Why Faster GPU Code Often Means Launching Fewer Kernels

Описание: A GPU can finish each tiny kernel quickly and still leave the application waiting on launch overhead and repeated memory traffic. This video explains what a CUDA kernel launch costs, how temporary buffers become the larger bottleneck, what kernel fusion changes, when CUDA Graphs solve a different problem, and why over-fusion can reduce occupancy or remove useful concurrency.

Chapters
0:00 A fast GPU fed in tiny pieces
0:36 What a kernel launch actually costs
1:27 The temporary-buffer cost
2:16 What fusion changes
3:12 Manual fusion, compiler fusion, and CUDA Graphs
4:19 When fusion makes code slower
5:12 A profiling rule you can use
6:17 Choosing the right fusion boundary

Primary references
https://developer.nvidia.com/blog/ker...
https://docs.nvidia.com/cuda/cuda-pro...
https://docs.nvidia.com/cuda/cuda-pro...
https://docs.nvidia.com/cuda/cuda-pro...
https://docs.nvidia.com/cuda/cuda-c-b...
https://docs.nvidia.com/nsight-system...
https://docs.nvidia.com/nsight-comput...
https://docs.pytorch.org/docs/stable/...

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Original video, narration, diagrams, and animations © 2026 Swetank Srivastava / SwetankLabs. Reuse or redistribution requires prior written permission. Third-party music, sound effects, and referenced materials remain subject to their respective licences and are credited above.