Le vendredi 28 novembre 2014 à 10:21 +0000, SLiZn Liu a écrit : > I'm doing row-wise/col-wise calculation, isn't it inevitable to create > row/col copies after iteratively extract single elements? No, I don't think so, though sometimes you'll want to extract a full row/column to pass it to a standard function instead of writing all of the computations by hand. That's where array views are very useful.
But can you give more details about the calculation you need to do? Regards > I will consider to take a shot on option 1, ArrayViews if this > single-element-extraction comes to a dead end. Thanks, Milan! > > > > On Fri Nov 28 2014 at 6:00:07 PM Milan Bouchet-Valat > <[email protected]> wrote: > Le vendredi 28 novembre 2014 à 01:45 -0800, Todd Leo a écrit : > > Hi Fellows, > > > > > > Say I have a 1000 x 1000 matrix, and I'm going to do some > calculation > > in a nested for-loop, with each pair of rows/cols in the > matrix. But I > > suffered a heavy performance penalty in row/col extraction. > Here's my > > minimum reproducible example, which I hope explains itself. > > > > > > A = rand(0.:0.01:1.,1000,1000) > > > > > > function test(x) > > for i in 1:1000, j in 1:1000 > > x[:,i] > > x[:,j] > > end > > end > > > > > > test(A) # warm up > > gc() > > @time test(A) > > ## elapsed time: 13.28547939 seconds (16208000080 bytes > allocated, > > 72.42% gc time) > > > > It takes 13 seconds, only extracting the rows/cols for the > sake of > > further calculations. I'm wondering if anything I could do > to improve > > the performance.Thanks in advance. > This is because extracting a row/column creates a copy. > Depending on > what calculation you want to do on them, you can: > - use arrays views (which will become the default when > extracting slices > in 0.4): https://github.com/JuliaLang/ArrayViews.jl > - manually write loops to go over the row and column so that > you only > extract one individual element of the matrix at a time > > > Regards
