[SciPy-User] In which numpy modules should MKL be better then ATLAS
Tue Oct 4 08:10:32 CDT 2011
Atlas will usually get close to mkl speed, but it will take more time and
you must compile it yourself. So on recent cpu, mkl should be faster.
There is 2 cases that could make this different: do atlas and mkl used the
same number ofthread? You should count only real core on the cpu, not
hyperthread core. Having more then the number of real core will probably
slow thing down.
Some version of mkl have a speed problem with gemm(used by dot). The speed
penalty is 2x. I don't know witch version are afftected.
On Oct 1, 2011 6:35 AM, "Klonuo Umom" <email@example.com> wrote:
> It's old Intel P4 3Ghz
> ATLAS/LAPACK are build from source so maybe more optimized
> On Sat, Oct 1, 2011 at 12:23 PM, Dag Sverre Seljebotn <
> firstname.lastname@example.org> wrote:
>> On 10/01/2011 11:55 AM, Klonuo Umom wrote:
>> > I had a chance to test this sample on different setups on same PC:
>> > import numpy as np
>> > A=np.ones((1000,1000))
>> > B=np.ones((1000,1000))
>> > %timeit np.dot(A, B)
>> > because of OS reinstalling.
>> > 1x = ATLAS on Linux (reference speed)
>> > 2x = MKL with GNU compilers on Linux
>> > 2x = MKL with Intel compilers on Windows 7
>> > 30x = bare numpy
>> > I didn't plan to do this so I didn't test additional calculations, and
>> > was using latest version to date for all products.
>> > On Internet I usually find that MKL should outperform ATLAS. I'm
>> > what would linalg module testing give, but as said I didn't test it. So
>> > in which modules should user expect impact of MKL over ATLAS? In matrix
>> > dot product obviously not.
>> What CPU are you on? MKL is tuned for Intel CPUs, perhaps ATLAS
>> outperforms it on AMD ones.
>> Dag Sverre
>> SciPy-User mailing list
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