I'm slightly confused – does that mean Julia is 2.4x faster in this case?
On Thu, Sep 18, 2014 at 3:53 PM, Andreas Noack <andreasnoackjen...@gmail.com > wrote: > In addition our lu calculates a partially pivoted lu and returns the L and > U matrices and the vector of permutations. To get something comparable in > MATLAB you'll have to write > > [L,,U,p] = lu(A,'vector') > > On my old Mac where Julia is compiled with OpenBLAS the timings are > > MATLAB: > >> tic();for i = 1:10 > [L,U,p] = qr(A, 'vector'); > end;toc()/10 > > ans = > > 3.4801 > > Julia: > julia> tic(); for i = 1:10 > qr(A); > end;toc()/10 > elapsed time: 14.758491472 seconds > 1.4758491472 > > Med venlig hilsen > > Andreas Noack > > 2014-09-18 15:33 GMT-04:00 Jason Riedy <ja...@lovesgoodfood.com>: > > And Elliot Saba writes: >> > The first thing you should do is run your code once to warm up the >> > JIT, and then run it again to measure the actual run time, rather >> > than compile time + run time. >> >> To be fair, he seems to be timing MATLAB in the same way, so he's >> comparing systems appropriately at that level. >> >> It's just the tuned BLAS+LAPACK & fftw v. the default ones. This >> is one reason why MATLAB bundles so much. (Another reason being >> the differences in numerical results causing support calls. Took >> a long time before MATLAB gave in to per-platform-tuned libraries.) >> >> >
