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

[Francesc Alted]

Sorry, I have to correct myself, as per:
http://docs.continuum.io/mkl-optimizations/index it seems that Anaconda is
not linking with MKL by default (I thought that was the case before?).
After installing MKL (conda install mkl), I am getting:

In [1]: import numpy as np
Vendor:  Continuum Analytics, Inc.
Package: mkl
Message: trial mode expires in 30 days

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 1min, sys: 468 ms, total: 1min 1s
Wall time: 15.3 s


so, it looks like you will need to buy a MKL license separately (which
makes sense for a commercial product).

Sorry for the confusion.
Francesc


2015-12-16 18:59 GMT+01:00 Francesc Alted <faltet at gmail.com>:

> 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();
>>
>> _______________________________________________
>> NumPy-Discussion mailing list
>> NumPy-Discussion at scipy.org
>> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>>
>>
>
>
> --
> Francesc Alted
>



-- 
Francesc Alted
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/numpy-discussion/attachments/20151216/97b695c2/attachment.html>