[2603.21331] AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

Computer Science > Machine Learning arXiv:2603.21331 (cs) [Submitted on 22 Mar 2026] Title:AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search Authors:Jaber Jaber, Osama Jaber View a PDF of the paper titled AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search, by Jaber Jaber and 1 other authors View PDF HTML (experimental) Abstract:Writing high-performance GPU kernels is among the most labor-intensive tasks in machine learning systems engineering. We present AutoKernel, an open-source framework that applies an autonomous agent loop to GPU kernel optimization for arbitrary PyTorch models. Given a model, AutoKernel profiles it to identify computational bottlenecks, ranks them by Amdahl's law impact, and iteratively refines Triton or CUDA C++ kernel implementations through hundreds of experiments without human intervention. A five-stage correctness harness covering smoke tests, shape sweeps, numerical stability, determinism verification, and edge-case coverage ensures every candidate kernel is validated before any speedup is recorded. The system comprises over 9,000 lines of Python, 18 starter kernel implementations across two backends, a six-tier optimization playbook, and integration with the KernelBench benchmark suite. AutoKernel covers nine kernel types spanning the dominant operations in modern transformer architectures. On an NVIDIA H100, our Triton kernels outperform both PyTorch eager and this http URL (max-aut...