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

• URL: http://arxiv.org/abs/2603.21331v1
• Date: Sun, 22 Mar 2026 17:15:28 GMT
• Title: AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search
• Abstract summary: Auto Kernel is a framework that applies an autonomous agent loop to GPU kernel optimization for arbitrary PyTorch models.<n>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.<n>On an NVIDIA H100, our Triton kernels outperform both PyTorch eager and torch.compile (max-autotune) on the majority of tested playbooks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 torch.compile (max-autotune) on the majority of tested configurations: 5.29x over eager on RMSNorm, 2.82x on softmax, and 2.21x on cross-entropy, while beating torch.compile by 2.83x, 3.44x, and 2.94x respectively. In community deployment, an AutoKernel-optimized kernel achieved first place on the vectorsum_v2 B200 leaderboard. The full system is available at https://github.com/RightNow-AI/autokernel.

Related papers

• KernelBench-Verified: Do LLM-Generated Kernels Actually Beat PyTorch? [13.026983194416168]
  Large language models (LLMs) can generate custom kernels that appear to outperform PyTorch on benchmarks such as KernelBench.<n>We demonstrate that frontier models frequently engage in reward hacking to artificially inflate reported performance.
  arXiv  Detail & Related papers  (2026-06-26T03:22:50Z)
• Kernel Forge: An Agent Harness for LLM-based Generation and Optimization of CUDA Kernels [2.833957521086226]
  optimizing machine learning kernels is one of the most direct ways to reduce latency and cost.<n>We present Kernel Forge, an open-source agentic harness that accepts any unmodified PyTorch model in place.<n>We evaluate it on four PyTorch models spanning vision, diffusion, and LLM on an NVIDIA DGX Spark with GB10 GPU.
  arXiv  Detail & Related papers  (2026-06-02T14:16:58Z)
• GPU Forecasters: Language Models as Selective Surrogates for Kernel Runtime Optimization [73.92934811090163]
  We study how LLMs can serve as selective GPU surrogates for kernel evaluation.<n>We measure whether their forecasts are accurate, calibrated, and practically useful for recovering fast kernels under limited GPU-measurement budgets.<n>Our experiments demonstrate that LLMs can accurately forecast relative kernel performance, that their utility can be improved through reinforcement learning.
  arXiv  Detail & Related papers  (2026-05-29T15:56:08Z)
• KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization [37.008713142905336]
  We present KernelSkill, a multi-agent framework with a dual-level memory architecture.<n>On Kernel Levels 1-3, KernelSkill achieves a 100% success rate and average speedups of 5.44xBench, 2.82x, and 1.92x over Torch Eager.
  arXiv  Detail & Related papers  (2026-03-10T13:43:38Z)
• CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization [36.794824560677064]
  CudaForge is a training-free multi-agent workflow for kernel generation and optimization.<n>By leveraging base models like OpenAI-o3, CudaForge achieves 97.6% correctness generated kernels and an average 1.68$times$ speedup.
  arXiv  Detail & Related papers  (2025-10-23T22:52:00Z)
• KernelBench: Can LLMs Write Efficient GPU Kernels? [36.4117525096377]
  KernelBench is an open-source framework for evaluating language models' ability to write fast and correct kernels.<n>We introduce a new evaluation metric fast_p, which measures the percentage of generated kernels that are functionally correct.<n>Our experiments show that frontier reasoning models perform the best out of the box but still fall short overall.
  arXiv  Detail & Related papers  (2025-02-14T19:30:53Z)
• Explore as a Storm, Exploit as a Raindrop: On the Benefit of Fine-Tuning Kernel Schedulers with Coordinate Descent [48.791943145735]
  We show the potential to reduce Ansor's search time while enhancing kernel quality. We apply this approach to the first 300 kernels that Ansor generates. This result has been replicated in 20 well-known deep-learning models.
  arXiv  Detail & Related papers  (2024-06-28T16:34:22Z)
• Kernel Identification Through Transformers [54.3795894579111]
  Kernel selection plays a central role in determining the performance of Gaussian Process (GP) models. This work addresses the challenge of constructing custom kernel functions for high-dimensional GP regression models. We introduce a novel approach named KITT: Kernel Identification Through Transformers.
  arXiv  Detail & Related papers  (2021-06-15T14:32:38Z)
• Kernel methods through the roof: handling billions of points efficiently [94.31450736250918]
  Kernel methods provide an elegant and principled approach to nonparametric learning, but so far could hardly be used in large scale problems. Recent advances have shown the benefits of a number of algorithmic ideas, for example combining optimization, numerical linear algebra and random projections. Here, we push these efforts further to develop and test a solver that takes full advantage of GPU hardware.
  arXiv  Detail & Related papers  (2020-06-18T08:16:25Z)
• Learning Deep Kernels for Non-Parametric Two-Sample Tests [50.92621794426821]
  We propose a class of kernel-based two-sample tests, which aim to determine whether two sets of samples are drawn from the same distribution. Our tests are constructed from kernels parameterized by deep neural nets, trained to maximize test power.
  arXiv  Detail & Related papers  (2020-02-21T03:54:23Z)
• PolyScientist: Automatic Loop Transformations Combined with Microkernels for Optimization of Deep Learning Primitives [55.79741270235602]
  We develop a hybrid solution to the development of deep learning kernels. We use the advanced polyhedral technology to automatically tune the outer loops for performance.
  arXiv  Detail & Related papers  (2020-02-06T08:02:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.