ascend-profiling-analysis git:20260911.0ddd029A

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---
name: ascend-profiling-analysis
description: Analyze existing Ascend profiler databases, kernel_details.csv, traces or communication summaries for step, layer, operator and cross-rank timing. Use when profiler data is available and timing analysis or a profiling report is requested. New trace collection uses profiling-collection; a slow-service report without traces does not by itself select this analyzer.
---

# ascend-profiling-analysis

Analyze existing Ascend profiler data and produce evidence-linked step, layer, operator and cross-rank findings.

Tie findings to actual rank/time/row evidence and retain uncertainty in model structure or hardware context. Use config.json or verified profile-visible evidence for model dimensions. Contextual diagnoses belong in project knowledge; deterministic classification and validation belong in the analyzer code or policy data with tests.

## Agent entry

Run from the repository root using the platform's Python launcher. The workspace
selects its installed platform environment automatically.

```text
python .agents/skills/ascend-profiling-analysis/scripts/profile_analyze.py --manifest collection/manifest.json
```

Use --remote-profile-root for an existing root and profile_sweep.py for multiple roots. The normal fast mode returns analysis_summary.json and compact report artifacts. --mode full adds detailed HTML/XLSX outputs. Remote parsing keeps large traces near their storage; explicit remote endpoints and execution references select the analysis target.

Use profiling-collection only when new traces are needed. HBM component attribution belongs to memory-profiling.

Read the relevant detail only when needed:

- [behavior](references/behavior.md)