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Agentic Kernel Optimization: Generating State-of-the-Art GPU Kernels Without Hand-Written CUDA — arXiv:2608.14560

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.14560
License (as archived): CC0-1.0
Archived: 2026-10-18T19:01:58.901Z
SHA-256 of the archived text: aec626c6ef588c01cb24f9cbcc2eedef76a17c94fe3920f14512598bd4265992
Stable URL of this page: https://metraipolis.com/documento/dc_3fa044fbe3a0

How to cite

Agentic Kernel Optimization: Generating State-of-the-Art GPU Kernels Without Hand-Written CUDA — arXiv:2608.14560 — arXiv (en), https://arxiv.org/abs/2608.14560 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_3fa044fbe3a0 (Metraipolis archive dc_3fa044fbe3a0, sha256 aec626c6ef588c01cb24f9cbcc2eedef76a17c94fe3920f14512598bd4265992)

BibTeX

@misc{dc3fa044fbe3a0,
  title = {Agentic Kernel Optimization: Generating State-of-the-Art GPU Kernels Without Hand-Written CUDA — arXiv:2608.14560},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.14560},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_3fa044fbe3a0; sha256 aec626c6ef588c01cb24f9cbcc2eedef76a17c94fe3920f14512598bd4265992},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.14560. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We study whether general-purpose code agents can produce state-of-the-art GPU kernels without any manually written CUDA code. We investigate this question using representative workloads from FlashInfer-Bench, focusing on the Fused MoE, DSA TopK Indexer, and DSA Sparse Attention, and evaluate all generated kernels under the correctness-gated FlashInfer-Bench protocol on NVIDIA B200 GPUs. Starting from the PyTorch implementations, workload definitions, benchmark commands, and a compact set of CUDA optimization skills, we build a kernel optimization workflow in Houmao, a multi-agent orchestration framework for heterogeneous coding agents, to generate, debug, profile, and optimize the kernels. Humans remain strictly in an orchestration role: defining the workflow, enforcing correctness and anti-hacking constraints, supplying key references, and redirecting the search when progress stalls, without reviewing or editing the kernel code itself. Across roughly 1.9 billion agent tokens, the resulting kernels achieve speedups of 92.68x on Fused MoE, 1101.02x on DSA TopK Indexer, and 181.35x on DSA Sparse Attention relative to the PyTorch reference implementations, while also significantly outperforming the corresponding FlashInfer baselines. In the official evaluation of the MLSys 2026 FlashInfer AI Kernel Generation Contest, our generated Fused MoE kernel achieves a 1.71x speedup over the FlashInfer baseline, exceeding the top result of the Fused MoE agent-assisted track, which reports a 1.68x speedup. These results suggest that, under a disciplined correctness-first workflow, code agents can serve as effective autonomous optimizers for modern GPU kernel development.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-08-18 in cs.DC).
Categorie arXiv: Distributed, Parallel, and Cluster Computing (cs.DC) ; Software Engineering (cs.SE).
Pagina del lavoro: https://arxiv.org/abs/2608.14560 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T18:07:16.056Z dalla pagina https://arxiv.org/catchup/cs.DC/2026-08-18?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).