Ok, I tried it on my main program but it's slower. Also in my main program I use vectors not 2D matrix so maybe that why it's slower.
On Saturday, July 2, 2016 at 3:23:49 AM UTC+2, Chris Rackauckas wrote: > > BLAS will be faster for (non-trivial sized) matrix multiplications, but it > doesn't apply to component-wise operations (.*, ./). > > For component-wise operations, devectorization here shouldn't give much of > a speedup. The main speedup will actually come from things like loop fusing > which gets rid of intermediates that are made when doing something like > A.*B.*exp(C). > > For this equation, you can devectorize it using the Devectorize.jl macro: > > @devec Mr = m.*m > > At least I think that should work. I should basically generate the code > you wrote to get the efficiency without the ugly C/C++ like extra code. > > On Saturday, July 2, 2016 at 1:11:49 AM UTC+2, baillot maxime wrote: >> >> @Tim Holy : Thank you for the web page. I didn't know it. Now I >> understand a lot of thing :) >> >> @Kristoffer and Patrick: I just read about that in the link that Tim gave >> me. I did change the code and the time just past from 0.348052 seconds to >> 0.037768 seconds. >> >> Thanks to you all. Now I understand a lot of things and why it was slower >> than matlab. >> >> So now I understand why a lot of people was speaking about Devectorizing >> matrix calculus. But I think it's sad, because if I want to do this I will >> use C or C++ . Not a matrixial language like Julia or Matlab. >> >> Anyway! So if I'm not mistaking... It's better for me to create a "mul()" >> function than use the ".*" ? >> >
