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> 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.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
>
