[SciPy-Dev] Fastest way to multiply a sparse matrix with another numpy array

[Manoj Kumar]

I'm sorry that I posted this to the developers mailing list. I was meaning
to post this to the users list.


On Mon, Aug 11, 2014 at 5:04 PM, Manoj Kumar <manojkumarsivaraj334 at gmail.com
> wrote:

> Hello,
>
> I was wondering what is the fastest way (format) to multiply a sparse
> matrix with a numpy array. Intuitively, a csr format multiplied with a
> numpy array which is fortran contiguous seems to be the fastest, but I have
> ran a few benchmarks and it seems otherwise. It is also mentioned here
>
> http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.sparse.csc_matrix.html
> that using csr matrices "may" be faster.
>
>
> In [5]: X
> Out[5]:
> <11314x130107 sparse matrix of type '<type 'numpy.float64'>'
>     with 1787565 stored elements in Compressed Sparse Row format>
> In [6]: _, n_features = X.shape
> In [9]: w_c = np.random.rand(n_features, 10)
> In [10]: w_f = np.asarray(w_c, order='f')
> In [13]: csc = sparse.csc_matrix(X)
> In [30]: %timeit X * w_f
> 10 loops, best of 3: 40.5 ms per loop
>
> In [31]: %timeit X * w_c
> 10 loops, best of 3: 37.3 ms per loop
>
> In [32]: %timeit csc *  w_c
> 10 loops, best of 3: 24.3 ms per loop
>
> In [33]: %timeit csc * w_f
> 10 loops, best of 3: 27.3 ms per loop
>
>
> It seems here, using a csc matrix is faster with a C-contiguous numpy
> array which is completely non-intuitive to me. Are there any hard rules for
> this? or is it data dependent?
>
> Sorry for my noobish questions!
> --
> Regards,
> Manoj Kumar,
> GSoC 2014, Scikit-learn
> Mech Undergrad
> http://manojbits.wordpress.com
>



-- 
Regards,
Manoj Kumar,
GSoC 2014, Scikit-learn
Mech Undergrad
http://manojbits.wordpress.com
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