Persist data on GPU between parfor Calls
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The actual overhead from invoking a parfor is pretty low (~17 ms), so it is fast enough to start up a parfor, do one operation per worker, and then repeat:
for i=1:N
parfor j=1:10
X{j} = gpuArray(X{j});
Y{j} = MyFunction(X{j}); % <-- Takes about 1 second per worker
end
end
However, it seems that Matlab re-copies all of the data in X{j} over to the GPU each iteration of the for loop. I would like X{j} to persist on the GPU between for loop iterations.
One hacky solution is to embed another for loop inside the parfor to reduce the amount of re-copying. This is not ideal for my application (I'm doing gradient descent function optimization).
Hopefully there is a simple way to force each X{j} to remain on its respective GPU.
采纳的回答
Edric Ellis
2015-4-7
Normally I would recommend the Worker Object Wrapper to do this - however, that's more useful for parfor "broadcast" variables - in this case, it looks like you're trying to persist "sliced" variables, which is tricky since each worker can end up with a different sliced variable each time. In this case, can't you simply switch the for loop to be inside the parfor loop? (Perhaps your actual code is a little more complicated and precludes that).
The other option is to use spmd and Composite objects (which behave somewhat like "WorkerObjWrapper" objects, but are specific to spmd). You might do something like this:
spmd
myX = gpuArray(labindex);
end
% myX is now a "Composite" that persists on the workers
for i = 1:N
spmd
myY = MyFunction(myX);
end
end
更多回答（1 个）
Jonathan
2015-4-7
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Edric Ellis
2015-4-8
Yes, the spmd block has overhead which you can minimise by moving loops inside. Note that while labSend and labReceive work correctly with gpuArray data, they are not optimised for that (in the sense of some of the more advanced technologies that NVIDIA have for sending GPU data directly via MPI) - effectively the data has to be gathered back to the CPU before it can be sent.
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