[Numpy-discussion] parallel numpy (by Brian Granger) - any info?

Matthieu Brucher matthieu.brucher@gmail....
Mon Jan 7 15:33:25 CST 2008

2008/1/7, dmitrey <dmitrey.kroshko@scipy.org>:
> The only one thing I'm very interested in for now - why the most
> simplest matrix operations are not implemented to be parallel in numpy
> yet (for several-CPU computers, like my AMD Athlon X2). First of all
> it's related to matrix multiplication and devision, either point or
> matrix (i.e. like A\B, A*B, dot(A,B)).
> Another one highly convenient and rather simple thing to be implemented
> is direct Decart multiplication like it is mentioned in
> pyslice (IIRC I had some troubles with installing the one)
> http://scipy.org/Topical_Software#head-cf472934357fda4558aafdf558a977c4d59baecb
> I guess for ~95% of users it will be enough, and only 5% will require
> message-pass between subprocesses etc.
> BTW, IIRC latest MATLAB can uses 2-processors CPU already, and next
> version is promised to handle 4-processors as well.
> Regards, D.

Matlab surely relies on MKL to do this (Matlab ships with MKL or ACML now).
The latest Intel library handles multiprocessing, so if you want to use
multithreading, use MKL (and it can handle quad-cores with no sweat). So
Numpy is multithreaded.

On a side note, one of my friends published an article on a markovian
predictor for prefetching data objects over a network to speed up
computations. It was implemented on Java (search google for Jackal and
Java), but it could help in the long term if it is managable.

French PhD student
Website : http://matthieu-brucher.developpez.com/
Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92
LinkedIn : http://www.linkedin.com/in/matthieubrucher
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