Plans for new sparse compilation backend for PyData/Sparse
Hameer Abbasi <[email protected]> Wed, 3 Jan 2024 14:34:17 +0100
| Newsgroups | gmane.comp.python.scientific.devel,gmane.comp.python.numeric.general |
|---|---|
| Message-ID | <[email protected]> |
--===============0104302331157273780== Content-Type: multipart/alternative; boundary="Apple-Mail=_65ACEEF1-B62B-4FE8-8209-921ACFD0150A" --Apple-Mail=_65ACEEF1-B62B-4FE8-8209-921ACFD0150A Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=utf-8 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=3Dis%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> =E2=80=94 please join = in the discussion there, to keep it all in one place. Best Regards, Hameer Abbasi= --Apple-Mail=_65ACEEF1-B62B-4FE8-8209-921ACFD0150A Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset=utf-8 <html><head><meta http-equiv=3D"content-type" content=3D"text/html; = charset=3Dutf-8"></head><body style=3D"overflow-wrap: break-word; = -webkit-nbsp-mode: space; line-break: after-white-space;"><p = dir=3D"auto">Hello everyone,</p><p dir=3D"auto">The stated goal = for <code class=3D"notranslate">sparse</code> 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 <a = href=3D"https://www.csail.mit.edu/" rel=3D"nofollow">MIT = CSAIL</a> - in particular <a = href=3D"https://www.csail.mit.edu/research/commit-group" = rel=3D"nofollow">Prof. Amarasinge's group</a> and the <a = href=3D"https://github.com/tensor-compiler/taco/">TACO</a> 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 <a = href=3D"https://github.com/Quansight-Labs/taco/pulls?q=3Dis%3Apr+llvm">LLV= M back-end</a> for <a = href=3D"https://github.com/tensor-compiler/taco/">TACO</a>, and a = pure-C++ template-metaprogramming approach called <a = href=3D"https://github.com/hameerabbasi/xsparse">XSparse</a>.</p><p = dir=3D"auto">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 <a = href=3D"https://www.darpa.mil/work-with-us/for-small-businesses/HR0011SB20= 234-06" rel=3D"nofollow">Small Business Innovation Research (SBIR) = program</a> to build out <code = class=3D"notranslate">sparse</code> using state-of-the-art = just-in-time compilation strategies to boost performance for users. = Additionally, as an interface, we'll adopt the <a = href=3D"https://data-apis.org/array-api/latest/" rel=3D"nofollow">Array = API standard</a> which was championed by major libraries like = NumPy, PyTorch and CuPy.</p><p dir=3D"auto">More details about the plan = are <a = href=3D"https://github.com/pydata/sparse/discussions/618">posted on = GitHub</a> =E2=80=94 please join in the discussion there, to keep = it all in one place.</p><p dir=3D"auto">Best Regards,</p><p = dir=3D"auto">Hameer Abbasi</p></body></html>= --Apple-Mail=_65ACEEF1-B62B-4FE8-8209-921ACFD0150A-- --===============0104302331157273780== Content-Type: text/plain; charset="us-ascii" MIME-Version: 1.0 Content-Transfer-Encoding: 7bit Content-Disposition: inline _______________________________________________ SciPy-Dev mailing list -- [email protected] To unsubscribe send an email to [email protected] https://mail.python.org/mailman3/lists/scipy-dev.python.org/ Member address: [email protected] --===============0104302331157273780==--