Thanks for sending that along - might go down that route once it's clear that MKL would do the trick and that the fixed costs of building it myself are worth it.
Are there other mac users using the pre-built binaries that see these same performance differences? Why do the mac binaries report libgfortblas and liblapack when the windows and Linux binaries report libopenblas? On Sunday, May 18, 2014, Leah Hanson <astriea...@gmail.com> wrote: > There are instructions in the Julia README and on Intel's website for > running Julia with MKL: > > https://github.com/JuliaLang/julia#intel-math-kernel-libraries > > https://software.intel.com/en-us/articles/julia-with-intel-mkl-for-improved-performance > > -- Leah > > > On Sun, May 18, 2014 at 3:59 PM, Thomas Covert > <thom.cov...@gmail.com<javascript:_e(%7B%7D,'cvml','thom.cov...@gmail.com');> > > wrote: > >> Seems like the windows and Mac versions of Julia call different >> blas/lapack routines. Might that be the cause? Is it possible for me to >> ask julia to use a different blas/lapack? >> >> >> On Sunday, May 18, 2014, J Luis >> <jmfl...@gmail.com<javascript:_e(%7B%7D,'cvml','jmfl...@gmail.com');>> >> wrote: >> >>> Funny, in a similar machine (but running Windows) I get the opposite >>> >>> Matlab 2012a (32 bits) >>> >> tic; inv(K); toc >>> Elapsed time is 3.837033 seconds. >>> >>> >>> julia> tic(); inv(K); toc() >>> elapsed time: 1.157727675 seconds >>> 1.157727675 >>> >>> julia> versioninfo() >>> Julia Version 0.3.0-prerelease+3081 >>> Commit eb4bfcc* (2014-05-16 15:12 UTC) >>> Platform Info: >>> System: Windows (x86_64-w64-mingw32) >>> CPU: Intel(R) Core(TM) i7 CPU M 620 @ 2.67GHz >>> WORD_SIZE: 64 >>> BLAS: libopenblas (USE64BITINT DYNAMIC_ARCH NO_AFFINITY) >>> LAPACK: libopenblas >>> LIBM: libopenlibm >>> >>> Domingo, 18 de Maio de 2014 19:16:48 UTC+1, Thomas Covert escreveu: >>>> >>>> I am finding that MATLAB is considerably faster than Julia for simple >>>> linear algebra work on my machine (mid-2009 macbook pro). Why might this >>>> be? Is this an OpenBLAS vs Intel MKL issue? >>>> >>>> For example, on my machine, matrix inversion of a random, symmetric >>>> matrix is more than twice as fast in MATLAB as it is in Julia: >>>> >>>> MATLAB code: >>>> K = randn(2500,2500); >>>> K = K' * K; >>>> tic; inv(K); toc >>>> Elapsed time is 2.182241 seconds. >>>> >>>> Julia code: >>>> K = convert(Array{Float32},randn(2500,2500)); >>>> K = K' * K; >>>> tic(); inv(K); toc() >>>> elapsed time: 6.249259727 seconds >>>> >>>> I'm running a fairly recent MATLAB release (2014a), and versioninfo() >>>> in my Julia install reads: >>>> Julia Version 0.3.0-prerelease+2918 >>>> Commit 104568c* (2014-05-06 22:29 UTC) >>>> Platform Info: >>>> System: Darwin (x86_64-apple-darwin12.5.0) >>>> CPU: Intel(R) Core(TM)2 Duo CPU P8700 @ 2.53GHz >>>> WORD_SIZE: 64 >>>> BLAS: libgfortblas >>>> LAPACK: liblapack >>>> LIBM: libopenlibm >>>> >>>> Any advice is much appreciated. >>>> >>>> >
