You are not using a fast BLAS, but the slow reference BLAS which unfortunately is the default on Linux. An option is to build Julia from source. Usually it is just to download the source and write make. Then you'll have Julia compiled with OpenBLAS which is much faster and comparable in speed to Intel MKL which MATLAB uses.
Med venlig hilsen Andreas Noack 2014-09-18 14:45 GMT-04:00 Stephan Buchert <stephanb...@gmail.com>: > I have installed julia 0.3 from > http://copr.fedoraproject.org/coprs/nalimilan/julia/ > on my i7 Haswell 2.4 GHz laptop with updated Fedora 20. > > Then I translated the first two parts of the Matlab bench script to julia: > > # Transscript of the Matlab bench, > # only LU and FFT > # Transscript of the Matlab bench, > # only LU and FFT > print("LU decomposition, "); > tic(); > A = rand(1600, 1600); > lu(A); > toc(); > print("FFT , "); > n = 2^21; > tic(); > x = rand(1,n); > fft(x); > fft(x); > toc(); > > The comparison is relatively disastrous for julia: > julia> include("code/julia/bench.jl") > LU decomposition, elapsed time: 0.936670955 seconds > FFT , elapsed time: 0.208915093 seconds > (best out of 10 tries) > > Matlab r2014a > LU decomposition: 0.0477 seconds > FFT: 0.0682 seconds > > LU is 24 times slower on julia, FFT is 3 times slower. According to > system-monitor Matlab bench causes 3 cores to be busy, julia only 1. This > could explain the FFT result, but not the LU. > >
