Workload Mapping Basics: Operators to Hardware - Interview Scenario

Scenario

You are reviewing Workload Mapping Basics: Operators to Hardware under AI Accelerator Landscape.

OBSERVED METRIC
Percent of model FLOPs sustained on hardware and memory-stall fraction by layer type.

45-MINUTE INTERVIEW FLOW
0-5: define workload and KPI
5-15: map first mechanism failure
15-25: identify proving artifact
25-35: propose bounded fix with owner
35-45: define validation matrix and rollback

Expected depth

• Mechanism to explain: Workload mapping converts a graph of operators into executable kernels that respect compute shape, memory hierarchy, and interconnect limits. Convolutions and GEMMs tend to map well to matrix engines when tiling aligns with local SRAM capacity, while attention, reductions, and control-heavy layers can expose synchronization or bandwidth bottlenecks. Effective mapping balances tile size, fusion boundaries, quantization format, and data-layout transforms so arithmetic units stay busy without overflowing memory traffic budgets. Compile-time scheduling plus runtime autotuning is usually required because the best plan varies with sequence length, batch size, and model variant.

• Artifact to request: Operator-to-kernel mapping sheet with tiling assumptions, fusion plan, and expected bottleneck class.

• Owners to include: ML compiler engineer, runtime systems engineer, performance modeling owner, framework integration lead

AI accelerator deep dive

Accelerator outcomes are cross-layer effects of mapping, memory behavior, and runtime policy.

Concept diagram

workload -> mapping -> memory and compute behavior -> SLA outcome

Metric graph

throughput / latency / perf-per-watt trend

Metrics and artifacts to collect

• throughput and latency profile

• power and thermal telemetry

• root-cause artifact packet

Mini case study

Freeze revisions and isolate first failing workload slice before optimization debate.

Debug branches

• Classify bottleneck

• Collect reproducible evidence

• Apply bounded fix

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.