Sorry; I have an older version of MATLAB (2010a) and saw the comparison on my machine and jumped to conclusions. I guess if I had 2012+ I would see MATLAB still being faster.
I'll pay attention to allocated bytes in the future. On Friday, 17 January 2014 13:33:15 UTC-6, Kevin Squire wrote: > > If you look at the bytes allocated, it seems that Andrea did that. > > What version of Julia are you running, Andrea? > > Kevin > > > On Fri, Jan 17, 2014 at 11:31 AM, Eric Davies <[email protected]<javascript:> > > wrote: > >> Running once before will cause compilation of the called functions for >> the given types. >> >> julia> A = randn(1000,1000); A = A+A'+eye(1000); x = randn(1000); >> >> julia> @time A\x; >> @time A\x; >> elapsed time: 0.752317463 seconds (48614156 bytes allocated) >> >> julia> @time A\x; >> @time A\x; >> elapsed time: 0.031127576 seconds (8016424 bytes allocated) >> >> Any subsequent calls to A\x with different random initializations will >> have performance like the second call shown here. >> >> >> On Friday, 17 January 2014 13:21:21 UTC-6, Andrea Vigliotti wrote: >>> >>> Hi All, >>> >>> I was comparing the performance of Julia and Matlab in solving systems >>> of linear equations and I got the following >>> >>> Matlab: >>> >> A = randn(1000); A = A+A'+eye(1000); x = randn(1000,1); >>> >> tic; A\x; toc >>> Elapsed time is 0.034763 seconds. >>> >>> Julia: >>> julia> A = randn(1000,1000); A = A+A'+eye(1000); x = randn(1000); >>> >>> julia> @time A\x; >>> elapsed time: 0.192572124 seconds (8012424 bytes allocated) >>> >>> I was wondering whether there is anything I can do to improve the >>> performances of Julia in solving this kind of problems? >>> >>> many thanks! >>> andrea >>> >> >
