SM Array Floorplanning: Reports & Metrics

Reports and metrics

Reports & Metrics for SM Array Floorplanning centers on wirelength, congestion density, and frequency vs area tradeoff. The objective is to connect profiler evidence to root-cause mechanism and release-safe action.

Reports must turn wirelength, congestion density, and frequency vs area tradeoff into a release decision. Single counter improvements are insufficient without workload context and traceability.

Trend snapshot

BEFORE / AFTER — SM Array Floorplanning

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 — SM Array Floorplanning

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

Interpretation: identify compute vs memory bound

• Track wirelength, congestion density, and frequency vs area tradeoff 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

GPU PPA closure must co-optimize floorplan locality, IR stability, thermal headroom, and timing margin.

Concept diagram

GPU PD VIEW

HBM edges + SM clusters + cache rings + power/clock grid

Metric graph

CLOSURE PRESSURE

timing risk        ███████
thermal risk       █████
IR transients      ████

Reports and artifacts

• SM-array congestion map

• thermal hotspot report

• IR drop during burst load

• timing closure dashboard

Mini case study

A floorplan iteration improved routing but worsened hotspot density, forcing DVFS throttling in sustained workloads.

Debug branches

• Map critical paths to floorplan and thermal zones

• Run burst-current IR checks, not only static IR

• Tie DVFS behavior back to physical hotspot evidence

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

SM arrays, caches, and memory interfaces must be floorplanned for locality and routability while preserving timing and power distribution quality. 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 wirelength, congestion density, and frequency vs area tradeoff 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 macro floorplan snapshot, congestion map, and placement tradeoff study.

GPU physical design must close timing, power, and thermals under highly bursty parallel workloads. 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 SM Array Floorplanning, reports should explain why wirelength, congestion density, and frequency vs area tradeoff 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.