On 07.12.2011, at 9:38PM, Oleg Mikulya wrote:
> Agree with your statement. Yes, it is MKL, indeed. For linear equations it is
> no difference, but there is difference for other functions. And yes, my
> suspicions is just threading options. How to pass them to MKL from python?
> Should I change some compiling options or environment options?
>
You could check by monitoring the CPU usage while running the tasks - if it
remains
around 100% it is rather not using multiple threads. Generally MKL (if you
linked the
multi-threaded version, which seems to be the case, as mkl_intel_thread is in
the libs)
heeds the OMP_NUM_THREADS environment variable like other OpenMP programs.
If that's set to your no. of cores before starting Python, it should be
inherited; might also
be possible to set it within Python (in any case you can check it with
os.getenv()).
I don't know if matlab sets different defaults so multiple threads are
automatically used;
normally I'd also expect Python to use all available cores if OMP_NUM_THREADS
is not set at all…
Cheers,
Derek
> On Wed, Dec 7, 2011 at 2:02 AM, Pauli Virtanen <[email protected]> wrote:
> 06.12.2011 23:31, Oleg Mikulya kirjoitti:
> > How to make Numpy to match Matlab in term of performance ? I have tryied
> > with different options, using different MKL libraries and ICC versions,
> > still Numpy is below Matalb for certain basic tasks by ~2x. About 5
> > years ago I was able to get about same speed, not anymore. Matlab
> > suppose to use same MKL, what it the reason of such Numpy slowness
> > (beside one, yet fundamental, task)?
>
> There should be no reason for a difference. It simply makes the calls to
> the external library, and the wrapper code is straightforward.
>
> If Numpy indeed is linked against MKL (check the build log), then one
> possible reason could be different threading options passed to MKL.
>
> --
> Pauli Virtanen
>
> _______________________________________________
> NumPy-Discussion mailing list
> [email protected]
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
_______________________________________________
NumPy-Discussion mailing list
[email protected]
http://mail.scipy.org/mailman/listinfo/numpy-discussion
