I was using axpy to replace the += only and doing the matrix muliply in the
argument to axpy. But you're right gemm! is actually what I should be using
(I'm just starting to learn the BLAS library names). Using gemm! the code
is now 1.68x faster than my Matlab code (I mean a whole epoch of backprop
training)! And down to 40% gc time. My goal of 2x speed up is in sight!
I'll look into subArrays next.
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)
Base.LinAlg.BLAS.gemm!('T', 'N', one(Float32), w[:,:,ti],
d[:,:,ti2], one(Float32), deltas.d[:,:,ti+ti2-1])
end
deltas.d
end
On Sunday, September 14, 2014 2:18:07 AM UTC-7, Viral Shah wrote:
>
> Oh never mind - I see that you have a matrix multiply there that benefits
> from calling BLAS. If it is a matrix multiply, how come you can get away
> with axpy? Shouldn’t you need a gemm?
>
> Another way to avoid creating temporary arrays with indexing is to use
> subArrays, which the linear algebra routines can work with.
>
> -viral
>
>
>
> > On 14-Sep-2014, at 2:43 pm, Viral Shah <vi...@mayin.org <javascript:>>
> wrote:
> >
> > 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....@gmail.com
> <javascript:>> 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
> >
>
>
