Matrix multiplication optimization using GPU parallel computation

조회 수: 5 (최근 30일)
Nick 2022년 8월 18일
댓글: Nick 2023년 1월 23일
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 ;
  댓글 수: 6
Jan 2022년 8월 19일
Okay. As far as I understand, you do not want to tell me the speed difference between
M = A1 * M + A2 * f * M;
M = (A1 + A2 * f) * M
and you do not want to show the complete code for the "old" implementation. Then I cannot estimate, if storing the data in "B(t_n)" is a cause of the problem.
Nick 2022년 8월 20일
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.

댓글을 달려면 로그인하십시오.

채택된 답변

Matt J
Matt J 2022년 8월 18일
편집: Matt J 2022년 8월 18일
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.
  댓글 수: 10
Matt J
Matt J 2023년 1월 19일
편집: Matt J 2023년 1월 19일
Do you know how MATLAB manages sparse array elements?
Here is some detail on how sparse matrices are stored,
If so, will any operation on those non-zero elements cause the sorting operations you mentioned above?
If a new sparsity pattern is generated, then it will. Here's maybe another example to show how this can make sparse operations slower than full operations:
toc; %sparse matrix addition
Elapsed time is 0.085529 seconds.
A=full(A); B=full(B);
toc %full matrix addition
Elapsed time is 0.049478 seconds.

댓글을 달려면 로그인하십시오.

추가 답변 (1개)

Joss Knight
Joss Knight 2022년 8월 19일
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.
  댓글 수: 3
Joss Knight
Joss Knight 2022년 8월 20일
Ah, I forgot that you cannot see utilization information for GeForce cards, sorry. Those charts are for graphics and so not relevant for compute (except the memory one).
You'll have to use nvidia-smi.
Nick 2022년 8월 29일
Hi Joss, thanks for your info!

댓글을 달려면 로그인하십시오.


Help CenterFile Exchange에서 Parallel Computing Fundamentals에 대해 자세히 알아보기




Community Treasure Hunt

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

Start Hunting!

Translated by