GPU Design · All levels
Power Virus & Thermal Test: Inputs & Outputs
Inputs & Outputs for Power Virus & Thermal Test.
Inputs and outputs contract
Inputs & Outputs for Power Virus & Thermal Test centers on peak power excursion, thermal throttle duty cycle, and reliability margin. The objective is to connect profiler evidence to root-cause mechanism and release-safe action.
Use this as the handoff contract across architecture, kernel, compiler, and silicon teams. Ambiguity here creates expensive late-stage rework.
INPUTS
- workload definition and expected KPI target
- kernel launch geometry, compiler flags, and toolchain versions
- hardware assumptions: SM count, memory type, clocks, thermal envelope
- acceptance criteria for throughput, latency tail, and stability
OUTPUTS
- counter and timeline report with reproducible tags
- bottleneck classification (compute, memory, fabric, thermal)
- owner-signed mitigation proposal
- benchmark rerun summary and release recommendationHierarchy map
GPU MEMORY HIERARCHY — Power Virus & Thermal Test
[ Registers ]
latency: 1-2 cycles
|
[ Shared/L1 ]
latency: 20-40 cycles
|
[ L2 ]
latency: 150-250 cycles
|
[ HBM/GDDR VRAM ]
latency: 300ns+ effective
Optimization lens: capacity vs latencyScheduler map
WARP SCHEDULER VIEW — Power Virus & Thermal Test
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: eligible warp qualityGPU deep dive
Performance claims need verification-grade reproducibility, not one-off profiler screenshots.
Concept diagram
PERF VERIFICATION LOOP
benchmark -> profile -> optimize -> verify correctness -> regressMetric graph
RELEASE READINESS
benchmarks stable █████████
accuracy gates pass ████████
perf regressions open ███Reports and artifacts
golden benchmark suite
deterministic replay log
perf regression dashboard
accuracy/perf gate status
Mini case study
A kernel passed microbenchmarks but failed production SLA due to host-device sync overhead hidden from isolated tests.
Debug branches
Enforce end-to-end benchmarks alongside kernels
Pair every speedup with accuracy diff checks
Promote only reproducible profiler baselines
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.
Handoff explanation
Inputs are not only API parameters or RTL configuration bits. For GPU design, inputs include workload distribution, launch geometry, shader/compiler form, memory layout, clocks, thermal state, SKU target, and runtime policy. Missing any of these makes the same counter mean different things.
Outputs must be decision-ready: peak power excursion, thermal throttle duty cycle, and reliability margin, the artifact set (stress test log, thermal throttle timeline, and guardband validation report), a bottleneck class, owner, expected effect, and regression scope. A handoff that says only "performance improved" is not enough for architecture or silicon signoff.
The safest handoff format is a before/after packet: workload, revisions, counters, traces, root-cause hypothesis, chosen change, rejected alternatives, and rollback criteria.