Several comments here about the need to de-vectorize code and use for-loops instead. However, vectorized code is a lot more compact and generally easier to read than lots of for-loops. I seem to recall that there was discussion in the past about speeding up vectorized code in Julia so that it could be on par with the vectorized code performance - is this still something being worked on for future versions?
Otherwise, if we keep telling people that they need to convert their code to use for-loops, I think Julia isn't going to seem very compelling for people looking for alternatives to Matlab, R, etc. On Sunday, October 18, 2015 at 6:41:54 AM UTC-7, Daniel Carrera wrote: > > Hello, > > Other people have already given advice on how to speed up the code. I just > want to comment that Julia really is faster than Matlab, but the way that > you make code faster in Julia is almost the opposite of how you do it in > Matlab. Specifically, in Matlab the advice is that if you want the code to > be fast, you need to eliminate every loop you can and write vectorized code > instead. This is because Matlab loops are slow. But Julia loops are fast, > and vectorized code creates a lot of overhead in the form of temporary > variables, garbage collection, and extra loops. So in Julia you optimize > code by putting everything into loops. The upshot is that if you take a > Matlab-optimized program and just do a direct line-by-line conversion to > Julia, the Julia version can easily be slower. But by the same token, if > you took a Julia-optimized program and converted it line-by-line to Matlab, > the Matlab version would be ridiculously slow. > > Oh, and in Julia you also care about types. If the compiler can infer > correctly the types of your variables it will write more optimal code. > > Cheers, > Daniel. > > > On Sunday, 18 October 2015 13:17:50 UTC+2, Vishnu Raj wrote: >> >> Although Julia homepage shows using Julia over Matlab gains more in >> performance, my experience is quite opposite. >> I was trying to simulate channel evolution using Jakes Model for wireless >> communication system. >> >> Matlab code is: >> function [ h, tf ] = Jakes_Flat( fd, Ts, Ns, t0, E0, phi_N ) >> %JAKES_FLAT >> % Inputs: >> % fd, Ts, Ns : Doppler frequency, sampling time, number of samples >> % t0, E0 : initial time, channel power >> % phi_N : initial phase of the maximum Doppler frequeny >> % sinusoid >> % >> % Outputs: >> % h, tf : complex fading vector, current time >> >> if nargin < 6, phi_N = 0; end >> if nargin < 5, E0 = 1; end >> if nargin < 4, t0 = 0; end >> >> N0 = 8; % As suggested by Jakes >> N = 4*N0 + 2; % an accurate approximation >> wd = 2*pi*fd; % Maximum Doppler frequency[rad] >> t = t0 + [0:Ns-1]*Ts; % Time vector >> tf = t(end) + Ts; % Final time >> coswt = [ sqrt(2)*cos(wd*t); 2*cos(wd*cos(2*pi/N*[1:N0]')*t) ]; >> h = E0/sqrt(2*N0+1)*exp(j*[phi_N pi/(N0+1)*[1:N0]])*coswt; >> end >> Enter code here... >> >> My call results in : >> >> tic; Jakes_Flat( 926, 1E-6, 50000, 0, 1, 0 ); toc >> Elapsed time is 0.008357 seconds. >> >> >> My corresponding Julia code is >> function Jakes_Flat( fd, Ts, Ns, t0 = 0, E0 = 1, phi_N = 0 ) >> # Inputs: >> # >> # Outputs: >> N0 = 8; # As suggested by Jakes >> N = 4*N0+2; # An accurate approximation >> wd = 2*pi*fd; # Maximum Doppler frequency >> t = t0 + [0:Ns-1]*Ts; >> tf = t[end] + Ts; >> coswt = [ sqrt(2)*cos(wd*t'); 2*cos(wd*cos(2*pi/N*[1:N0])*t') ] >> h = E0/sqrt(2*N0+1)*exp(im*[ phi_N pi/(N0+1)*[1:N0]']) * coswt >> return h, tf; >> end >> # Saved this as "jakes_model.jl" >> >> >> My first call results in >> julia> include( "jakes_model.jl" ) >> Jakes_Flat (generic function with 4 methods) >> >> julia> @time Jakes_Flat( 926, 1e-6, 50000, 0, 1, 0 ) >> elapsed time: 0.65922234 seconds (61018916 bytes allocated) >> >> julia> @time Jakes_Flat( 926, 1e-6, 50000, 0, 1, 0 ) >> elapsed time: 0.042468906 seconds (17204712 bytes allocated, 63.06% gc >> time) >> >> For first execution, Julia is taking huge amount of time. On second call, >> even though Julia take considerably less(0.042468906 sec) than first( >> 0.65922234 sec), it's still much higher to Matlab(0.008357 sec). >> I'm using Matlab R2014b and Julia v0.3.10 on Mac OSX10.10. >> >> - vish >> >
