cuda-kernel-autodev

To optimize a CUDA kernel safely, establish a correctness-verified baseline, then iterate using a keep-or-revert discipline to track speedups across workloads on your target GPU.

Nsight Compute profiling integrates with CUDA autotuning by collecting performance metrics within an experiment governance loop. This enforces strict experiment hygiene, ensuring one focused change per iteration with commit-before-run discipline for measured performance planning.

Yes, a correctness-verified baseline is required before kernel autotuning. The workflow orchestrates an experiment-by-experiment optimization loop to ensure any speedup achieved does not break kernel correctness.

The best way to plan CUDA kernel optimizations is by using roofline strategy and a technique catalog to identify bottlenecks. This approach helps choose what to try next and applies proven technique patterns for the identified performance bottlenecks.

The keep-revert workflow for CUDA kernel development requires strict experiment hygiene, enforcing one focused change per iteration and commit-before-run practices. It demands monotonic progress expectations, which may limit exploring multiple simultaneous optimizations.