CUDA Profiling

Purpose

Guide agents through profiling CUDA applications with Nsight Systems (timeline-level) and Nsight Compute (kernel-level metrics), using the NCU CLI for automated metric collection, interpreting roofline models, and diagnosing whether kernels are memory-bound or compute-bound.

When to Use

• A CUDA kernel is slower than expected and you need bottleneck identification
• Comparing kernel variants (tiling strategies, block sizes)
• Building CI performance regression checks with ncu metrics
• Correlating CPU and GPU activity in multi-stream pipelines
• Annotating application phases with NVTX for timeline visibility
• Interpreting occupancy, memory throughput, and SM utilization metrics

Workflow

1. Choose profiling tool