Le dimanche 12 juillet 2015 à 11:38 -0700, John Myles White a écrit : > http://julia.readthedocs.org/en/release-0.3/manual/performance-tips/ I don't think running the code in the global scope is the problem here: most of the computing time is probably in BLAS anyway. I think MATLAB uses Intel MKL while Julia uses OpenBLAS, and maybe on that particular problem and with your particular machine the former is significantly faster.
If you really need this 2.3 factor you could try building Julia with MKL. See https://github.com/JuliaLang/julia/#intel-compilers-and-math -kernel-library-mkl Regards > On Sunday, July 12, 2015 at 8:33:56 PM UTC+2, Evgeni Bezus wrote: > > Hi all, > > > > I am a Julia novice and I am considering it as a potential > > alternative to MATLAB. > > My field is computational nanophotonics and the main numerical > > technique that I use involves multiple solution of the > > eigenvalue/eigenvector problem for dense matrices with size of > > about 1000*1000 (more or less). > > I tried to run the following nearly equivalent code in Julia and in > > MATLAB: > > > > Julia code: > > > > n = 1000 > > M = rand(n, n) > > F = eigfact(M) > > tic() > > for i = 1:10 > > F = eigfact(M) > > end > > toc() > > > > > > MATLAB code: > > > > n = 1000; > > M = rand(n, n); > > [D, V] = eig(M); > > tic; > > for i = 1:10 > > [D, V] = eig(M); > > end > > toc > > > > It turns out that MATLAB's eig() runs nearly 2.3 times faster than > > eig() or eigfact() in Julia. On the machine available to me right > > now (relatively old Core i5 laptop) the average time for MATLAB is > > of about 37 seconds, while the mean Julia time is of about 85 > > seconds. I use MATLAB R2010b and Julia 0.3.7 (i tried to run the > > code both in Juno and in a REPL session and obtained nearly > > identical results). > > > > Is there anything that I'm doing wrong? > > > > Best regards, > > Evgeni > >
