Compiler-Runtime Mapping: Reports and Metrics

Reports and metrics

Reports and Metrics for Compiler-Runtime Mapping is anchored on Compile-time mapping accuracy versus runtime adaptation gain for changing input shapes.. Convert measurements into mechanism-backed decisions with clear owner accountability.

A useful report explains why movement happened, not only that movement happened.

Evidence matrix

EVIDENCE MATRIX - Compiler-Runtime Mapping

+-----------------------------+--------------------------------+--------------------------------+---------------------------+
| Evidence                    | Tells you                      | Does not prove                 | Next action               |
+-----------------------------+--------------------------------+--------------------------------+---------------------------+
| occupancy + timeline traces | where utilization is lost      | precise root cause             | map to memory and schedule|
| cache/SRAM/bandwidth stats  | data movement pressure         | model-level quality impact     | correlate with quality run|
| counter + profile alignment | bottleneck class confidence    | rollout safety                 | run full regression matrix|
| thermal/power telemetry     | sustained operating envelope   | correctness closure            | pair with verification    |
| before/after scenario pack  | mitigation movement            | long-tail stability            | execute guardrail replay  |
+-----------------------------+--------------------------------+--------------------------------+---------------------------+

• Track Compile-time mapping accuracy versus runtime adaptation gain for changing input shapes. on representative production workloads.

• Include build/runtime metadata in every report header.

• Correlate throughput, latency, and quality before rollout decisions.

• Call out contradictory evidence explicitly.

AI accelerator deep dive

Scheduling quality decides whether architecture headroom reaches product throughput.

Concept diagram

SCHEDULING PIPELINE

compile plan -> runtime queue -> core placement -> completion and tail behavior

Metric graph

TAIL-LATENCY DRIVERS

queueing delay      ███████
core imbalance      █████
mapping fallback    ████

Metrics and artifacts to collect

• queue wait profile

• batch policy impact

• core-level fairness

• operator fusion effect

Mini case study

Aggressive fusion reduced launch overhead but increased memory bursts that worsened p95 latency.

Debug branches

• Inspect tail first, not average

• Check fairness across streams

• Validate fusion against memory constraints

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.

Report interpretation

Compiler-runtime mapping defines how static optimization and dynamic control cooperate. The compiler captures graph-level transforms such as fusion, layout selection, and initial placement, producing executable plans tuned for expected workload envelopes. The runtime then adapts launch parameters, stream priorities, and memory residency as live traffic and input shape distributions drift. High-performing systems expose feedback loops where runtime telemetry informs compiler heuristics, reducing mismatch between offline assumptions and production behavior. A useful explanation always ties observed symptom to a repeatable path where useful work was blocked, delayed, or diluted by overhead.

Use Compile-time mapping accuracy versus runtime adaptation gain for changing input shapes. as an alarm, then anchor action using hard evidence such as Closed-loop mapping report combining offline compile profiles with runtime telemetry and retuning actions..

Scheduling determines whether hardware capability converts into SLA-level throughput and latency. Senior reviews expect a chain of proof: workload intent -> mapping -> hardware behavior -> product impact.

For Compiler-Runtime Mapping, reports should explain why Compile-time mapping accuracy versus runtime adaptation gain for changing input shapes. moved and which path consumed budget first.