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 <https://www.csail.mit.edu/> - in particular
Prof. Amarasinge's group <https://www.csail.mit.edu/research/commit-group> and
the TACO <https://github.com/tensor-compiler/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
<https://github.com/Quansight-Labs/taco/pulls?q=is%3Apr+llvm> for TACO
<https://github.com/tensor-compiler/taco/>, and a pure-C++
template-metaprogramming approach called XSparse
<https://github.com/hameerabbasi/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
<https://www.darpa.mil/work-with-us/for-small-businesses/HR0011SB20234-06> 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 <https://data-apis.org/array-api/latest/> which was
championed by major libraries like NumPy, PyTorch and CuPy.
More details about the plan are posted on GitHub
<https://github.com/pydata/sparse/discussions/618> — please join in the
discussion there, to keep it all in one place.
Best Regards,
Hameer Abbasi
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