Multi-Core Accelerator Scheduling: Inputs and Outputs

Inputs and outputs contract

Inputs and Outputs for Multi-Core Accelerator Scheduling is anchored on Core utilization balance, queue depth variance, and cross-core synchronization overhead.. 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 - Multi-Core Accelerator Scheduling

+----------------------+--------------------------------+--------------------------------+
| Team                 | Primary responsibility         | Closure artifact               |
+----------------------+--------------------------------+--------------------------------+
| runtime scheduler owner | mechanism and architecture intent| design rationale + tradeoffs   |
| NoC and memory subsystem owner | mapping, runtime, and execution   | profile traces + bottleneck map|
| systems performance engineer | correctness, risk, and signoff    | test report + closure memo     |
+----------------------+--------------------------------+--------------------------------+

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.

Handoff explanation

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

Outputs must include actionable interpretation of Core utilization balance, queue depth variance, and cross-core synchronization overhead., required artifacts (Scheduler decision memo with partitioning strategy, fairness policy, and contention mitigations.), owner, and validation scope.

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