nvidia-cuda-kernel-performance-review

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 .cu and .cuh sources 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 cudaDeviceSynchronize inside 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-compute and nsight-systems commands 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.