Dear Paul, For k=100 and your purpose, parallelization may not be the utmost performance bottleneck here. I advise you to use the Distances.jl <https://github.com/JuliaStats/Distances.jl> package. Since Julia stores contiguous memory in column-major order <https://julia.readthedocs.org/en/latest/manual/performance-tips/#access-arrays-in-memory-order-along-columns>, you will first need to transpose the matrix D — or, better, to define it foremost as a n*k matrix instead of k*n. Once you've ensured that, calling mapa = Distances.pairwise(Euclidean(), D)
should give you at least a 100x speedup over the for loop you've written, so parallelization should no longer be necessary. Vincent. Le dimanche 11 octobre 2015 16:34:27 UTC+2, paul analyst a écrit : > > Like here , what wrong ? > k=100 > mapa=zeros(k,k) > > julia> @parallel for i=1:k,j=1:k > mapa[i,j]=sqrt(sum([D[i,:]-D[j,:]].^2)) > end > ERROR: syntax: invalid assignment location > > Paul >
