Passing big matrix to workers

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or ohev shalom
or ohev shalom 2018 年 7 月 5 日
編集済み: Matt J 2018 年 7 月 5 日
I'm trying to pass very large matrix (65000x460x150) to workers with Worker Object Wrapper, with no luck.
The code:
Imat_worker = WorkerObjWrapper(Imat);
parfor kk = 1:size(num_iterations,2)
Imat_par = Imat_worker(:,:,1:num_iterations(kk));
some code...
end
I get the following message while running the code:
Error using WorkerObjWrapper/workerInit (line 156)
The parallel pool that SPMD was using has been shut down.
Error in WorkerObjWrapper (line 97)
WorkerObjWrapper.workerInit( tmpId, ctor, args, dtor );
A write error occurred while sending to worker 2.
Any ideas what am I doing wrong?
It is worth noting that I'm running the code on a server with enough RAM for the matrix.
Thanks in advance!
  8 件のコメント
Matt J
Matt J 2018 年 7 月 5 日
編集済み: Matt J 2018 年 7 月 5 日
Imat is double.
Yes, but are most of those double values zeros?
or ohev shalom
or ohev shalom 2018 年 7 月 5 日
Unfortunately not. There isn't even 1 single zero value inside.

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Matt J
Matt J 2018 年 7 月 5 日
編集済み: Matt J 2018 年 7 月 5 日
In that case, since I need only a slice of the matrix for each worker, shouldn't I clone only the relevant data somehow before the parfor ?
Yes you should, and that's something parfor will do for you automatically as long as the operations you do on Imat inside the loop satisfy certain restrictions.
  2 件のコメント
or ohev shalom
or ohev shalom 2018 年 7 月 5 日
Ok, Thank you very much.
Now it is much clearer, and I'm pretty sure that I can't run the code the way I wanted (not enough RAM...).
Matt J
Matt J 2018 年 7 月 5 日
編集済み: Matt J 2018 年 7 月 5 日
Yes, making Imat a sliced variable is much more sensible if it is possible to do so.
If you can afford the loss in precision, you could also consider storing the original Imat as single floats. You could still do the calculations in double precision on the workers by converting the current slice being processed to double in the course of the loop. The total amount of double data being held anywhere simultaneously will then be much smaller.

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