The explanation of the profile result of sglang.profiler

[ai-easy-cpu]

Thanks for @sufeng-buaa , and their answer is summarized in bold below.

Is there a quick help about the profiling result of following (by sglang.profiler and opened in Perfetto, overlap is disabled for more clear logic)

• Is this chart of trace result expected? - Yes
• what are the 3 lines at the top? The green bar may be the heatmap
• what are these 3 tags? (the first seems to be the CPU, second GPU, but I am not sure because their time events are not consistent...) - the 3 tracks are processes, first CPU scheduler, second GPU, the third is pytorch profiler (not usually used), during data copy of cudaMemcpyAsync, the GPU kernels are still running because kernels are submitted in async manner

(Did not find anything helpful connecting the profiler here and the Perfetto doc, any related materials would be helpful as well.)

[sufeng-buaa]

Is this chart of trace result expected?

yes, it is expected.

what are the 3 lines at the top?

Do you mean the timelines at the top? The very top one shows the overall time range covered by the collected data. The timelines below it represent the time range of the current zoomed-in view.

what are these 3 tags?

sglang::scheduler 10614 contains CPU profiling information; it is a process, 10614 is the pid(tgid) of this process. and each line represents a thread's flame graph entry from this process.
sglang::scheduler 0 contains GPU profiling information. each line tells you what operator ran on which stream.
spans 80756 seems to just be a marker showing when the PyTorch profiler was enabled. I don't actually use it in my analysis.

I don't quite understand what you mean by 'their time events are not consistent'. Could you please explain it in more detail or show a screenshot?

[ai-easy-cpu]

@sufeng-buaa Great thank to your answer, and more explanation is below.

• "Do you mean the timelines at the top? " - I mean this section, seems no description around it.

• "I don't quite understand what you mean by 'their time events are not consistent'. Could you please explain it in more detail or show a screenshot?" - Following is a screenshot of one prefill batch, from my view, it computes the forward and copy back to CPU, but during the copy, I assume the GPU cores are idle, but it's showing GEMM kernels. (Not sure what am I interpret wrong here)

The zoomed-in GPU stream is

[sufeng-buaa]

@sufeng-buaa Great thank to your answer, and more explanation is below.

• "Do you mean the timelines at the top? " - I mean this section, seems no description around it.

* "I don't quite understand what you mean by 'their time events are not consistent'. Could you please explain it in more detail or show a screenshot?" - Following is a screenshot of one prefill batch, from my view, it computes the `forward` and copy back to CPU, but during the copy, I assume the GPU cores are idle, but it's showing GEMM kernels. (Not sure what am I interpret wrong here) The zoomed-in GPU stream is

• This area at the top looks like a heatmap of each process over time, but I'm not entirely sure, I don't use this information very often.
• Cuda kernel launches are asynchronous, so it’s normal that you’re seeing the CPU call cudaMemcpyAsync while the GPU is still busy with a GEMM. The earlier kernels haven’t finished yet — they’re still running or waiting in line. That memcpy won’t actually run on the GPU until those are done, if they’re all in the same stream. Normally, cudaMemcpyAsync doesn’t block the CPU, it just queues up the copy and returns immediately. The CPU can continue execution and will only wait for the GPU to finish when cuda synchronize is called. From what I see in your screenshot, there’s probably an implicit synchronization happening, which is making the CPU hang around polling during cudaMemcpyAsync.

[ai-easy-cpu]

Thanks for the follow-up, I will summarize this answer in question soon.

to explain the "cudaMemcpyAsync doesn’t block the CPU", I think this is because I run in non-overlap mode.