KernelArc: A Multi-Agent Framework for GPU Kernel Optimization

2026-08-17

AI Summary

本文提出KernelArc，一种多代理框架，通过并行运行的策略特化代理和特定协调机制来解决GPU内核在异构工作负载下的优化问题。

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. At the public SOL-ExecBench leaderboard snapshot recorded on July~30, 2026, these submissions ranked first on representative L1, L2, Quantization, and FlashInfer tasks. 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.

Problem

• GPU kernel optimization
• heterogeneous workloads
• multi-agent framework

Innovation

• multi-agent framework
• GPU kernel optimization
• heterogeneous workloads
• conclusions-only shared memory
• deterministic benchmark guard