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Chuck37
Posts:
84
Registered:
2/23/10
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Re: Sharing big data across parfor or spmd
Posted:
Dec 12, 2012 11:23 AM
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Yeah, I found an alternative also, using persistent variables. e.g.
M = <big mxn array>
function Mout = getM(m,n)
persistent M
if isempty(M)
<load M from file>
end
Mout = M(m,n)
end
Each worker will load the persistent variable the first time it's called on the read from M (using getM instead of direct access).
This works but doesn't address the problem of having to store a big variable (e.g.) 12 times, using up the associated memory. I was mistaken, in my case my big array is 7.5 GB, so my machine can't even store it that many times over. For now I got around it because the matrix is sparse, so I can keep it around that way instead. Access is a little slower though I think. Still seems like workers on the same machine should be able to access the same memory.
Thanks.
Edric M Ellis <eellis@mathworks.com> wrote in message <ytwy5h3d5q1.fsf@uk-eellis0l.dhcp.mathworks.com>...
> "Chuck37 " <chuck3737@yahooremovethis.com> writes:
>
> > I have a big piece of never changing data that I'd like to be used by
> > all the workers in a parallel setting. Since I'm only working with
> > local workers, I don't understand why I have to eat the huge latency
> > associated with transferring the data to workers each time the parfor
> > loop is called. Can someone explain?
> >
> > I tried to use spmd to send data once at the beginning and have it
> > stay there, but the data is kind of big (~2 GB going to 10 workers),
> > and I got "error during serialization" anyway. Is there a solution to
> > this problem with local workers where they can all access the data
> > from the same memory? Accesses are infrequent, so simultaneous access
> > shouldn't cause a big slowdown.
> >
> > Any ideas would be great.
> >
> > My setup is something like this by the way:
> >
> > M = big data;
> > for x = 1:m
> > stuff
> > parfor y = 1:n
> > a(y) = function(M,a(y));
> > end
> > stuff
> > end
> >
> > Parfor is presently worse than 'for' because of the overhead from sending M every time.
>
> You could use my worker object wrapper which is designed for exactly
> this sort of situation. See
>
> <http://www.mathworks.com/matlabcentral/fileexchange/31972-worker-object-wrapper>
>
> In your case, you could use it like this:
>
> spmd
> M = <big data>;
> end
> M = WorkerObjWrapper(M);
>
> for ...
> parfor ...
> a(y) = someFunction(M.Value, ...);
> end
> end
>
> By building M on the workers directly and building the WorkerObjWrapper
> from the resulting Composite, the data is never actually transmitted
> over the wire at any stage, so you should experience no problems with
> the current 2GB transfer limit.
>
> Cheers,
>
> Edric.
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