Warp Scheduling & Control Flow

Section goal

Scheduler policies, latency hiding dynamics, control-flow behavior, and synchronization costs in massively threaded execution.

How to study this section

1. Start with each topic hub and sketch the SIMT-to-silicon mechanism.

2. Use reports and debug pages to prove bottlenecks with evidence.

3. Practice worked examples and interview drills for design-review fluency.

4. Close with checklist and silicon impact before signoff claims.

Topics

1. warp-scheduler-architectures/ - Warp Scheduler Architectures

2. latency-hiding-and-occupancy/ - Latency Hiding & Occupancy

3. branch-divergence-predication/ - Branch Divergence & Predication

4. barrier-synchronization/ - Barrier Synchronization

Related topics

• GPU Design course hub

GPU deep dive

Warp scheduling quality determines whether latency hiding survives real control-flow and memory variance.

Concept diagram

WARP SCHEDULING LOOP

ready warp? -> issue -> dependency wait -> reconverge -> issue

Metric graph

STALL REASON SHARE

long scoreboard    ███████
divergence replay  █████
barrier wait       ███

Reports and artifacts

• eligible warp ratio

• stall reason histogram

• barrier wait cycles

• scheduler fairness report

Mini case study

A barrier-heavy kernel looked occupancy-safe, but warp arrival imbalance turned sync points into dominant stalls.

Debug branches

• Compare scheduler policy traces under bursty workloads

• Measure reconvergence delay and predication side effects

• Quantify barrier idle time before tuning launch size

Senior review question

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