Individual skill
flydsl-kernel-trace-analysis
Profile GPU kernels using rocprofv3 to collect ATT instruction-level traces, then analyze the trace data using hotspot_analyzer.py to identify top-K stall hotspots (VMEM-load, VMEM-wait, LDS/SMEM-wait, barrier, MFMA stalls) mapped back to source lines, and produce an actionable optimization plan. Also analyzes rocprofv3 PMC counter CSVs (L2 hit rate, partial reads, over-fetch). Usage: /kernel-trace-analysis <cmd> Can also analyze an existing dispatch dir directly: /kernel-trace-analysis --dir <path>
skills/flydsl-kernel-trace-analysis/SKILL.md
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Read excerpt
# Kernel Trace Analysis
Profile and analyze GPU kernel ATT traces to identify stall hotspots and produce
an optimization plan.
All analysis is done by running the bundled `hotspot_analyzer.py` (ATT) and
`pmc_l2_analyzer.py` (PMC CSVs); never recompute their metrics yourself.
Do **not** use GUI tools.
> If the question is only "did my change increase register pressure, spills, or
> LDS?", use `/isa-resource-diff` first — it is compile-only, needs no GPU or
> profiler run, and answers in seconds. Come here when you need to know *why* a
> kernel is slow rather than *what resources it uses*.
>