Matrix multiplication optimization using GPU parallel computation
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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) ;
if (there is a periodic boundary condition)
M = A1 * M + A2 * f * M
M = A1 * M ;
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.
More Answers (1)
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.