Hello, I am a new Julia user. I am trying to write a function for computing "self" dot product of all columns in a matrix, i.e. calculating a square of each element of a matrix and computing a column-wise sum. I am interested in a proper way of doing it because I often need to process large matrices.
I first put a focus on calculating the squares. For testing purposes I use a matrix of random floats of size 7000x7000. All timings here are deducted after several repetitive runs. I used to do it in Octave (v3.8.1) a follows: tic; X = rand(7000); toc; Elapsed time is 0.579093 seconds. tic; XX = X.^2; toc; Elapsed time is 0.114737 seconds. I tried to to the same in Julia (v0.2.1): @time X = rand(7000,7000); elapsed time: 0.114418731 seconds (392000128 bytes allocated) @time XX = X.^2; elapsed time: 0.369641268 seconds (392000224 bytes allocated) I was surprised to see that Julia is about 3 times slower when calculating a square than my original routine in Octave. I then read "Performance tips" and found out that one should use * instead of of raising to small integer powers, for example x*x*x instead of x^3. I therefore tested the following. @time XX = X.*X; elapsed time: 0.146059577 seconds (392000968 bytes allocated) This approach indeed resulted in a lot shorter computing time. It is still however a little slower than my code in Octave. Can someone advise on any performance tips ? I then finally do a sum over all columns of XX to get the "self" dot product but first I'd like to fix the squaring part. Thanks a lot. Best Regards, Jan p.s. In Julia manual I found a while ago an example of using @vectorize macro with a squaring function but can not find it any more. Perhaps the name of macro was different ...
