Matrix multiplication optimization using GPU parallel computation

43 views (last 30 days)
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
% diffusion
M = A1 * M ;
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.

Sign in to comment.

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.

Sign in to comment.

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.

Sign in to comment.


Find more on Get Started with GPU Coder in Help Center and File Exchange

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by