Declare gpuArray while executing on gpu

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cedric W
cedric W 2019-1-4
评论： Marios 2019-11-15
I'm trying to modify my code working well for a CPU setup to a GPU setup. It appears to be more complicated than what I had expected.
I'm using arrayfun to send the computation to GPU for my monte carlo simulations:
MC_Paths = arrayfun(@Heston_One_Simulation, Param1, Param2, and so on)
with the beginning of the function Heston_One_Simulation defined as
function [S] = Heston_One_Simulation(Param1,Params2, so on...)
HistoCorrelatedPaths1=zeros(N,NbAssets,BatchSize,'gpuArray');
...
end
And I get the following error message:
Function passed as first input argument contains unsupported or unknown function 'zeros'. For more information see Tips and Restrictions.
Is it impossible to declare new variables while running on a GPU ?
I'm sure I'll have more problems once this one is solved but let's start with that one.

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回答（1 个）

Joss Knight
Joss Knight 2019-1-5
GPU arrayfun functions can only do scalar operations. You can declare new scalar variables but you can't create new arrays.
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Joss Knight
Joss Knight 2019-1-7
Most problems are solved using a combination of arrayfun, pagefun, and standard vectorisation. You need to look for opportunity to batch up all your work as much as possible. Arrayfun can be used wherever you have a sequence of element-wise maths, but isn't usually necessary because MATLAB is clever enough to group all those operations into a single kernel for you where possible.
Essentially you are looking to remove all the loops in your code and replace them with masks, vector algebra and arrayfun/pagefun calls.
This is a good blog post to get you on the right track:
https://devblogs.nvidia.com/high-performance-matlab-gpu-acceleration/
Marios
Marios 2019-11-15
Is this way the example paralleldemo_gpu_arrayfun does not show any improvement between the standard "tgpuObject" and GPU arrayfun() "tgpuArrayfun"? I have tried different GPUs (1080, 1650) on 2019b and both cases return the same speed. I assume it's because horner() has been optimized in 2019b to "bunch" all the kernels in one anyway eliminating the need for GPU arrayfun()?

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