Nice :-) On Thu, Sep 18, 2014 at 4:20 PM, Andreas Noack <andreasnoackjen...@gmail.com > wrote:
> 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.) >>>> >>>> >>> >> >
