Workload Mapping Basics: Operators to Hardware: Inputs and Outputs

Inputs and outputs contract

Inputs and Outputs for Workload Mapping Basics: Operators to Hardware is anchored on Percent of model FLOPs sustained on hardware and memory-stall fraction by layer type.. 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 - Workload Mapping Basics: Operators to Hardware

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

AI accelerator deep dive

Accelerator selection quality depends on workload realism and full-stack delivery readiness.

Concept diagram

ACCELERATOR LANDSCAPE

model shape + SLA + power budget
  -> candidate platform shortlist
  -> benchmark under production-like load
  -> choose architecture + stack strategy

Metric graph

PLATFORM TRADE CURVE

throughput      ███████████
latency         ███████
energy          ████████
engineering risk █████

Metrics and artifacts to collect

• workload fit matrix

• latency-throughput sweep

• perf-per-watt dashboard

• owner and risk map

Mini case study

A platform looked best on synthetic GEMM but lost in production due to runtime overhead and memory-tail behavior.

Debug branches

• Validate workload representativeness

• Check software-stack maturity

• Tie KPI gains to product SLA

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 Percent of model FLOPs sustained on hardware and memory-stall fraction by layer type., required artifacts (Operator-to-kernel mapping sheet with tiling assumptions, fusion plan, and expected bottleneck class.), owner, and validation scope.

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