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To: cygwin@cygwin.com
From: Tony Kelman <tony@kelman.net>
Subject: Re: Parallelization
Date: Mon, 18 Jul 2016 05:38:36 +0000 (UTC)
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Eliot Moss <moss <at> cs.umass.edu> writes:

> True ... it also made me think of Python, which is designed to use
> parallelized numpy (etc.) libraries, optimized for your platform.
> Can use all the hardware threads on your machine, as well as make
> good use of vector extensions such as AVX.  A 64-bit (x86-64)
> version will give best use of vector processing, in my
> experience.
> 
> Regards -- Eliot Moss

numpy is only as parallel as the underlying BLAS/LAPACK library that
it uses is. So if you're using Cygwin's openblas then you're in
decent shape. But I don't think cv_adams spends much time (if any?)
in BLAS/LAPACK dense linear algebra functions, I think it's mostly
dominated by function evaluation time.

-Tony



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