Continuum provides MKL free now - you just need to have a free anaconda.org account to get the license: http://docs.continuum.io/mkl-optimizations/index
HTH, Michael On Wed, Dec 16, 2015 at 12:35 PM Edison Gustavo Muenz < edisongust...@gmail.com> wrote: > Sometime ago I saw this: https://software.intel.com/sites/campaigns/nest/ > > I don't know if the "community" license applies in your case though. It is > worth taking a look at. > > On Wed, Dec 16, 2015 at 4:30 PM, Francesc Alted <fal...@gmail.com> wrote: > >> 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 <fal...@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 <edwardlricha...@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@scipy.org >>>> https://mail.scipy.org/mailman/listinfo/numpy-discussion >>>> >>>> >>> >>> >>> -- >>> Francesc Alted >>> >> >> >> >> -- >> Francesc Alted >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> https://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion >
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