--- name: perf-baseline-record last_validated: 2026-05-20 description: >- Capture any performance measurement (NCCL bandwidth, MFU, step-time, latency, throughput, kernel-time, anything operator-defined) into a versioned baseline registry under experiments/artifacts/perf-baselines/. Workload-agnostic. Records full provenance (operator, cluster, cohort, git SHA, capture command, raw payload sha256) so future regressions can be diffed deterministically by the paired perf-baseline-diff skill. Triggers on "record baseline", "register baseline", "baseline this measurement", "save as baseline", "perf-baseline-record", "baseline perf-of-record", or any combination of "record / register / save / capture / store" with "baseline / golden / reference / perf-of-record". allowed-tools: - mcp__profile_and_optimize__perf_baseline_record - mcp__profile_and_optimize__search_runbooks - Bash(sha256sum:*) - Bash(date:*) - Bash(git:*) - Bash(uname:*) - Bash(hostname:*) - Bash(jq:*) - Read - Write --- # perf-baseline-record ## Purpose Make perf measurements **durable, comparable, and diff-able** by recording them into a versioned registry with full provenance. Replaces the hand-maintained "best run of record" table pattern: any operator can register any measurement (NCCL BW, MFU, latency, throughput, kernel-time, anything that produces a number or structured JSON) and have future regressions diff against it deterministically. Pairs with [`perf-baseline-diff`](/plugins/profile-and-optimize/skills/perf-baseline-diff/SKILL.md). Together they replace the historical "I'll just remember last week's number" anti-pattern with a registry that survives the operator changing. ### Inference-perf use For inference perf measurements specifically (TTFT / ITL / throughput / tok-per-user / prefix-cache-hit-rate captured by [`inference-perf-bench`](/plugins/profile-and-optimize/skills/inference-perf-bench/SKILL.md)), prefer the [`inference-perf-baseline-bridge`](/plugins/profile-and-optimize/skills/inference-perf-baseline-bridge/SKILL.md) skill. It wraps this verb with the canonical `inference_perfbench_v1` schema, parses the upstream perf-bench bundle automatically, and supplies per-metric tolerances tuned for inference workloads (TTFT 5%, throughput 3%, cache-hit-rate 2 absolute pts). No new MCP verb is added. The bridge is a thin frontmatter-fluent wrapper around `perf_baseline_record` + `perf_baseline_diff`. > This skill is backed by a native MCP verb: `mcp__profile_and_optimize__perf_baseline_record`. The verb does the registry write atomically (provenance + source snapshot + INDEX.md update in one shot). The Bash-tool path documented below remains supported as a fallback for environments without the MCP server. ## Why a registry, not a one-shot A single perf measurement is a number with no shoulder. A registered baseline carries: - **Provenance**: who recorded it, on what cluster, on what cohort, with what git SHA, with what capture command. - **Raw payload hash**: `sha256sum` of the source data the measurement was derived from. Lets `perf-baseline-diff` confirm that two measurements were derived from the same shape of input. - **Versioning**: every registration is a timestamped immutable directory. Nothing is ever overwritten. Going back N versions is `ls` + `cat`. - **Schema check**: the measurement shape (units, dimensions, ranges) is validated against an operator-supplied JSON schema. Diffing apples-to-oranges fails fast. ## When to use - After a fabric benchmark run (e.g. the upstream `nccl-tests` suite) on a fresh cluster / driver / firmware release - register the per-N busBW numbers as the new baseline. - After an `nvbandwidth` link sweep - register the per-node heatmap. - After a green MLPerf training run - register the step-time + MFU. - After a kernel-level profile capture - register the top-N kernel-time table. - Anytime the operator hits a perf number worth remembering. Do **not** use this skill for: - One-shot debugging measurements that don't need to be remembered - those go in `experiments/artifacts///` directly without registration. - Per-run results that change with every run by design (e.g. live goodput counters) - record those as ordinary run artifacts, not baselines. ## Example prompts - "Register this nccl-tests bundle as the new B200 8B busBW baseline." - "Save this MFU number as the perf-of-record for llama31_8b on cohort X." - "Baseline the nvbandwidth heatmap I just captured." - "Record this latency number as the inference-baseline for the deepseek-v3 image." - "perf-baseline-record family=llama31_8b measurement=nccl_busbw value=480 unit=GB/s source=" - `/perf-baseline-record --family llama31_8b --measurement nccl_busbw --source experiments/artifacts/nccl-tests//results.json` ## Prerequisites 1. **Source data** - operator names `--source ` (file or directory). Must be readable. 2. **Family + measurement-name** - `--family ` (e.g. `llama31_8b`, `gb300-cluster`, `deepseek-v3-inference`) + `--measurement ` (e.g. `nccl_busbw`, `step_time`, `nvlink_pairwise_bw`). 3. **Value** - `--value ` for scalar baselines, or `--source` for structured baselines (the source file IS the baseline payload). 4. **Optional units** - `--unit `. 5. **Optional schema** - `--schema ` for structured baselines. Skill validates the source against the schema. 6. **`PROFILE_AND_OPTIMIZE_REPO_ROOT`** for the registry path. ## Interaction style Mostly autonomous. One pause: confirm the registry path + provenance before write. ## Workflow ### Phase 0: resolve baseline metadata Resolve and report: - **Family** (`` becomes the top-level registry directory). - **Measurement name** (`` becomes the per-measurement subdirectory). - **Value / source** (scalar OR structured). - **Units** (free-text, operator-supplied). - **Schema** (optional. If provided, validate the source). - **Registry path**: `${PROFILE_AND_OPTIMIZE_REPO_ROOT}/experiments/artifacts/perf-baselines////`. Ask: "Register?" One confirmation. ### Phase 1: gather provenance Collect in parallel: - `Bash(date -u +%Y-%m-%dT%H:%M:%SZ)` - registration time. - `Bash(hostname)` - workstation that recorded it. - `Bash(uname -a)` - workstation kernel. - `Bash(git -C ${PROFILE_AND_OPTIMIZE_REPO_ROOT} rev-parse HEAD)` - current SHA of the bundled server checkout. - `Bash(sha256sum )` - content hash of the source payload. - Operator identity: `${USER}` - recorded for the audit trail, not as an ownership claim. ### Phase 2: schema validate (if provided) If `--schema ` was provided, parse the source as JSON and validate against the schema. If invalid, stop and report. ### Phase 3: write **Preferred (MCP verb):** ```text mcp__profile_and_optimize__perf_baseline_record with: args: ["--family", "", "--measurement", "", "--source", "", "--value", "", # omit for structured baselines "--unit", "", "--notes", "", "--json"] ``` The verb does the entire write atomically: creates the immutable directory, snapshots the source, hashes it, writes `baseline.json` + `SOURCE.md`, appends to `INDEX.md`. Returns the entry directory path and the source SHA-256. **Fallback (Bash-tool path, no MCP server):** ```text mkdir -p ${PROFILE_AND_OPTIMIZE_REPO_ROOT}/experiments/artifacts/perf-baselines//// Write baseline.json with: { "family": "", "measurement": "", "value": , "unit": "", "source_path": "", "source_sha256": "", "schema_path": "", "registered_at_utc": "", "registered_by": {"team": "", "operator_user": ""}, "workstation": {"hostname": "", "uname": ""}, "profile_and_optimize_sha": "", "notes": "" } Copy into the registry directory as source-snapshot. Write SOURCE.md with human-readable provenance + the capture command (the operator's original prompt that produced the source). ``` ### Phase 4: update the per-measurement index ```text ${PROFILE_AND_OPTIMIZE_REPO_ROOT}/experiments/artifacts/perf-baselines///INDEX.md ``` Append a row to the index (one line per registered baseline). Index becomes the chronological history of this measurement. ### Phase 5: confirm + hand off Print the registry path. Recommend: - "Diff a future measurement against this baseline" -> [`perf-baseline-diff`](/plugins/profile-and-optimize/skills/perf-baseline-diff/SKILL.md) `--baseline `. - "Make this the documented run-of-record" -> cite the registry path in your project docs. The `INDEX.md` row is the canonical history. ## Registry layout ``` ${PROFILE_AND_OPTIMIZE_REPO_ROOT}/experiments/artifacts/perf-baselines/ / # e.g. llama31_8b, gb300-cluster / # e.g. nccl_busbw, step_time INDEX.md # chronological history / # immutable per-registration dir baseline.json SOURCE.md source-snapshot. # exact source data / ... ``` ## Safety - **Never overwrite.** Every registration is a new timestamped directory. The registry is append-only by construction. - **Schema validation fails fast.** If the source doesn't match the schema, the registry write does not happen. No corrupt baselines. - **Provenance is mandatory.** A baseline without a `source_sha256` + `profile_and_optimize_sha` + `registered_at_utc` is rejected by the writer. - **Attribution is audit-only.** `baseline.json` captures the operator's `${USER}` for the audit trail. It is never asserted as a "lead" / "owner" claim. ## Full-context reporting (no bare numbers) Per `docs/METHODOLOGY.md` "Full-context reporting": every number this skill emits MUST carry its full measurement-context descriptor, and every comparison MUST be matched on it. A bare `tok/s` / TPOT / BW / %SoL / speedup is a defect - it cannot set a default, ship a config, or appear in a report. - **Identity:** model (+HF path), hardware (exact ceiling token `GB300`/`B200`), quant, kv-cache dtype. - **Parallelism:** TP, DP (replicas), PP, EP, parallel_strategy. - **Serving cfg:** max-num-seqs, max-num-batched-tokens, gpu-memory-utilization, max-model-len, cudagraph_mode/enforce_eager, async_scheduling, prefix-caching. - **Workload:** dataset, ISL/OSL (or mean in/out tokens), concurrency, num-prompts. - **Regime:** warm vs cold. Latency vs throughput tier. - **Stack:** image/vllm commit, bench backend, serving engine. - **Grounding:** `%SoL` (+ ceiling key from `configs/sol-ceilings.yaml` - never inline a peak), sol_rigor (L1-L4), trials n (mean±std), same-node, baseline named. - **Per-number exact shape (no smoothing):** when reporting more than one number, keep EACH with its own exact shape (ISL/OSL, concurrency, dataset, regime) - never normalize a set to one uniform descriptor that hides per-point variation (e.g. `c=1 @ ISL1024/OSL256` + `c=64 @ ISL4096/OSL512`, NOT one shared "random"). Per `docs/METHODOLOGY.md` "Speed-of-light framing", `baseline.json` SHOULD carry an optional `sol_pct` field per measurement, computed at record time and frozen with the baseline (so future diffs can report SoL deltas against an immutable reference). Schema fragment: ```json { "measurement_value": 310.0, "units": "GB/s", "sol_pct": 17.2, "sol_ceiling_key": "b200_sm100.nvlink5_tbps", "sol_ceiling_value": 1800.0 } ``` - `sol_ceiling_key` is the YAML path used to source the peak (e.g. `b200_sm100.nvlink5_tbps`, `gb300_nvl72.nvfp4_dense_pflops`) - sourced from `configs/sol-ceilings.yaml`. Never inline. - `sol_ceiling_value` is the value snapshotted at record time so future diffs are stable even if the YAML's peak number is later updated. - `sol_pct` is computed at record time = `measurement_value / sol_ceiling_value * 100` for bandwidth measurements. For compute measurements use the analogous TFLOPS / peak_TFLOPS ratio. Baselines for measurements that are not roofline-bound (eval scores, acceptance rates, etc.) MAY omit the `sol_*` fields. ## Next lever / BREAKTHROUGH (Grind Mandate) If this skill emits a measured result, its output MUST end by naming the **next perf lever**, its **expected unlock** (direction + rough magnitude), and the **gate** that proves/refutes it, per `docs/METHODOLOGY.md` "Always be grinding". A measured win is the new floor, not the finish -- so **do everything we can to find the next BREAKTHROUGH**: the highest-EV unlock toward Speed-of-Light (a new champion / kernel / router / quant / parallelism / spec-decode win, or an unblocked stack), not just the next micro-lever. Rank the candidate breakthrough levers by value x cost (the GRIND FRONTIER, `perftunereport value_view`), pursue the top, bank the rest with evidence. Record WHY a refuted lever loses, update the standing frontier in the active bundle's `HANDOFF.md`. Never conclude "exhausted/optimal/done" without an explicit next-lever frontier (an empty frontier AND a documented SoL wall only). Delete this section ONLY if the skill produces no measurements. ## Verdict rigor (DRAFT vs VERDICT) Per `docs/METHODOLOGY.md` "Verdict rigor: DRAFT vs VERDICT", tier every number this skill records. Default to **DRAFT** (provisional). A baseline is only VERDICT-grade when it is variance-controlled (same-node, >=3 trials, mean +/- std), metric-isolated (median TPOT/ITL for decode-latency, not output tok/s at small num_prompts), and captured against a production-representative config. Record the tier + provenance in `baseline.json` so a downstream diff can be promoted to a VERDICT. ## Source-of-truth references - [`docs/METHODOLOGY.md`](/docs/METHODOLOGY.md) - measurement canon (full-context reporting, verdict rigor, speed-of-light framing). - [`server/docs/perf-lake-contract.md`](/plugins/profile-and-optimize/server/docs/perf-lake-contract.md) - raw-payload provenance rules. - [`server/AGENTS.md`](/plugins/profile-and-optimize/server/AGENTS.md) - fail-fast + artifact-durability rules.