Hi, I just ran both on the same hardware and got a slightly faster computation with numpy:
Matlab R2012a: 16.78 s (best of 3) numpy (python 3.4, numpy 1.10.1, anaconda accelerate (MKL)): 14.8 s (best of 3) The difference could because my Matlab version is a few years old, so it's MKL would be less up to date. Greg On Thu, Dec 17, 2015 at 9:29 AM, Andy Ray Terrel <andy.ter...@gmail.com> wrote: > > > On Thu, Dec 17, 2015 at 5:52 AM, Sturla Molden <sturla.mol...@gmail.com> > wrote: > >> On 17/12/15 12:06, Francesc Alted wrote: >> >> Pretty good. I did not know that OpenBLAS was so close in performance >>> to MKL. >>> >> >> MKL, OpenBLAS and Accelerate are very close in performance, except for >> level-1 BLAS where Accelerate and MKL are better than OpenBLAS. >> >> MKL requires the number of threads to be a multiple of four to achieve >> good performance, OpenBLAS and Accelerate do not. It e.g. matters if you >> have an online data acquisition and DSP system and want to dedicate one >> processor to take care of i/o tasks. In this case OpenBLAS and Accelerate >> are likely to perform better than MKL. >> >> > The last time I benchmarked them MKL was much better at tall skinny > matrices. > > >> >> Sturla >> >> >> _______________________________________________ >> 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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