[SciPy-user] Getting the right numerical libraries for scipy

William K. Coulter wcoulter@berkeley....
Fri Apr 3 10:32:43 CDT 2009

```Thanks all for your replies.

While this benchmark is interesting, it is not the one that I was
trying.  I'm interested in computations where just *one* of the matrices
is actually sparse.  I'm looking at something more like the following in
your script's language -- I'm guessing on the syntax for Timer:

c = np.random.random((s,s))     # c is dense
b = np.random.random((s, s))
b[b > d] = 0                    # b sparse, but dense representation
b_s = ss.csc_matrix(b)          # b_s sparse, with sparse rep.

timer_dense = Timer("np.dot(b,c)", "import __main__ as m")    # test1
timer_sparse = Timer("b_s.matmat(c)", "import __main__ as m") # test2

Could you look at those curves?  Would you not expect the multiplication
using the sparse version of b (b_s) to be faster?

On matlab, the equivalent to test2 runs far faster (20x) than test1,
whereas in python, I'm seeing completely the opposite.  Since there are
questions about the dense/sparse ratio, I get speedup in matlab on using
sparse representation for one matrix for ratios (d) of 0.5 and lower.

Any ideas for the performance difference?

-- Will

From Stéfan van der Walt on 4/3/2009 6.55:
> 2009/4/3 David Cournapeau <david@ar.media.kyoto-u.ac.jp>:
>> Maybe a more useful benchmark would be the dense/sparse ratio as a
>> function of density for a given size,
>
> http://mentat.za.net/refer/bench_ratio.png
>
> Cheers
> Stéfan
>
>
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--
William K. Coulter                          wcoulter@berkeley.edu
Graduate Student, Helen Wills Neuroscience Institute, UC Berkeley
```