AgentKernelArena: Generalization-Aware Benchmarking of GPU Kernel Optimization Agents
Sharareh Younesian, Wenwen Ouyang, Sina Rafati, Mehdi Rezagholizadeh, Sharon Zhou, Ji Liu, Yue Liu, Yuchen Yang, Hao Li, Ziqiong Liu, Dong Li, Vikram Appia, Zhenyu Gu, Emad Barsoum

TL;DR
AgentKernelArena is an open-source benchmark for evaluating AI coding agents on GPU kernel optimization, emphasizing generalization to unseen configurations and covering diverse tasks and workflows.
Contribution
It introduces a comprehensive, modular benchmark with unseen-configuration testing for AI agents optimizing GPU kernels, filling a gap in existing evaluation methods.
Findings
High correctness and compilation rates across most tasks.
Significant speedups achieved by top configurations.
Unseen-configuration transfer varies by task, with shape assumptions affecting correctness.
Abstract
GPU kernel optimization is increasingly critical for efficient deep learning systems, but writing high-performance kernels still requires substantial low-level expertise. Recent AI coding agents can iteratively read code, invoke compilers and profilers, and refine implementations, yet existing kernel benchmarks evaluate single LLM calls rather than full agent workflows, and none include both kernel-to-kernel optimization and unseen-configuration generalization testing. We present AgentKernelArena, an open-source benchmark for measuring AI coding agents on GPU kernel optimization. The benchmark contains 196 tasks spanning HIP-to-HIP optimization, Triton-to-Triton optimization, and PyTorch-to-HIP translation, and evaluates complete agent workflows in isolated workspaces using gated compilation, correctness, and performance checks, centralized scoring and an unseen-configuration…
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