|Re: [eigen] benchmarks for large matrices?|
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- To: eigen@xxxxxxxxxxxxxxxxxxx
- Subject: Re: [eigen] benchmarks for large matrices?
- From: Benoit Jacob <jacob.benoit.1@xxxxxxxxx>
- Date: Tue, 17 Feb 2009 21:40:12 -0500
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2009/2/17 David Roundy <daveroundy@xxxxxxxxx>:
> Hello eigen folks,
> I recently took a look at the very impressive benchmarks shown at:
> and had a couple of questions. First, would it be a problem to extend these
> benchmarks to larger matrices?
A user benchmarked 4000x4000 matrix products against MKL:
His result was the same that we got in our benchmark for 1000x1000:
Namely, Eigen is at about 2/3 of the speed of MKL.
> multiplication of two (21,000 by 100) matrices (I just took these numbers
> from a recent calculation), multiplied such that the result is a small,
> square matrix.
Ah, we dont have benchmarks for that.
> Relating to that, I was curious as to the options given to ATLAS. If it's
> running without sse2 support compiled in, then it should be compared with
> eigen2_novec, and is quite competitive and sometimes wins. However, if it
> was compiled with sse2 support, then eigen2 appears much more impressive.
I cant speak for Gael who did all the benchmarking (as well as all the
optimization of matrix products), but, as written on this benchmark
page, his architecture is x86-64, and on this architecture SSE2 is
enabled by default (because all x86-64 cpus support SSE2). So I would
be very surprised if his ATLAS didn't use SSE2.
Thanks for your interest,