Yes. It appears so on my Mac. I just redid the timings with the same result.
Med venlig hilsen Andreas Noack 2014-09-18 15:55 GMT-04:00 Stefan Karpinski <ste...@karpinski.org>: > 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.) >>> >>> >> >
