Latency Hiding & Occupancy: Worked Example

Worked example

Worked Example for Latency Hiding & Occupancy centers on memory-latency cover ratio, long scoreboard stall %, and active warp depth. The objective is to connect profiler evidence to root-cause mechanism and release-safe action.

A regression flags memory-latency cover ratio, long scoreboard stall %, and active warp depth. Correct triage freezes revisions, validates mechanism with counters/traces, then applies one reversible fix before full rollout.

Execution snapshot

SIMT EXECUTION — Latency Hiding & Occupancy

warp 0 lanes:  0 1 2 3 4 5 6 7 ... 31
active mask :  1 1 1 1 0 0 1 1 ...  1
instruction :  IF branch taken on active lanes

cycle 10: issue warp 0
cycle 11: issue warp 3
cycle 12: warp 0 reconverges

Focus: lane masking and warp progress
Metric tracked: memory-latency cover ratio, long scoreboard stall %, and active warp depth

Eligible warps vs long stalls

WARP SCHEDULER VIEW — Latency Hiding & Occupancy

cycle ->      0    1    2    3    4
eligible   [W1,W2,W5] [W2] [W2,W7] [W7] [W3,W7]
issued         W1      W2    W7      W7    W3
stall reason    -    dep wait  -   mem wait  -

Scheduler objective: keep issue slots non-empty.
Focus: demonstrate occupancy thresholds needed to hide DRAM latency

1. Capture baseline and regressed workload traces.

2. Tag launch geometry, build revisions, and runtime environment.

3. Compare expected vs observed warp and memory behavior.

4. Collect latency cover model, occupancy-vs-throughput curve, and stall reason timeline.

5. Apply one bounded fix and predefine rollback conditions.

Did the fix hold?

BEFORE / AFTER — Latency Hiding & Occupancy

metric quality
  ^
  |                        o target region
  |                 o post-fix validation
  |            o
  |      o baseline (failing)
  +------------------------------------------> iteration
      evidence capture  mechanism fix  closure

Use this to prove improvement is causal, not incidental.

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?

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.

Worked-example reasoning

Suppose memory-latency cover ratio, long scoreboard stall %, and active warp depth regresses on one product workload. The shallow answer is to tune launch shape or widen a buffer. The deeper answer is to first compare baseline and regressed traces, then explain which part of Warp-level multithreading overlaps stalled warps with ready warps; occupancy and scheduler quality determine how much latency can be hidden. changed.

If the first failing evidence is lane-mask loss, investigate divergence and reconvergence. If it is transaction inflation, inspect coalescing and memory layout. If it is eligible-warp starvation, inspect dependencies, barriers, and scoreboard waits. If it is stable until temperature rises, pull in power and physical-design evidence.

Only after that classification should the team choose a fix. The fix might be a kernel rewrite, compiler scheduling change, cache policy, arbitration adjustment, RTL change, floorplan change, or product workload guardrail.