Matrix multiplication optimization using GPU parallel computation

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Dear all,
I have two questions.
(1) How do I monitor GPU core usage when I am running a simulation? Is there any visual tool to dynamically check GPU core usage?
(2) Mathematically the new and old approaches are same, but why is the new approach is 5-10 times faster?
%%% Code for new approach %%%
M = gpuArray(M) ;
for nt=1:STEPs
if (there is a periodic boundary condition)
M = A1 * M + A2 * f * M
else
% diffusion
M = A1 * M ;
end
end
  6 Comments
Nick
Nick on 20 Aug 2022
Hi Jan,
The following table summarizes the computation time comparison over different approach and GPU enabled/disabled.
New one-step app 1 doesn't have any improvement.

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Accepted Answer

Matt J
Matt J on 18 Aug 2022
Edited: Matt J on 18 Aug 2022
Because in your second formulation, there is no need to build a table of non-zero entries for the sparse matrix B. The table-building step requires sorting operations, which your second version avoids.
Also, if B has many columns, it will consume a lot of memory in proportion to the number of columns (independent of the sparsity). That is avoided as well by the second implementation.
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More Answers (1)

Joss Knight
Joss Knight on 19 Aug 2022
The Windows Task Manager lets you track GPU utilization and memory graphically, and the utility nvidia-smi lets you do it in a terminal window.
Neither the CUDA driver nor the runtime provide access to which core is running what, although you might be able to hand-code something using NVML.
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