Practical notes on setting up a GPU kernel-dev environment: CUDA 13's GPU cutoff, async timing pitfalls, profiler counter permissions [P]

Overview

I'm working through a free series on GPU kernel engineering (my own channel; I posted the roadmap here earlier: https://www.reddit.com/r/MachineLearning/comments/1xa4h2g/open_gpu_kernel_engineering_series_naive_gemm_to/ ) . Session 1 is a setup session. While preparing it I collected a few things that cost me time and that I rarely see in one place, so I'm sharing them here directly. The video and code are linked at the end. Check compute capability, not just "has an NVIDIA GPU&quot

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