Using multiple GPUs in a parfor type of loop

I am working on a machine learning problem where I am training my classifier using a GPU and the parallel computing toolbox. I want to do 10-fold cross-validation (basically train 10 different classifiers, completely independently of each other). I have access to a machine with two Tesla GPUs. Is there a straightforward way to to combine a parfor loop with two workers, each with their own GPU attached, to do this?

When I open the matlabpool can I just specify the two different GPU devices, one for each worker and then never have to bother with it again?

[Jason Ross]

What release are you working with?

Answer 1

[James Lebak]

In R2012b and later, in a matlabpool on a single machine with two GPUs, each worker will use a different GPU by default. You can see this by executing the following code:

matlabpool local 2

parfor ix = 1:10
  gd=gpuDevice;
  d(ix)=gd.Index;
end

The value of d will show you the index of the GPU on which each iteration was executed.

In earlier releases the workers all use device 1 by default, and you would have to set the device manually using spmd outside the parfor loop or (less optimally) inside each loop iteration.