Paper page - AgentKernelArena: Generalization-Aware Benchmarking of GPU Kernel Optimization Agents

Source: https://huggingface.co/papers/2605.16819 Authors:

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Abstract

AgentKernelArena is introduced as an open-source benchmark for evaluating AI coding agents on GPU kernel optimization, assessing full agent workflows and unseen-configuration generalization across multiple optimization tasks.

GPU kernel optimizationis increasingly critical for efficient deep learning systems, but writing high-performance kernels still requires substantial low-level expertise. RecentAI coding agentscan iteratively read code, invoke compilers and profilers, and refine implementations, yet existing kernel benchmarks evaluate single LLM calls rather than fullagent workflows, and none include both kernel-to-kernel optimization andunseen-configuration generalizationtesting. We present AgentKernelArena, an open-source benchmark for measuringAI coding agentsonGPU kernel optimization. The benchmark contains 196 tasks spanningHIP-to-HIP optimization,Triton-to-Triton optimization, andPyTorch-to-HIP translation, and evaluates completeagent workflowsin isolated workspaces using gatedcompilation,correctness, andperformance checks, centralized scoring and anunseen-configuration generalizationprotocol that tests whether optimizations transfer to input configurations the agent never observed. Across production agents including Cursor Agent, Claude Code, and Codex Agent, we find near-perfectcompilationand highcorrectnessrates on most task categories, with the strongest configurations achieving mean speedups of up to 6.89x on PyTorch-to-HIP, 6.69x on HIP-to-HIP, and 2.13x on Triton-to-Triton tasks. Our unseen-configuration evaluation shows that HIP-to-HIP andTriton-to-Triton optimizations largely transfer to unseen input shapes, while PyTorch-to-HIP exhibits substantialcorrectnessdrops, indicating that agents generating kernels from scratch frequently hardcode shape-specific assumptions. AgentKernelArena is designed as a modular, extensible framework for rigorous evaluation of agenticGPU kernel optimizationacross agents, tasks, and hardware targets.

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