[IPOL discuss] [LATE] Re: Clapack and cblas libraries interfacing

[Pascal Getreuer]

By the way (if anyone reads until there...), from my tests on a
recent machine with 2x8 cores and AVX vector instructions, MKL is a
lot faster than any other BLAS implementation, by a large
difference. And Eigen is slooow.

FWIW, Eigen recently added support for AVX, providing 2x to 3x
improvement in speed of matrix multiplication on recent CPUs:
https://bitbucket.org/eigen/eigen/pull-request/42/added-support-for-avx-and-fma-to-eigen/diff



On Fri, Oct 31, 2014 at 7:42 AM, Nicolas Limare
<nicolas.limare at cmla.ens-cachan.fr> wrote:
> Hi,
>
> For those interested, I spend a couple of more hours on this
> matter. The result, rewritten and hopefully easier to understand, is
> there:
> http://limare.perso.math.cnrs.fr/notes/2014/10/31_cblas_clapack_lapacke/
>
> For IPOL, the guidelines should be changed to:
> 1. for BLAS, use the CBLAS C interface
> 2. for LAPACK, use the LAPACK Fortran interface for gcc/gfortran
> Once the IPOL server is upgraded and liblapacke instaled, it can become:
> 1. for BLAS, use the CBLAS C interface
> 2. for LAPACK, use the LAPACKE C interface
> I will suggest a phrasing for these changes.
>
> For CBLAS on your development machines, you can use these libraries
>
> CBLAS     BLAS
> --------  --------
> MKL       MKL
> OpenBLAS  OpenBLAS
> ATLAS     ATLAS
> GSL       -
> Netlib    Netlib
>
> For LAPACKE, I think the following stacks are possible with the proper
> link option:
>
> LAPACKE   LAPACK    BLAS
> --------  --------  --------
> MKL       MKL       MKL
> Netlib    OpenBLAS  OpenBLAS
> Netlib    OpenBLAS  ATLAS
> Netlib    Netlib    ATLAS
> Netlib    Netlib    Netlib
>
> On recent multi-core with AVX vector extensions, MKL is
> faster. Otherwise, if your code depends on linear algebra, it's worth
> trying to find the best option.
>
> All the best,
>
> --
> Nicolas LIMARE
> http://nicolas.limare.net/                         pgp:0xFA423F4F
>
> --
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