That is great! However, by devectorizing, I meant writing the loop statement
itself as two more loops, so that you end up with 3 nested loops effectively.
You basically do not want all those w[:,:,ti] calls that create matrices every
time.
You could also potentially hoist the deltas.d out of the loop. Try something
like:
function errprop!(w::Array{Float32,3}, d::Array{Float32,3}, deltas)
deltas.d[:] = 0.
dd = deltas.d
for ti=1:size(w,3), ti2 = 1:size(d,3)
for i=1:size(w,1)
for j=size(w,2)
dd[i,j,ti+ti2-1] += w[i,j,ti]'*d[i,j,ti2]
end
end
end
deltas.d
end
-viral
> On 14-Sep-2014, at 12:47 pm, Michael Oliver <michael.d.oli...@gmail.com>
> wrote:
>
> Thanks Viral for the quick reply, that's good to know. I was able to squeeze
> a little more performance out with axpy (see below). I tried devectorizing
> the inner loop, but it was much slower, I believe because it was no longer
> taking full advantage of MKL for the matrix multiply. So far I've got the
> code running at 1.4x what I had in Matlab and according to @time I still have
> 44.41% gc time. So 0.4 can't come soon enough! Great work guys, I'm really
> enjoying learning Julia.
>
> function errprop!(w::Array{Float32,3}, d::Array{Float32,3}, deltas)
> deltas.d[:] = 0.
> rg =size(w,2)*size(d,2);
> for ti=1:size(w,3), ti2 = 1:size(d,3)
>
> Base.LinAlg.BLAS.axpy!(1,w[:,:,ti]'*d[:,:,ti2],range(1,rg),deltas.d[:,:,ti+ti2-1],range(1,rg))
> end
> deltas.d
> end
>
> On Saturday, September 13, 2014 10:10:25 PM UTC-7, Viral Shah wrote:
> The garbage is generated from the indexing operations. In 0.4, we should have
> array views that should solve this problem. For now, you can either manually
> devectorize the inner loop, or use the @devectorize macros in the Devectorize
> package, if they work out in this case.
>
> -viral
>
> On Sunday, September 14, 2014 10:34:45 AM UTC+5:30, Michael Oliver wrote:
> Hi all,
> I've implemented a time delay neural network module and have been trying to
> optimize it now. This function is for propagating the error backwards through
> the network.
> The deltas.d is just a container for holding the errors so I can do things in
> place and don't have to keep initializing arrays. w and d are collections of
> weights and errors respectively for different time lags.
> This function gets called many many times and according to profiling, there
> is a lot of garbage collection being induced by the fourth line, specifically
> within multidimensional.jl getindex and setindex! and array.jl +
>
> function errprop!(w::Array{Float32,3}, d::Array{Float32,3}, deltas)
> deltas.d[:] = 0.
> for ti=1:size(w,3), ti2 = 1:size(d,3)
> deltas.d[:,:,ti+ti2-1] += w[:,:,ti]'*d[:,:,ti2];
> end
> deltas.d
> end
>
> Any advice would be much appreciated!
> Best,
> Michael
