But That ok :) Thank you for the idea!
On Saturday, July 2, 2016 at 11:22:21 AM UTC+2, baillot maxime wrote: > > 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 ".*" ? >>> >>
