Operator Fusion and Tiling: Reports and Metrics

Reports and metrics

Reports and Metrics for Operator Fusion and Tiling is anchored on Kernel launch overhead reduction and on-chip reuse ratio for representative model blocks.. 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 - Operator Fusion and Tiling

+-----------------------------+--------------------------------+--------------------------------+---------------------------+
| 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 Kernel launch overhead reduction and on-chip reuse ratio for representative model blocks. 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

Fusion combines adjacent graph operators into fewer kernels so intermediate tensors can stay in registers or local SRAM instead of round-tripping through global memory. Tiling then partitions fused workloads into shapes that fit accelerator buffers and maximize data reuse while preserving vector or matrix engine alignment. The best fusion boundary is workload-dependent: too little fusion leaves bandwidth and launch overhead on the table, while too much fusion can increase register pressure, spill traffic, and compilation complexity. Effective mapping uses profile-driven boundaries and tile shapes that balance compute occupancy with memory-system limits. A useful explanation always ties observed symptom to a repeatable path where useful work was blocked, delayed, or diluted by overhead.

Use Kernel launch overhead reduction and on-chip reuse ratio for representative model blocks. as an alarm, then anchor action using hard evidence such as Fusion and tile-plan worksheet linking operator groups, tile shapes, and expected bottleneck shifts..

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 Operator Fusion and Tiling, reports should explain why Kernel launch overhead reduction and on-chip reuse ratio for representative model blocks. moved and which path consumed budget first.