[Numpy-discussion] performance solving system of equations in numpy and MATLAB

[Francesc Alted]

Hi,

Probably MATLAB is shipping with Intel MKL enabled, which probably is the
fastest LAPACK implementation out there.  NumPy supports linking with MKL,
and actually Anaconda does that by default, so switching to Anaconda would
be a good option for you.

Here you have what I am getting with Anaconda's NumPy and a machine with 8
cores:

In [1]: import numpy as np

In [2]: testA = np.random.randn(15000, 15000)

In [3]: testb = np.random.randn(15000)

In [4]: %time testx = np.linalg.solve(testA, testb)
CPU times: user 5min 36s, sys: 4.94 s, total: 5min 41s
Wall time: 46.1 s

This is not 20 sec, but it is not 3 min either (but of course that depends
on your machine).

Francesc

2015-12-16 18:34 GMT+01:00 Edward Richards <edwardlrichards at gmail.com>:

> I recently did a conceptual experiment to estimate the computational time
> required to solve an exact expression in contrast to an approximate
> solution (Helmholtz vs. Helmholtz-Kirchhoff integrals). The exact solution
> requires a matrix inversion, and in my case the matrix would contain ~15000
> rows.
>
> On my machine MATLAB seems to perform this matrix inversion with random
> matrices about 9x faster (20 sec vs 3 mins). I thought the performance
> would be roughly the same because I presume both rely on the same LAPACK
> solvers.
>
> I will not actually need to solve this problem (even at 20 sec it is
> prohibitive for broadband simulation), but if I needed to I would
> reluctantly choose MATLAB . I am simply wondering why there is this
> performance gap, and if there is a better way to solve this problem in
> numpy?
>
> Thank you,
>
> Ned
>
> #Python version
>
> import numpy as np
>
> testA = np.random.randn(15000, 15000)
>
> testb = np.random.randn(15000)
>
> %time testx = np.linalg.solve(testA, testb)
>
> %MATLAB version
>
> testA = randn(15000);
>
> testb = randn(15000, 1);
> tic(); testx = testA \ testb; toc();
>
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>
>


-- 
Francesc Alted
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