Nvidia cuda kernel performance review
Skill Raishin/vanguard-frontier-agentic/skills/nvidia/nvidia-cuda-kernel-performance-review
Curated marketplace of AI skills, agents, and rules for cloud, zero-trust, and compliance-aware engineering - works with Claude Code, Codex, Cursor, Copilot, and more.
npx -y skills add Raishin/vanguard-frontier-agentic --skill nvidia-cuda-kernel-performance-reviewAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 18 stars18 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Use this skill when reviewing CUDA C/C++ kernel sources statically against NVIDIA's published performance guidance — global-memory coalescing, shared-memory bank conflicts, warp divergence, occupancy and register pressure, stream/event concurrency, kernel launch parameter selection. Trigger when the user asks whether a `.cu` or `.cuh` file follows NVIDIA's published performance and correctness guidance, or asks for the exact `nsight-compute` or `nsight-systems` invocation to run themselves.
SKILL.md
2.9 KB, 432 tokens by cl100k_base, as published. Nobody here has run it
NVIDIA CUDA Kernel Performance Review
Purpose
Static review of CUDA C/C++ kernels for memory coalescing, shared-memory bank conflicts, occupancy, register pressure, and stream concurrency against NVIDIA's official CUDA Programming and Best Practices Guides. This skill is doc-anchored: it grounds review findings in NVIDIA's published documentation rather than in a certification blueprint, because no NVIDIA certification currently covers this developer-facing surface as a standalone exam objective.
Lean operating rules
- Prefer the user's actual
.cuand.cuhsources as evidence; otherwise fall back to documentation-based inference and say so. - Separate confirmed facts from inference. If kernel launch params, register count, or shared-memory usage were not directly read from source, say so.
- Treat global-memory access patterns where adjacent threads in a warp do not access adjacent words as a high finding — coalescing is broken.
- Treat shared-memory access patterns where lanes in a warp hit the same bank with different addresses as a high finding — bank conflicts serialize the warp.
- Treat warp-divergent control flow inside hot loops as a medium finding — occupancy and instruction throughput drop.
- Treat kernel launches with thread-block sizes that are not multiples of 32 as a medium finding — warp utilization is reduced.
- Treat use of
cudaDeviceSynchronizeinside hot paths or per-batch loops as a medium finding — stream concurrency is destroyed. - Treat absence of
__restrict__qualifiers on non-aliasing pointer arguments as a low finding — the compiler cannot keep loads in registers. - Always emit the exact
nsight-computeandnsight-systemscommands the user should run for runtime confirmation — do not execute them.
Response minimum
Return, at minimum:
- the scoped target (kernel inventory and target architectures, global-memory access posture, shared-memory and register posture, occupancy and launch-parameter posture, stream and synchronization posture, recommended Nsight invocations) and evidence level,
- findings labelled critical / high / medium / low,
- recommended NVIDIA-tooling invocations the user should run themselves,
- safe next actions and assumptions or blockers.
What ships with it: 1 file
1.5 KB alongside SKILL.md
- metadata.json1.5 KB