Nvidia introduces nsight python for GPU kernel profiling

NVIDIA has introduced Nsight Python, an open-source tool that allows developers to profile GPU kernels directly within Python scripts. The tool integrates with Nsight Compute, enabling users to annotate and analyze code regions for hardware metrics. The latest version, 1.1, includes an experimental CUPTI backend and custom information collectors, enhancing the profiling capabilities. The tutorial demonstrates how to use Nsight Python on a Colab T4 GPU, highlighting features such as cache and clock control, and the ability to measure performance across different configurations. It also covers kernel fusion, hardware counters, and the comparison of results between Nsight Compute and the new CUPTI backend. The tool's integration with Jupyter and Colab simplifies the profiling process, offering insights into GPU performance metrics and their impact on computational tasks. The tutorial emphasizes the importance of running multiple measurements to assess stability and confidence intervals, providing a comprehensive view of kernel performance. It also outlines the necessity of using a newer Nsight Compute version than what Colab provides and the implications of default settings such as cache flushing and clock locking. By leveraging Nsight Python, developers can gain deeper insights into GPU behavior, optimizing their applications for better performance and efficiency.