GPU Whiteboard Framework

Whiteboard flow

1. Draw pipeline: launch -> warp scheduling -> memory/fabric -> completion.
2. Mark metric target and failure observation.
3. Mark dominant stall source and proof artifact.
4. Show one bounded fix with owner.
5. State perf, power, and correctness regression gates.

Key takeaways

• Strong answers connect software choices to microarchitectural behavior.

• Always include ownership and validation, not only optimization ideas.

GPU deep dive

GPU design closure blends architecture intent, software behavior, and reproducible performance evidence.

Concept diagram

kernel intent -> hardware execution -> measured outcome

Metric graph

throughput trend

Reports and artifacts

• kernel profile

• occupancy report

• regression dashboard

Mini case study

Tag profiler runs with exact build and launch metadata.

Debug branches

• Reproduce before optimizing

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.