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[Submitted on 17 Aug 2026 (v1), last revised 20 Aug 2026 (this version, v2)]
Abstract:We present KernelArc, a multi-agent framework for autonomous GPU kernel optimization across heterogeneous workloads. Strategy-specialized agents run in parallel and coordinate through conclusions-only shared memory, a deterministic benchmark guard, and read-only cross-agent state with plateau-triggered drafting. We evaluate KernelArc on NVIDIA H100 and B200 GPUs using category-representative SOL-ExecBench workloads. The resulting implementations span custom BF16 GEMM, static cuBLASLt Expert-API configuration tables, fused mixture-of-experts backward, shape-gated decoder-layer fusion, native NVFP4 grouped-query attention, and paged prefill attention. In the public SOL-ExecBench leaderboard snapshot recorded on August~20, 2026, KernelArc ranked first on every representative L1, L2, Quantization, and FlashInfer task evaluated. The trajectories support the paper’s central motivation: shared multi-agent search can broaden exploration and reach stronger incumbents within a fixed candidate budget, while the value of individual coordination features depends on the kernel and optimization stage.
Submission history
From: Joyjit Kundu [view email]
[v1]
Mon, 17 Aug 2026 19:21:23 UTC (135 KB)
[v2]
Thu, 20 Aug 2026 21:29:17 UTC (137 KB)
추출 본문 · 출처: arxiv.org · https://arxiv.org/abs/2608.17071