SDDMM speedup goal

[willow-ahrens]

Lets get a real speedup in pydata/sparse#674

[mtsokol]

@willow-ahrens We merged pydata/sparse#674 and with Finch v0.6.26 we have x2.5 speedup (It's still away from x100) for NxN arrays where N=10000 and density is 0.0001 compared to both Numba and SciPy! 🎉 Great progress! Thank you!

Finch
Took 8.787564675013224 s.

Numba
Took 22.904020706812542 s.

SciPy
Took 22.59452811876933 s.

https://github.com/pydata/sparse/actions/runs/9078498356/job/24945650514#step:5:299

[willow-ahrens]

That's great! I'll try to run it in debug mode to see if the autoscheduler can be improved

[willow-ahrens]

Finch
Took 0.009948968887329102 s.

Numba
Took 0.44066866238911945 s.

SciPy
Took 0.2068446477254232 s.

[mtsokol]

Wow! This looks amazing!

Did you change any parameters/code in https://github.com/pydata/sparse/blob/main/examples/sddmm_example.py?

[willow-ahrens]

https://gist.github.com/willow-ahrens/94f35177fa163cc78ed3d0242b6051ef

[willow-ahrens]

I changed some of the code. The issue right now is that the performance of dense matmul is pretty good, so I changed the density and dimensions

[willow-ahrens]

this is a 43x improvement, I think this is good enough to close the issue 🎉 . I am curious what kind of matmul is being used in numba.

[mtsokol]

x43 is what we needed for sure! Thank you!!!