From michael.m.spiegel at gmail.com  Tue Nov  2 00:43:19 2010
From: michael.m.spiegel at gmail.com (Michael Spiegel)
Date: Mon, 1 Nov 2010 19:43:19 -0400
Subject: [Rd] BLAS benchmarks on R 2.12.0
In-Reply-To: <20101101220704.GA96310@piskorski.com>
References: <AANLkTi=Er4FQYh-JO4eSA3PFefWdT6fuHr2S1ocwEe7t@mail.gmail.com>
	<20101101220704.GA96310@piskorski.com>
Message-ID: <AANLkTikpQtBKhfL14V4BmOK2WVwnqDK8F=A9oFHi1Nmj@mail.gmail.com>

Hi Andrew,

In the majority of use cases of our package, we end up doing lots and
lots of matrix operations on small matrices, as opposed to matrix
operations on large matrices.  The optimized BLAS libraries are
usually optimized for large matrices.  The reference implementation is
faster than either veclib, Atlas, or the Goto BLAS implementations,
I've tested all of them on our performance test suite.

--Michael

On Mon, Nov 1, 2010 at 6:07 PM, Andrew Piskorski <atp at piskorski.com> wrote:
> On Sun, Oct 31, 2010 at 12:41:24PM -0400, Michael Spiegel wrote:
>
>> 1) Compile the reference BLAS implementation with unsafe optimizations
>> and include it as a part of the OpenMx library.
>
> If BLAS speed is important to you, why are you even trying to use the
> slow reference BLAS library at all, rather than one of the faster
> optimized BLAS libraries (Atlas, Goto, AMD, Intel, etc.)?
>
> --
> Andrew Piskorski <atp at piskorski.com>
> http://www.piskorski.com/
>



[Rd] BLAS benchmarks on R 2.12.0
Michael Spiegel