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 <courn...@gmail.com>wrote: > On Sat, Feb 13, 2010 at 6:20 PM, Wolfgang Kerzendorf > <wkerzend...@googlemail.com> 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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