Latency Hiding & Occupancy: Reports & Metrics

Reports and metrics

Reports & Metrics 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.

Reports must turn memory-latency cover ratio, long scoreboard stall %, and active warp depth into a release decision. Single counter improvements are insufficient without workload context and traceability.

Trend snapshot

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.

Roofline interpretation

BANDWIDTH ROOFLINE — Latency Hiding & Occupancy

performance
   ^
   |                compute ceiling
   |               /
   |              /
   |-------------/------------------ memory ceiling
   +------------------------------------------> operational intensity
      memory-bound             compute-bound

Interpretation: identify compute vs memory bound

• Track memory-latency cover ratio, long scoreboard stall %, and active warp depth across representative workloads, not one microbenchmark.

• Include counter captures with matching compiler, driver, and firmware tags.

• Correlate scheduler stalls with memory and interconnect pressure before optimization.

• Report frame or kernel tail behavior, not only average throughput.

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.

Report interpretation

Warp-level multithreading overlaps stalled warps with ready warps; occupancy and scheduler quality determine how much latency can be hidden. This mechanism matters because GPUs are throughput machines: a small inefficiency repeated across lanes, warps, SMs, frames, or dispatches can dominate product performance even when a unit-level diagram looks balanced.

Use memory-latency cover ratio, long scoreboard stall %, and active warp depth as an entry point, not as the conclusion. A metric shift only becomes actionable after it is tied to a workload slice, a profiler capture, an architectural path, and a reproducible artifact such as latency cover model, occupancy-vs-throughput curve, and stall reason timeline.

Warp scheduling is a latency-hiding discipline, not a pure fairness problem. The review posture is therefore evidence-first: explain what the kernel or graphics workload asked for, how the GPU mapped it onto hardware, where useful work stopped, and which owner can change the smallest boundary safely.

For Latency Hiding & Occupancy, reports should explain why memory-latency cover ratio, long scoreboard stall %, and active warp depth changed, not merely that it changed. Ask whether the movement came from useful work, reduced waste, different scheduling, changed memory traffic, or hidden throttling.

A strong report includes counter consistency checks: the story told by occupancy should agree with issue activity; the memory story should agree with cache and transaction behavior; the silicon story should agree with clocks, voltage, thermals, and power telemetry.