MATLAB Answers

Parallel Processing with Multipul GPUs - fftn

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Nathan Zechar
Nathan Zechar on 15 May 2020
Commented: Walter Roberson on 16 May 2020
Hello, I was wondering it is possible to utilize multipul GPUs to perform several ffts in a manner where each individual fft could be given to an individual GPU to be processed in parallel - or simultaneous calculation of two separate functions.
For instance, something like this.
clear all
Nx = 256;
Ny = 256;
Nz = 512;
A = rand(Nx,Ny,Nz)+1i*rand(Nx,Ny,Nz);
A = gpuArray(A);
B = rand(Nx,Ny,Nz)+1i*rand(Nx,Ny,Nz);
B = gpuArray(B);
% GPU 1 will process this
A = fftn(A);
% GPU 2 will process this while GPU 1 is still processing the above
B = fftn(B);
% program waits for GPUs to finish then proceeds
Is this possible in MATLAB?


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Answers (1)

Matt J
Matt J on 15 May 2020
Edited: Matt J on 15 May 2020
You can use gpuDevice() to select different GPUs for different calculations. gpuArray commands run asynchronously without blocking Matlab execution on your host CPU. So, the tasks below will run essentially in parallel with GPU 1 having only a slight head start.
% GPU 1 will process this
A = gpuArray.rand(Nx,Ny,Nz)+1i*gpuArray.rand(Nx,Ny,Nz);
A = fftn(A);
% GPU 2 will process this
B = gpuArray.rand(Nx,Ny,Nz)+1i*gpuArray.rand(Nx,Ny,Nz);
B = fftn(B);


Show 2 older comments
Nathan Zechar
Nathan Zechar on 15 May 2020
Before I even attempt this, can we walk through your thought process on why you think this would work.
1.) It takes a considerable amount of time when this is called. Please, try this function out. You can do something simple like this.
for i = 1:10
2.) Again, using 'gpuDevice()' clears variables.
Matt J
Matt J on 15 May 2020
1) For me it only takes 0.7 sec per call to gpuDevice(). It doesn't make much sense to be parallelizing if that's your bottleneck. The rest of your tasks would have to be trivially fast.
2) In parfeval, you are executing gpuDevice on different parpool workers, each acting like a different Matlab session, with its own workspace. I would not expect the workspace of the two parpool workers to interact at all.
Walter Roberson
Walter Roberson on 16 May 2020
If you use gpuDevice() to select a different device than is currently selected for that process then the existing GPU will be reset, losing information.
When you use parfeval() with a pool size that is no larger than the number of GPUs that you have, then each one will run in its own process and will be granted a GPU of its own. If I recall correctly, gpu selection is automatic for this situation.

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