krnopt-cuda-profiling

CUDA bottleneck diagnosis works by analyzing NSYS traces to find the top runtime-consuming kernels, then using NCU reports to classify the bottleneck family and attribute it to specific source regions.

The best way to attribute CUDA bottlenecks to source regions is by using NVTX ranges and NVTX-correlated profiler views, which map runtime bottlenecks directly to actionable code inspection points.

NCU report analysis classifies CUDA kernel bottlenecks into specific families such as compute, memory, latency, or underfill, helping you understand the exact nature of the runtime limitation.

You need NVTX ranges to accurately attribute CUDA kernel bottlenecks to specific source regions, as they provide correlated profiler views that map runtime bottlenecks to actionable code inspection points.

The correct profiling workflow to find the dominant kernel consuming CUDA runtime is to first profile with NSYS to identify top kernels, then run NCU on those kernels to classify and attribute bottlenecks.

You can get structured handoff guidance after diagnosing a CUDA bottleneck, which provides seamless transitions to downstream optimization or architecture-change skills for further action.