Plans for new sparse compilation backend for PyData/Sparse

Hello everyone

The stated goal for sparse is to provide a NumPy-like API with a sparse representation of arrays. To this end, Quansight and I have been collaborating with researchers at MIT CSAIL - in particular Prof. Amarasinge’s group and the TACO team - to develop a performant and production-ready package for N-dimensional sparse arrays. There were several attempts made to explore this over the last couple of years, including a LLVM back-end for TACO, and a pure-C++ template-metaprogramming approach called XSparse.

To this end, we, at Quansight, are happy to announce that we have received funding from DARPA, together with our partners from MIT, under their Small Business Innovation Research (SBIR) program to build out sparse using state-of-the-art just-in-time compilation strategies to boost performance for users. Additionally, as an interface, we’ll adopt the Array API standard which was championed by major libraries like NumPy, PyTorch and CuPy.

More details about the plan are posted on GitHub — please join in the discussion there, to keep it all in one place.