Thread Block & Grid Hierarchy: Review Checklist

Review checklist

Review Checklist for Thread Block & Grid Hierarchy centers on SM residency, block scheduling efficiency, and launch overhead. The objective is to connect profiler evidence to root-cause mechanism and release-safe action.

• Workload scope and target KPI are explicitly documented.

• Profiler + counter evidence is reproducible with revision tags.

• Bottleneck classification is proved with mechanism-level traces.

• Mitigation includes owner, blast radius, and rollback criteria.

• End-to-end benchmark matrix confirms closure.

• Owners signed: kernel engineer, GPU architect, runtime owner.

Signoff ownership

GPU OWNERSHIP LAYERS — Thread Block & Grid Hierarchy

artifact area     owner
----------------  ----------------------------
architecture    kernel engineer
RTL/microarch   GPU architect
software/tools  runtime owner

Rule: each metric needs a named owner before signoff.

GPU deep dive

SIMT abstractions are productive only when launch geometry and divergence behavior align with hardware.

Concept diagram

PROGRAMMING MODEL STACK

host API -> kernel launch -> grid -> block -> warp -> lane

Metric graph

KERNEL EFFICIENCY TREND

warp execution efficiency  ██████████
memory replay ratio        █████
idle issue slots           ███

Reports and artifacts

• occupancy report

• warp efficiency summary

• kernel launch audit

• replay counter snapshot

Mini case study

A block-size bump improved theoretical occupancy but increased replay and reduced achieved throughput by 22%.

Debug branches

• Map launch geometry to active warps per SM

• Correlate branch masks with divergence hotspots

• Validate occupancy against achieved IPC

Senior review question

Ask: which metric and benchmark pairing proves this topic is truly closed in production context?

Key takeaways

• Always pair micro-kernel metrics with end-to-end workload impact.

• Lock toolchain, driver, and launch metadata before comparing performance results.

Common pitfalls

• Optimizing occupancy without checking memory-system saturation.

• Comparing profiler captures from different driver or compiler builds.

• Declaring wins without reproducible accuracy and performance gates.

Review checklist explanation

A checklist is not bureaucracy here; it is how GPU teams avoid confusing local wins with product wins. Every signoff item should protect against a known class of false confidence.

For Thread Block & Grid Hierarchy, the minimum checklist is workload scope, SM residency, block scheduling efficiency, and launch overhead, artifact evidence (launch geometry worksheet, occupancy calculator output, and SM residency chart), bottleneck classification, owner, rollback path, and full matrix validation.

If the change affects architecture or RTL, include correctness and PPA evidence. If it affects compiler/runtime policy, include compatibility and deployment evidence. If it affects physical design, include timing, IR, thermal, and observability evidence.