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Reply-To: moss AT cs DOT umass DOT edu
Subject: Re: Parallelization
References: <db3a62ee-a3d2-82df-0eb1-140f442d9f79 AT netcologne DOT de> <loom DOT 20160717T135427-906 AT post DOT gmane DOT org> <b9e23399-e039-2de4-837f-8d46f366c892 AT cs DOT umass DOT edu> <loom DOT 20160718T073628-733 AT post DOT gmane DOT org>
To: cygwin AT cygwin DOT com
From: Eliot Moss <moss AT cs DOT umass DOT edu>
Message-ID: <cfcf9acc-f761-67fe-7983-babf60e9d275@cs.umass.edu>
Date: Mon, 18 Jul 2016 13:29:07 -0400
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On 7/18/2016 1:38 AM, Tony Kelman wrote:
> 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.

Ok -- I'm not sure what to suggest then ...

Regards -- EM

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