related Readings/Links:
     • The relevant documentation for conda-forge.
     • The main CMakeLists.txt for the project.
   • Potential mentors: John Lee (@leej3), Hameer Abbasi (@hameerabbasi)
4. CSR/CSC format support and performance
   • Description: The sparse project has the GCXS format, which is a generalization of CSR/CSC. Ideally, we'd like to special case it for CSR/CSC as well as have better performance for certain operations.
   • Skills: Python knowledge, data structures and algorithms
   • Difficulty Level: Easy
   • Related Readings/Links:
     • The documentation for GCXS.
     • The source tree for GCXS.
   • Potential mentors: Dale Tovar (@daletovar), Hameer Abbasi (@hameerabbasi)