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Analyze NVIDIA Nsight Compute (ncu) profiling reports (.ncu-rep files). Extract metrics, performance data, SASS/CUDA source, and identify bottlenecks. TRIGGER when: user asks to analyze, profile, or look at an ncu report, .ncu-rep file, Nsight Compute report, kernel performance/profiling data from ncu, or asks to generate/collect an ncu profile for a tilus kernel or example script. DO NOT TRIGGER when: user is writing unrelated profiling code.
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Skill details
Versionv1.0.0
AuthorNVIDIA
Categoryanalysis
Skill IDNVIDIA/tilus/.claude/skills/ncu-report
Related skills
Write DocsConvention and format for writing instruction docstrings and RST tutorials in tilus documentation. TRIGGER when: user asks to add, update, or write documentation for tilus instructions, instruction groups, or tutorials.Perf AnalysisPerformance analysis coordination workflow. Guides profiling delegation, bottleneck classification (compute/memory/launch/communication/sync), and structured report generation. Use when the user asks to analyze performance, profile a workload, check MFU/SOL, or diagnose bottlenecks.Perf Nsight Compute AnalysisAnalyze ncu (NVIDIA Nsight Compute) profiling output: SOL% bottleneck classification, roofline analysis, occupancy diagnosis, memory hierarchy analysis, warp stall analysis, metric interpretation, and programmatic .ncu-rep report analysis. NOT for kernel writing or code generation, Nsight Systems (nsys), host-side profiling, or system-level profiling.Perf Nsight SystemsNsight Systems (nsys) CLI for system-level timeline profiling. Use when the user wants to run nsys profile, analyze .nsys-rep reports, use nsys stats/analyze/recipe commands, diagnose GPU idle time from timeline traces, or profile distributed training with NCCL overlap analysis. NOT for kernel-level metrics like SOL%, occupancy, or roofline (use perf-nsight-compute-analysis for ncu). NOT for writing or generating kernels. NOT for applying optimizations like CUDA Graphs.Perf Workload ProfilingCode instrumentation for timing workloads. Two scenarios: (1) Training loop — inject manual timing to report per-iteration latency, throughput (samples/sec), and data load time. (2) Standalone kernel/op — write CUDA event timing code with warmup, per-iteration statistics, and anti-pattern avoidance. Also covers NVTX annotation for labeling profiler timelines. NOT for: running or analyzing profiler tools (nsys, ncu, Nsight Systems, Nsight Compute), writing kernels (Triton, CuTe, CUDA), applying oTrt Perf AnalysisValidate and analyze TensorRT performance data from paired layer-info JSON and profile/latency JSON files. Use when asked to inspect TensorRT, TRT, torch-tensorrt, or ONNX-TensorRT perf reports, verify that layer/profile JSON files are valid and from the same model, infer basic model information, find likely fusion or latency optimization opportunities, and produce a concise Markdown performance report or structured JSON data.