Perhaps it's my inability to properly use openmp, but when working on scikits.image on algorithms doing per-pixel manipulation with numpy arrays (using Cython), i saw better performance using Python threads and releasing the GIL than I did with openmp. I found the openmp overhead to be quite large, and for the size the images i was working with (5 MP), the overhead wasn't worth it. I made a post to Cython-dev about it. cheers, Chris On Sat, Feb 13, 2010 at 7:25 AM, René Dudfield wrote:

hi,

see: http://numcorepy.blogspot.com/

They see a benefit when working with large arrays. Otherwise you are limited by memory - and the extra cores don't help with memory bandwidth.

cheers,

On Sat, Feb 13, 2010 at 2:20 PM, David Cournapeau wrote:

...

On Sat, Feb 13, 2010 at 6:20 PM, Wolfgang Kerzendorf wrote:

...

Dear all,

I don't know much about parallel programming so I don't know how easy it is to do that: When doing simple arrray operations like adding two arrays or adding a number to the array, is numpy able to put this on multiple cores? I have tried it but it doesnt seem to do that. Is there a special multithread implementation of numpy.

Depending on your definition of simple operations, Numpy supports multithreaded execution or not. For ufuncs (which is used for things like adding two arrays together, etc...), there is no multithread support.

...

IDL has this feature where it checks how many cores available and uses

them. This feature in numpy would make an already amazing package even better.

AFAIK, using multi-thread at the core level of NumPy has been tried only once a few years ago, without much success (no significant performance improvement). Maybe the approach was flawed in some ways. Some people have suggested using OpenMP, but nobody has every produced something significant AFAIK:

http://mail.scipy.org/pipermail/numpy-discussion/2008-March/031897.html

Note that Linear algebra operations can run in // depending on your libraries. In particular, the dot function runs in // if your blas/lapack does.

cheers,

David _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion

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