Hello I was comparing the performance of two snippets of MATLAB (running on Octave) and Julia code on Windows.
MATLAB code: n=1000; a=rand(n,n); tic for i=1:10000 b(1:n,1:n)=a(1:n,1:n); %b=a; end toc JULIA Code: n=1000; b = Array(Float64,n,n); a=rand(n,n); tic(); for i=1:10000 b[1:n,1:n]=a[1:n,1:n]; #b=a; end toc(); I understand that both the statements in the loop are b=a, but I decided to index the arrays and see what happened. The MATLAB code takes 0.22 seconds (on Octave ) whereas the JULIA code takes between 80 to 100 seconds (varies every time). I think Octave is performing some simple optimization here, and I think Julia might benefit from the same. Version info: Octave 3.6.4 and Julia 0.4.0-dev (but I saw the same happen in 0.3.5 too)
