Batch Size Effects: Worked Example

Worked example

Worked Example for Batch Size Effects is anchored on Throughput, P99 latency, and energy per inference across batch sweep under target traffic patterns.. Convert measurements into mechanism-backed decisions with clear owner accountability.

A regression appears in Throughput, P99 latency, and energy per inference across batch sweep under target traffic patterns.. Strong closure isolates first failing stage, proves mechanism, applies one reversible fix, and validates blast radius before release.

Execution lens

ACCELERATOR EXECUTION FLOW - Batch Size Effects

request ingress and model metadata
      |
      v
graph lowering and kernel selection
      |
      v
tile/dataflow scheduling and memory placement
      |
      v
tensor execution + synchronization barriers
      |
      v
result assembly + quality/SLA validation
      |
      v
release decision and rollback guardrails

Decision matrix

EVIDENCE MATRIX - Batch Size Effects

+-----------------------------+--------------------------------+--------------------------------+---------------------------+
| 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  |
+-----------------------------+--------------------------------+--------------------------------+---------------------------+

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.

Worked-example reasoning

Suppose Throughput, P99 latency, and energy per inference across batch sweep under target traffic patterns. regresses only under burst traffic. The shallow response is clock scaling. The stronger response is to inspect queueing, mapping, and memory-pressure interactions first.

If occupancy drops with high memory stalls, prioritize locality and scheduling fixes. If occupancy remains high with latency spikes, inspect contention and fairness policy.

Pick one bounded change per hypothesis and validate against baseline artifacts.