Summary
In this chapter, we began by emphasizing the importance of profiling and debugging in GPU computing. We discussed key challenges in GPU profiling, such as asynchronous execution, discrepancies between CPU and GPU timing, and the separation between host and device operations. Additionally, common CUDA performance bottlenecks were outlined.
We then introduced lightweight profiling tools, starting with Python's time and timeit modules and the Scalene line profiler, followed by nvtop, a Linux utility for real-time GPU monitoring. Next, we examined NVIDIA's dedicated profiling tools, beginning with Nsight Systems for timeline-based profiling and moving to Nsight Compute for detailed kernel-level analysis and access to low-level performance metrics.
Finally, we explored debugging in Numba, demonstrating how to inspect JIT-compiled functions for details such as local and shared memory usage. We also showed how to use the Numba CUDA simulator to detect issues such as out-of...
