[SciPy-Dev] Improving performance of sparse matrix multiplication

[Ralf Gommers]

On Tue, Aug 29, 2017 at 4:14 AM, marc <marc.barbry at mailoo.org> wrote:

> Dear Scipy developers,
>
> We are developing a program that perform a large number of sparse matrix
> multiplications. We recently wrote a Python version of this program for
> several reasons (the original code is in Fortran).
>
> We are trying now to improve the performance of the Python version and we
> noticed that one of the bottlenecks are the sparse matrix multiplications,
> as example,
>
> import numpy as np
> from scipy.sparse import csr_matrix
>
> row = np.array([0, 0, 1, 2, 2, 2])
> col = np.array([0, 2, 2, 0, 1, 2])
> data = np.array([1, 2, 3, 4, 5, 6], dtype=np.float32)
>
> csr = csr_matrix((data, (row, col)), shape=(3, 3))
> print(csr.toarray())
>
> A = np.array([1, 2, 3], dtype=np.float32)
>
> print(csr*A)
>
>
> I started to look at the Scipy code to see how this functions were
> implemented, and realized that there is no openmp parallelization over the
> for loops. Like in function  csr_matvec in sparse/sparsetools/csr.h (line
> 1120). Is it possible to parallelize this loops with openmp?


Short answer: no openmp in scipy. It has been discussed a number of times
before, see for example
http://numpy-discussion.10968.n7.nabble.com/Cython-based-OpenMP-accelerated-quartic-polynomial-solver-td41263.html#a41298

Cheers,
Ralf




> Do you have maybe better ideas to improve the performances for this kind
> of operations?
>
> Best regards,
> Marc Barbry
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