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 > >
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