Unfortunately, this is not supported by Parallel Computing Toolbox in R2012b. The gpuArray version of arrayfun currently does not support binding in the constant data to an anonymous function handle. Arrayfun arguments must be passed directly, and must all either be scalar or the same size.

If you could bind in the constant arguments, you would next discover that you cannot currently index into them (or perform any non-scalar operations on them).

Perhaps you might be able to build up your algorithm using supported routines such as CONV2 or FILTER2.

Inside arrayfun, one can only perform computations with scalars, so vector and matrix computations are not supported. In particular, one cannot create random matrices. Having said that, you can easily write loops, create multiple random numbers, etc., so the equivalent functionality of the example code would be achieved via: function highvalue = getHighRandValue(outerbound) highvalue = -inf; for i = 1:outerbound highvalue = max(highvalue, randn()); end end Best, Narfi

|arrayfun| IS just a loop, unless the inputs are |gpuArrays|. Your input isn't a |gpuArray|, because you've passed all your |gpuArrays| as up-values. What you're doing is just simple algebra with scalar expansion: corr1 = reshape(corr1, [], [], length(corr1)); corr2 = reshape(corr2, [], [], length(corr2)); imfinal = sum( arrayfun(@calculateCorrImage, im, corr1, corr2), 3 );

A few points here. Firstly, (and most importantly), to time code on the GPU, you need to use either gputimeit, or you need to inject a call to wait(gpuDevice) before calling `toc`. That's because work is launched asynchronously on the GPU, and you only get accurate timings by waiting for it to finish. With those minor modifications, on my GPU, I see 0.09 seconds for the `gpuArray` method, and 0.18 seconds for the `arrayfun` version. Running a loop of GPU operations is generally inefficient, so the main gain you can get here is by pushing the loop inside the `arrayfun` function body so that that loop runs directly on the GPU. Like this: %%% Function to operate on matrices %%% function x = test_function(x,Nt) for ii = 1:Nt x = exp(-1i*(x + abs(x).^2)); end end You'll need to invoke it like `A = arrayfun(@test_function, A, Nt)`. On my GPU, this brings the `arrayfun` time down to 0.05 seconds, so about twice as fast as the plain `gpuArray` version.