Harness Engineering for LLM-Driven GPU Kernel Generation

Abstract:Large language models (LLMs) can assist GPU kernel generation, but their practical effectiveness depends on whether generated code can be reliably constrained, validated, profiled, and selected. This paper presents a harness-centered system for LLM-driven GPU kernel optimization in the MLSys 2026 FlashInfer AI Kernel Generation Contest on NVIDIA Blackwell B200 GPUs. The system separates an evaluation harness from a profile-backed optimization controller: the harness enforces compilation, correctness, official-aligned timing, and artifact archival, while the controller turns profiler and workload evidence into bounded candidate-generation decisions. Human-authored skills capture operator constraints, references, profiling procedures, and promotion rules, while Codex and Claude Code agents generate candidate kernels inside those constraints. Across five operator definitions, the retained official-aligned artifacts achieved mean-latency speedups over supplied FlashInfer baselines of 1.62x, 18.05x, 29.68x, 1.12x, and 13.70x. The Agent-Assisted kernels outperform the Full-Agent artifacts across the evaluated definitions, indicating that expert-provided optimization directions, high-quality references, and workload context remain critical for reliable AI-driven kernel optimization.

24 pages, 6 figures. Extended technical report on our submission to the MLSys 2026 FlashInfer AI Kernel Generation Contest. Code: this https URL

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2607.17979 [cs.LG]

(or arXiv:2607.17979v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.17979 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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