Batch Size Effects: Debug Playbook

Debug playbook

Debug Playbook 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.

1. Freeze workload seed, model revision, and execution environment.

2. Locate first persistent stage where metrics diverge.

3. Classify dominant mechanism: compute, memory, scheduling, precision, or thermal.

4. Build one focused reproducer and apply one bounded fix.

5. Re-run full correctness, quality, and performance matrix.

Review memo template

ACCELERATOR REVIEW MEMO - Scheduling & Workload Mapping / Batch Size Effects

1. Symptom
   - Failing metric: Throughput, P99 latency, and energy per inference across batch sweep under target traffic patterns.
   - Workload or traffic slice: <name>
   - First failing layer or stage: <operator, schedule, memory, runtime>
   - Build and runtime tags: <compiler/firmware/runtime/hardware>

2. Mechanism hypothesis
   - Primary mechanism: Batch size directly changes accelerator utilization and queueing behavior. Larger batches improve arithmetic intensity and reduce per-item launch overhead, often raising throughput and energy efficiency, but they also increase waiting time and can worsen tail latency under bursty arrivals. Smaller batches reduce queue delay and improve responsiveness, yet may underutilize compute units and memory channels. Practical policy selection couples offline sweep data with production arrival models to choose adaptive batching thresholds that respect SLA and thermal constraints.
   - Competing hypotheses: <dataflow mismatch, memory stalls, precision drift, thermal limits>
   - Missing evidence: <counter packet, trace, replay, signoff data>

3. Proposed action
   - Smallest reversible change: <mapping/runtime/policy/config>
   - Expected movement: <throughput, p99 latency, perf-per-watt>
   - Regression risk: correctness, quality, thermal, software compatibility

4. Signoff
   - Required artifact: Batch policy curve pack showing feasible operating regions for throughput, latency, and power.
   - Required owners: inference platform owner, capacity planning engineer, SRE performance owner, power and thermal architect
   - Final decision: ship, bounded rollout, rollback, or escalate

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.

Principal accelerator review addendum

Batch Size Effects should be framed as a full-system behavior, not an isolated kernel trick. Production outcomes are set by model shape mix, compiler choices, runtime queueing policy, memory hierarchy limits, and silicon delivery margins.

Batch size directly changes accelerator utilization and queueing behavior. Larger batches improve arithmetic intensity and reduce per-item launch overhead, often raising throughput and energy efficiency, but they also increase waiting time and can worsen tail latency under bursty arrivals. Smaller batches reduce queue delay and improve responsiveness, yet may underutilize compute units and memory channels. Practical policy selection couples offline sweep data with production arrival models to choose adaptive batching thresholds that respect SLA and thermal constraints. A useful explanation always ties observed symptom to a repeatable path where useful work was blocked, delayed, or diluted by overhead.

Use Throughput, P99 latency, and energy per inference across batch sweep under target traffic patterns. as an alarm, then anchor action using hard evidence such as Batch policy curve pack showing feasible operating regions for throughput, latency, and power..

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.

Use this addendum to force explicit owner assignment, bounded fixes, and reproducible evidence before declaring closure.