Rocm Profiler Analysis

ROCm Profiler Analysis

Use this skill when you need to turn a profiling run into structured optimization evidence instead of a raw trace file.

This skill is the AMD/ROCm/MI355X adaptation of SGLang's torch-profiler analysis workflow. It is designed for our current amdpilot stack:

• MI355X / gfx950 nodes
• ROCm 7.2
• SGLang / vLLM issue-driven runs
• dashboard artifacts, not just terminal output

Why This Exists

Raw traces are not good enough for agents or dashboards. They tell you that time was spent somewhere, but they do not directly answer:

1. Which kernel families dominate prefill or decode on ROCm?
2. Which kernels still have overlap headroom?
3. Which hotspots map back to Python or operator-level code paths?
4. Which results are actually relevant to gfx950 / MI355X, and which are only generic?
5. Which profiling outputs should be written into our canonical experiment/trial schema?

This skill standardizes that path.

Main Workflow

Preserve the same four subcommands as the upstream SGLang profiler skill:

• triage
• breakdown
• overlap
• perfetto-fix

For normal agent use, default to triage.

triage

Use this when you want one compact answer with three main outputs:

• kernel table
• overlap-opportunity table
• fuse-opportunity table

breakdown

Use this when you need one-trace category share analysis without overlap reasoning.

overlap

Use this when you have both:

• a graph-off mapping trace
• a graph-on formal trace

and need to tie overlap headroom back to code paths.

perfetto-fix

Use this only when Perfetto renders overlapped lanes incorrectly and you need a repaired trace for human inspection.

Recommended Inputs

This skill supports two input shapes:

1. Existing trace directory / trace file

   • trace.json
   • trace.json.gz
   • profiler output directory

2. Live server / live experiment

   • trigger profiling against a running SGLang or vLLM server
   • then immediately analyze the result and attach artifacts back to the run

For amdpilot integration, prefer the second path for optimization-stage profiling and the first path for post-hoc investigation.

AMD / ROCm Adaptation Rules

1. Use ROCm-native kernel categories

Do not reuse CUDA/H100/B200 assumptions. On our nodes, category tables should explicitly account for ROCm-specific paths:

• RCCL / communication
• Triton kernels
• CK / composable kernel paths
• AITER paths
• hipBLASLt / rocBLAS GEMM
• MIOpen / attention runtime kernels
• quantization
• normalization
• memory / copy / scheduler overhead

See references/rocm-kernel-categories.md.

2. Keep hardware relevance explicit

Every profiling result must declare whether it is truly relevant to our MI355X node:

• observed_arch: actual arch from the run
• arch_match: exact | compatible | unknown
• hardware_relevance_reason: short human-readable explanation

Do not hide gfx942 vs gfx950 differences.

3. Treat profiling as structured artifacts

Do not stop at stdout tables. Write stable artifacts that can be attached to an experiment or trial. Minimum recommended outputs:

• profile_summary.md
• profile_metadata.json
• kernel_table.json
• overlap_opportunities.json
• fuse_opportunities.json
• perfetto_fixed_trace.json if used

See references/artifact-contract.md.

Canonical Metadata Contract

profile_metadata.json should contain enough information to tie profiling results back to the dashboard and DB.

Minimum fields:

• experiment_id
• trial_id
• observed_arch
• arch_match
• hardware_relevance_reason
• rocm_version
• base_image
• resource_class
• gpu_device_ids
• gpu_clocks_mhz
• preflight_passed
• server_flags
• benchmark_config_hash
• model_name
• profile_stage
• source_trace_path

This is the difference between "a useful local notebook" and "a reusable profiling artifact".

Dashboard / DB Integration

The intended downstream path is:

1. run profile
2. emit structured artifacts
3. attach artifacts to experiment / trial
4. surface the summary and tables in dashboard

This skill should feed:

• experiment detail page profiling section
• trial-level artifact list
• trajectory context for optimization retries
• future data-flywheel / SFT signals

See references/dashboard-integration.md.

MI355X / gfx950 Specific Guidance

On our node, prefer profiling plans that stay grounded in actual machine facts:

• 8x MI355X
• gfx950
• ROCm 7.2
• explicit GPU ID allocation from the experiment
• exact Docker image tag, not just "ROCm 7.2"

When you compare profiles across runs, never compare them without also checking:

• base_image
• resource_class
• gpu_device_ids
• server_flags
• benchmark_config_hash

Otherwise the comparison is not trustworthy.

Suggested Rollout

Phase A: Analysis adaptation

Make the kernel classification and overlap heuristics ROCm-aware.

Phase B: Artifactization

Write the profiling outputs into stable JSON + Markdown artifacts.

Phase C: Live integration

Trigger profiling from real optimization stages and surface the artifacts in dashboard.

Relationship to Other AMD Skills

• rocprofv3-profiler Use that skill when you need low-level AMD hardware counters or kernel-level bottleneck data. Use this skill when you need SGLang/vLLM trace triage tied back to Python/operator semantics.

• env-probe Run env-probe before profiling if you suspect hidden runtime defaults are skewing results.

• rocm-crash-debug Use crash-debug when the run is failing. Use this skill when the run is healthy enough to generate profiling evidence.

Reviewer Checklist

Before calling this skill "done", verify:

1. It is explicitly MI355X / gfx950 / ROCm-aware
2. It produces structured artifacts, not just console output
3. It carries experiment/trial linkage fields
4. It distinguishes arch_match programmatically
5. It can be attached to dashboard and DB without ad-hoc parsing