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I’m using the PyTorch profiler to analyze sglang, and I noticed that in the CUDA timeline, some kernels show “Command Buffer Full”. This causes the cudaLaunchKernel time to become very long, as shown in the attached screenshot.

I would like to understand:

Why does the Command Buffer Full occur? Is it because the GPU limits the maximum number of concurrently running kernels?

If that’s the case, does PyTorch provide any information when launchKernel encounters a command buffer full situation?

How can I check the GPU’s command buffer information, such as maximum capacity and remaining capacity?

Thanks in advance for any insights! enter image description here

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    Ordinarily kernel launches are asynchronous. That means the CPU thread can issue the launch into a queue, and then move on to process the next line of code after the launch. It does not wait for the kernel to begin executing. However this process is supported by a queue of limited depth. Therefore, in some scenarios it is possible to fill up the queue. When the queue is full, the kernel launch process is no longer asynchronous; the CPU thread will wait at the launch point for a queue slot to open up. This is mentioned in a various internet posts that you can find with a bit of searching. Commented Oct 23 at 13:53
  • Here is a related question/answer, there are others. There is no method provided by CUDA to check any characteristics of the queue, such as its depth, status, or remaining space. This doesn't really have anything directly to do with kernel concurrency. Commented Oct 23 at 16:13
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