thanks @chris ... thanks a lot ...
On Sunday, October 16, 2016 at 8:51:56 PM UTC+3:30, Chris Rackauckas wrote: > > Take a look at the performance tips > <http://docs.julialang.org/en/release-0.4/manual/performance-tips/>. The > first time you run it, the function will compile. Then the compiled > function is cached. On my computer I did: > > a = rand(1000,1000) > y=similar(a) > @time a*a > @time a*a > @time A_mul_B!(y,a,a) > @time A_mul_B!(y,a,a) > > Which gives output: > > > 0.435561 seconds (367.13 k allocations: 20.108 MB, 1.58% gc time) > 0.019922 seconds (7 allocations: 7.630 MB) > > 0.027144 seconds (53 allocations: 2.875 KB) > 0.016211 seconds (4 allocations: 160 bytes) > > Notice how after compiling, the allocations and the timings go way down. > For a more in-depth look at how Julia is looking to get the speed (and how > to make the most of it), take a look at this blog post > <http://www.stochasticlifestyle.com/7-julia-gotchas-handle/>. Julia is a > little bit more complex than MATLAB, but the payoffs can be huge once you > take the time to understand it. Happy Julia-ing! > > > On Sunday, October 16, 2016 at 9:45:00 AM UTC-7, majid.z...@gmail.com > wrote: >> >> i have run the same matrix multiplication in both matlab and julia but >> matlab in much faster that julia, i have used both A_mul_B! and *() >> functions >> my codes are : >> in matlab : >> tic >> a = rand(1000,1000) >> a*a >> toc >> the output is : Elapsed time is 0.193979 seconds >> >> in Julia : >> a = rand(1000,1000) >> y=similar(a) >> @time a*a >> @time A_mul_B!(y,a,a) >> >> the output is: >> 1.575159 seconds >> 1.497884 seconds >> Majid >> >
