Thanks to you both! However, there is still another odd issue.
These two functions should be the same, but take very different amounts of
time/memory. Both 'T' and 'n_classes' are both of type Int64.
@time (
for t = 2:T
for n = 1:n_classes
for j = 1:n_classes
end
end
end
)
@time (
for t = 2:5000
for n = 1:10
for j = 1:10
end
end
end
)
elapsed time: 0.063186286 seconds (18190040 bytes allocated)
elapsed time: 0.002261641 seconds (71824 bytes allocated)
Any insight on this?
On Wednesday, November 26, 2014 12:37:12 PM UTC-5, Tim Holy wrote:
>
> Nice job using track-allocation to figure out where the problem is.
>
> If you really don't want allocation, then you should investigate Devec.jl
> or
> InPlaceOps.jl, or write out these steps using loops to access each element
> of
> those matrices.
>
> --Tim
>
> On Wednesday, November 26, 2014 07:55:59 AM Colin Lea wrote:
> > I'm implementing an inference algorithm and am running into memory
> > allocation issues that are slowing it down. I created a minimal example
> > that resembles my algorithm and see that the problem persists.
> >
> > The issue is that Julia is allocating a lot of extra memory when adding
> > matrices together. This happens regardless of whether or not I
> preallocate
> > the output matrix.
> >
> > Minimal example:
> > https://gist.github.com/colincsl/ab44884c5542539f813d
> >
> > Memory output of minimal example (using julia --track-allocation=user):
> > https://gist.github.com/colincsl/c9c9dd86fca277705873
> >
> > Am I misunderstanding something? Should I be performing the operation
> > differently?
> >
> > One thing I've played with is the matrix C. The indices are a sliding
> > window (e.g. use C[t-10:t] for all t). When I remove C from the equation
> > the performance increases by a factor of 2.5. However, it still uses
> more
> > memory than expected. Could this be the primary issue?
>
>
