-
Notifications
You must be signed in to change notification settings - Fork 978
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
[FEA] Better grid size for H100 GPU with SXM5 #1921
Comments
When I try to hack the cutlass code for better performance on H100 GPU with SXM5, I find that when using cluster shape (4, 2, 1) or (2, 2, 1), only 120 SMs can be used. That is, when I set grid size greater than 120, the waves per SM would be greater than 1. For example,
I also check this setting: cluster shape (1, 1, 1) and grid size (132, 1, 1), waves per SM is 1. Any ideas on the number of SMs that can be used when using thread block cluster? |
cc @hwu36 @Junkai-Wu |
@ANIKET-SHIVAM Hi, can you help to explain this? :) |
@zhipeng93 yes, your observation is correct and is a known issue that for cluster sizes >= 4 optimal grid size is not launched. We should be able to elevate that limitation with cudaOccupancyMaxActiveClusters API. Will try to upstream changes soon. |
@ANIKET-SHIVAM Thanks for the reply! It seems that I can never really get to use more than 120 sms when cluster sizes >=4, even I hack the heuristic for computing the grid size. For example,
In Hopper SXM5, I should be able to use 132 sms. Can you also help to explain this? |
Is your feature request related to a problem? Please describe.
When doing gemm in Hopper, we need to decide the grid size based on problem size, cluster shape and the hopper architectures.
Currently, cutlass only considered GH100 GPU but not H100 GPU with SXM5 [1]. They have different architectures [2]:
In H100 GPU with SXM5, cutlass may fail to find the optimal grid size, leading to sub-optimal performance. For example, when doing gemm mnk=(4096, 4096, 4096) on H100 GPU with SXM5 and using cluster shape (4, 2, 1), the result grid size is (4, 28, 1). However, cublas used grid size (60, 2, 1), and cublas has a better performance by 17%.
cutlass settings:
[1] https://github.com/NVIDIA/cutlass/blob/main/include/cutlass/gemm/kernel/tile_scheduler_params.h#L249-L259
[2] https://developer.nvidia.com/blog/nvidia-hopper-architecture-in-depth/
Describe the solution you'd like
Compute grid size according to the GPU archi.
Describe alternatives you've considered
Additional context
The text was updated successfully, but these errors were encountered: