NVIDIA Nsight Python Tutorial: How to Profile GPU Kernels
In this tutorial, we implement on Nsight Python, NVIDIA’s open-source Python interface to its kernel profilers.
Proof1 independent outlet
Key points
- Instead of running a script under the Nsight Compute command line and digging through a report afterward, we mark the code we care about with nsight.annotate, decorate a benchmark function with nsight.analyze.kernel, and get back a pandas DataFrame of hardware metrics per annotated region, per configuration, and per run.
- Since version 1.0, the library attaches Nsight Compute to the running Python process, making it usable in Jupyter and Colab, and the upcoming 1.1 release adds an experimental CUPTI backend, explicit tool management, and custom information collectors.
Sources (1)
- [1]NVIDIA Nsight Python Tutorial: How to Profile GPU KernelsMarkTechPost · Oct 22, 03:59 AM
In this tutorial, we implement on Nsight Python, NVIDIA’s open-source Python interface to its kernel profilers.
Instead of running a script under the Nsight Compute command line and digging through a report afterward, we mark the code we care about with nsight.annotate, decorate a benchmark function with nsight.analyze.kernel, and get back a pandas DataFrame of hardware metrics per annotated region, per configuration, and per run.
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