是否可以使用BLAS加快稀疏矩阵乘法? [英] Is it possible to use BLAS to speed up sparse matrix multiplication?

问题描述

我目前正在尝试加快大型稀疏(scipy)矩阵乘法.我已经成功地将numpy安装程序与OpenBLAS链接了，从此以后，也可以使用scipy.我已经成功运行了这些测试.

I am currently trying to speed up my large sparse (scipy) matrix multiplications. I have successfully linked my numpy installation with OpenBLAS and henceforth, also scipy. I have run these tests with success.

当我使用numpy.dot(X,Y)时，我可以清楚地看到性能提升，并且同时使用多个内核.但是，当我使用scipy的点功能时，看不到这种性能提升，仍然使用了一个核心.例如:

When I use numpy.dot(X,Y) I can clearly see performance boosts and also that multiple cores are used simultaneously. However, when I use scipy's dot functionality, no such performance boosts can be seen and still one one core is used. For example:

x = scipy.sparse.csr_matrix(numpy.random.random((1000,1000)))
x.dot(x.T)

有人知道我如何使BLAS也能与scipy的点功能一起使用吗?

Does anyone know how I can make BLAS also work with scipy's dot functionality?

推荐答案

BLAS仅用于密集的浮点矩阵. scipy.sparse.csr_matrix的矩阵乘法是使用纯C ++函数完成的，该函数不会对外部BLAS库进行任何调用.

BLAS is just used for dense floating-point matrices. Matrix multiplication of a scipy.sparse.csr_matrix is done using pure C++ functions that don't make any calls to external BLAS libraries.

例如，矩阵乘法是在csr_matmat_pass_1和csr_matmat_pass_2中实现.

For example, matrix-matrix multiplication is implemented here, in csr_matmat_pass_1 and csr_matmat_pass_2.

经过优化的BLAS库通过将密集的输入矩阵分解为较小的块矩阵来有效地利用CPU缓存，从而获得更好的参考位置.我的理解是，这种策略不能轻易应用于稀疏矩阵，因为非零元素可以在矩阵内任意分布.

Optimised BLAS libraries are highly tuned to make efficient use of CPU caches by decomposing the dense input matrices into smaller block matrices in order to achieve better locality-of-reference. My understanding is that this strategy can't be easily applied to sparse matrices, where the non-zero elements may be arbitrarily distributed within the matrix.

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