Workload Mapping Basics: Operators to Hardware: Interview Drills

Interview drills

Interview Drills 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.

PROMPT
You observe regression in Percent of model FLOPs sustained on hardware and memory-stall fraction by layer type. for Workload Mapping Basics: Operators to Hardware. Explain root cause and release decision.

STRONG ANSWER
1. Defines workload and first failing mechanism.
2. Explains mechanism: 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.
3. Requests proving artifact: Operator-to-kernel mapping sheet with tiling assumptions, fusion plan, and expected bottleneck class.
4. Proposes bounded fix + owner + rollback-safe validation.

WEAK ANSWER
Gives generic optimization ideas without mechanism proof or ownership.

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.

Interview answer expansion

A strong answer on Workload Mapping Basics: Operators to Hardware names the workload symptom, explains mechanism (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.), and proposes one measurable validation plan.

Then it identifies owner and fallback action if the proposed fix under-delivers.

The goal is practical engineering reasoning, not keyword listing.