Design a kernel benchmarking setup that will not lie to you. What does it control for, and how do fake speedups get published?

Define what the timer includes, give both implementations the same conditions, and keep the sample distribution. A useful benchmark explains the workload and comparison well enough for someone else to reproduce them.

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Work through the numbers

Change the assumptions in a free calculator, then explain what the result establishes and what still needs measurement.

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The concepts behind this question

Use these concept explanations to review the ideas behind this question.

Foundational

🧮 Open Weights & Serving Engines

Serving Benchmarks That Do Not LieDesign reproducible serving experiments with realistic arrivals, cache state and failure accounting. Interpret latency-qualified throughput and conditional per-rank bounds without turning optimistic kernel estimates into production capacity.

Foundational

🖧 Hardware & Cluster Build-Out

Burn-In and Acceptance TestingAcceptance testing checks that a new cluster meets agreed performance and reliability criteria. Sustained tests and comparisons between nodes help reveal defects before production.

Advanced

🚀 Inference & Serving🔒 Premium

The Serving Optimization WaterfallTune an LLM deployment one change at a time against one fixed workload, in an order that follows the binding constraint. A measured run went from 789 to 6,568 output tokens per second, and the two biggest wins were not the knobs people usually reach for first.

Foundational

⚡ Kernels & Compilers

CUDA Programming ModelCUDA programs launch GPU kernels from host code. Thread indexing, memory access and stream ordering determine whether the result is correct.

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UP NEXT ON YOUR JOURNEY

Next in this trackYour fused attention kernel matches the reference at 512 tokens and drifts at 8,000. Find the bug.Next in this trackMap a fused bias-GELU kernel to threads and verify the final partial block Next in this trackCan you reuse a GPU buffer after the request is canceled?

FEDITOR'S NOTE

Define a reproducible timing boundary and matched workload. Explain warmup, cache behavior, device completion, run order and statistical reporting without treating any one statistic or peak percentage as proof of production performance.

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