This *can* be true, but I think a lot of us forget just how much time it takes to learn to write compact vectorized code, and how difficult it can be to vectorize absolutely everything. For instance, it's great to be able to write
x' * A * x in Matlab, but a whole ton of code using repmat or bsxfun often becomes *easier* to read (IMO) when you can actually look at a single line in a loop and understand a) how many indices everything has and b) what the [i, j, k, ...] entry of the resulting array looks like. I have been forced to write a lot of very difficult-to-parse code in Matlab simply in order to avoid what would have been straightforward loops. On Monday, October 19, 2015 at 3:05:11 PM UTC-4, Phil Tomson wrote: > > 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 >>> >>
