MATLAB: Parfor on GPU

gpuparfor

I want to run two functions in parallel on a GPU. For this i want to use pafor(eg: for ii=1 fun1 and ii=2 fun2).

Can variables on GPU be used for parfor operations on GPU?

Best Answer

Yes, you can do this. Whether you get much benefit depends on whether you have multiple GPUs in your system (under some circumstances, a single GPU might actually suffice if you have enough CPU work to keep things busy).

You might wish to do something like

spmd
  gpuDevice( 1 + mod( labindex - 1, gpuDeviceCount ) )
end

before you go any further (if you have multiple supported GPUs)

After that, gpuArrays can be passed into and out from PARFOR loops with no further modification. The following example shows this - but note that this is a proof of concept - it performs very badly because you're operating on scalar elements of the gpuArray.

g = gpuArray(1:10);
parfor ii=1:numel(g)
  x(ii) = 1/g(ii);
end

Related Solutions

MATLAB: Fmincon GPU Cuda Acceleration using Parallel Computing Toolbox

The way you would exploit the GPU in fmincon would be to accelerate the computation of your objective function and its derivatives. To do that, you just use gpuArray objects for heavy and highly parallel computations inside the objective function, just like you would in any other code.

On the other hand, if the computational cost of each objective function evaluation is low, parallel computing is probably not going to be of any advantage.

MATLAB: Is it possible to use MATLAB GPU functionality on a compute cluster with NVIDIA Tesla cards on each node in MATLAB Distributed Compute Server 5.0 (R2010b)

The functionality to use GPU Computing with MATLAB on a cluster is supported in MATLAB 7.11 (R2010b). The only limitation is, that each MATLAB worker can at most use one GPU at a time.

There are three use cases possible:

1. Each nodes has only a single GPU and is of a single core processor. In this configuration there will be no issues.

2. Each node has a multi-core processor and a single GPU per node. In this configuration two uses are possible:

- run only one MATLAB worker per each node at a time.

- allow multiple workers to run on the same node at the same time and share the GPU. However, then GPU Computation will be serialized across workers.

3. Each nodes has a multi-core processor and has multiple GPUs per node. In this situation you will need to manually assign GPUs to workers on each node to ensure that each worker gets an independent GPU card. An example is shown below:

matlabpool open 8 
spmd 
   gpuDevice( labindex ); 
   % customer GPU code goes here 
end 
matlabpool close

Best Answer

Related Solutions

MATLAB: Fmincon GPU Cuda Acceleration using Parallel Computing Toolbox

MATLAB: Is it possible to use MATLAB GPU functionality on a compute cluster with NVIDIA Tesla cards on each node in MATLAB Distributed Compute Server 5.0 (R2010b)

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