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Hello, I am interested in using this with a project of mine which is using https://github.com/Rust-GPU/Rust-CUDA/. Is it expected that this will work properly in that context? I'm assuming the idea is to instrument the kernel code itself with the nvtx code here, and then a profile will be made available ... somewhere? Perhaps NSight extracts it from the running GPU, or some well known location on the GPU host, is that correct? |
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Replies: 6 comments
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Hey @thedodd , Your idea is mostly right. You will augment your code with tracers and you use NSight Systems to execute and return a technical report. You will need an NVIDIA GPU and NSight Systems (NSight Systems also has a CLI). I recommend starting with the GUI. NSight Systems is essentially a program executor, and you instruct NSight Systems which command to run, e.g. It is important that you check NSight Systems to subscribe to NVTX annotations. Otherwise your report will not show any NVTX tracers. There are some specific instructions in examples/, but I also have an in-depth blog post if you want a quick read. |
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I'm going to close this, but feel free to re-open it if you need further assistance or consider starting a discussion. |
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So, it looks like this crate is a std crate, and therefore can not actually run GPU-side. All of the examples are for CPU side code as well. Is it possible to generate instrumentation for GPU-side code so that one might be able to profile their GPU code in greater detail? As-is, one is left to just instrument the time it takes for a kernel invocation from the vantage point of the CPU, not the code running on the GPU itself. Does that make sense? Hopefully I am asking these questions clearly |
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I'll make it a no-std crate and that should solve all issues. :) +1 |
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Crate is now no_std as of |
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I made this into a discussion since the issue (being no-std) is tracked and finished in #11 |
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I'll make it a no-std crate and that should solve all issues. :) +1