Performance Counters and Profiling for Bring-up: Inputs and Outputs

Inputs and outputs contract

Inputs and Outputs for Performance Counters and Profiling for Bring-up is anchored on Counter fidelity and correlation error between measured bottlenecks and expected roofline or model-level projections.. Convert measurements into mechanism-backed decisions with clear owner accountability.

INPUTS
  - workload profile and SLA target
  - model precision and quality thresholds
  - compiler/runtime/firmware metadata
  - hardware operating envelope assumptions

OUTPUTS
  - evidence-backed bottleneck classification
  - owner-signed mitigation proposal
  - validation matrix with rollback triggers
  - release recommendation

Ownership split

OWNERSHIP LAYERS - Performance Counters and Profiling for Bring-up

+----------------------+--------------------------------+--------------------------------+
| Team                 | Primary responsibility         | Closure artifact               |
+----------------------+--------------------------------+--------------------------------+
| performance architect | mechanism and architecture intent| design rationale + tradeoffs   |
| silicon validation engineer | mapping, runtime, and execution   | profile traces + bottleneck map|
| compiler and runtime owner | correctness, risk, and signoff    | test report + closure memo     |
+----------------------+--------------------------------+--------------------------------+

AI accelerator deep dive

Bring-up speed and correctness depend on designed-in observability and replayable debug flow.

Concept diagram

BRING-UP EVIDENCE LOOP

failure symptom -> trace packet -> replay -> isolate root cause -> bounded fix

Metric graph

OBSERVABILITY VALUE

directed tests only      ██████████
plus counters            ███████
plus trace and replay    ███

Metrics and artifacts to collect

• counter completeness

• trace trigger coverage

• replay success rate

• escape-risk trend

Mini case study

A silicon-only regression closed quickly because trace identity and counter alignment were planned before tapeout.

Debug branches

• Start from first failing trace window

• Align software and hardware timestamps

• Demand reversible owner fix before signoff

Senior review question

Ask: which first-principles bottleneck class explains the symptom, and what artifact proves it reproducibly?

Key takeaways

• Tie every accelerator claim to a reproducible workload slice and one primary metric trend.

• Prefer bounded fixes with clear owner and rollback boundary over broad tuning bundles.

Common pitfalls

• Optimizing synthetic kernels without production-shape validation.

• Reading average latency while ignoring p95 and p99 behavior.

• Declaring sparse or precision wins without fallback and quality evidence.

Handoff explanation

Inputs should include workload profile, model revision, compiler/runtime versions, and platform power mode.

Outputs must include actionable interpretation of Counter fidelity and correlation error between measured bottlenecks and expected roofline or model-level projections., required artifacts (Counter reference and profiling playbook with interpretation rules and bottleneck triage flow.), owner, and validation scope.

The ideal handoff packet is reproducible: fixed seeds, explicit baseline, and rejected alternatives.