On Thu, Feb 6, 2014 at 1:11 PM, Thomas Unterthiner < thomas_unterthi...@web.de> wrote:
> On 2014-02-06 11:10, Sturla Molden wrote: > > BTW: The performance of OpenBLAS is far behind Eigen, MKL and ACML, but > > better than ATLAS and Accelerate. > Hi there! > > Sorry for going a bit off-topic, but: do you have any links to the > benchmarks? I googled around, but I haven't found anything. FWIW, on my > own machines OpenBLAS is on par with MKL (on an i5 laptop and an older > Xeon server) and actually slightly faster than ACML (on an FX8150) for > my use cases (I mainly tested DGEMM/SGEMM, and a few LAPACK calls). So > your claim is very surprising for me. > > Also, I'd be highly surprised if OpenBLAS would be slower than Eigen, > given than the developers themselves say that Eigen is "nearly as fast > as GotoBLAS"[1], and that OpenBLAS was originally forked from GotoBLAS. > > I'm also a little sceptical about the benchmarks, e.g. according to the FAQ eigen does not seem to support AVX which is relatively important for blas level 3 performance. The lazy evaluation is probably eigens main selling point, which is something we cannot make use of in numpy currently. But nevertheless eigen could be an interesting alternative for our binary releases on windows. Having the stuff as headers makes it probably easier to build than ATLAS we are currently using.
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