[SciPy-Dev] Improving performance of sparse matrix multiplication

[Michele Martone]

Dear Marc,

did you try the PyRSB prototype:
 
 "librsb is a high performance sparse matrix library implementing the
  Recursive Sparse Blocks format, which is especially well suited for
  multiplications in iterative methods on huge sparse matrices.
  PyRSB is a Cython-based Python interface to librsb."
 https://github.com/michelemartone/pyrsb

?

How large are your matrices ?

Are they symmetric ?

If your matrices are large you might get quite of a speedup;
if symmetric, even better.

Best regards,
Michele

p.s.: PyRSB (a thin interface) is a prototype, but librsb itself
 http://librsb.sourceforge.net/
is in a mature state and usable also from Fortran, and OpenMP based.

On 20170828 at 18:14, marc 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? Do you have
> maybe better ideas to improve the performances for this kind of operations?
> 
> Best regards,
> Marc Barbry
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> SciPy-Dev at python.org
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