Astra: A Multi-Agent System for GPU Kernel Performance Optimization

• URL: http://arxiv.org/abs/2509.07506v1
• Date: Tue, 09 Sep 2025 08:39:50 GMT
• Title: Astra: A Multi-Agent System for GPU Kernel Performance Optimization
• Authors: Anjiang Wei, Tianran Sun, Yogesh Seenichamy, Hang Song, Anne Ouyang, Azalia Mirhoseini, Ke Wang, Alex Aiken,
• Abstract summary: We introduce Astra, the first multi-agent system for GPU kernel optimization.<n>Within Astra, specialized agents collaborate through code generation, profiling, and planning to produce kernels that are both correct and high-performance.
• Score: 10.715861478214961
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: GPU kernel optimization has long been a central challenge at the intersection of high-performance computing and machine learning. Efficient kernels are crucial for accelerating large language model (LLM) training and serving, yet attaining high performance typically requires extensive manual tuning. Compiler-based systems reduce some of this burden, but still demand substantial manual design and engineering effort. Recently, researchers have explored using LLMs for GPU kernel generation, though prior work has largely focused on translating high-level PyTorch modules into CUDA code. In this work, we introduce Astra, the first LLM-based multi-agent system for GPU kernel optimization. Unlike previous approaches, Astra starts from existing CUDA implementations extracted from SGLang, a widely deployed framework for serving LLMs, rather than treating PyTorch modules as the specification. Within Astra, specialized LLM agents collaborate through iterative code generation, testing, profiling, and planning to produce kernels that are both correct and high-performance. On kernels from SGLang, Astra achieves an average speedup of 1.32x using zero-shot prompting with OpenAI o4-mini. A detailed case study further demonstrates that LLMs can autonomously apply loop transformations, optimize memory access patterns, exploit CUDA intrinsics, and leverage fast math operations to yield substantial performance gains. Our work highlights multi-agent LLM systems as a promising new paradigm for GPU kernel optimization.

Related papers

• CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation [51.72529978689561]
  Agent is a large-scale agentic reinforcement learning system that develops kernel expertise through three components.<n>Agent delivers 100%, 100%, and 92% faster rate over torchcompile on KernelBench.
  arXiv  Detail & Related papers  (2026-02-27T18:58:05Z)
• KernelBlaster: Continual Cross-Task CUDA Optimization via Memory-Augmented In-Context Reinforcement Learning [3.4998382481249286]
  We release KernelBlaster as an open-source agentic framework, accompanied by a test harness, verification components, and a reproducible evaluation.<n>Our method achieves mean speed-ups of 1.43x, 2.50x, and 1.50x on KernelBench Levels 1, 2, and 3, respectively.
  arXiv  Detail & Related papers  (2026-02-15T19:48:43Z)
• Optimizing PyTorch Inference with LLM-Based Multi-Agent Systems [1.2289544895833646]
  We present a framework for comparing multi-agent PyTorch optimization systems.<n>We show that exploit-heavy strategies perform best when paired with error-fixing agents.<n>The best implementation achieves an average 2.88x speedup on an H100 GPU.
  arXiv  Detail & Related papers  (2025-11-21T05:37:38Z)
• Eliminating Multi-GPU Performance Taxes: A Systems Approach to Efficient Distributed LLMs [61.953548065938385]
  We introduce the ''Three Taxes'' (Bulk Synchronous, Inter- Kernel Data Locality, and Kernel Launch Overhead) as an analytical framework.<n>We propose moving beyond the rigid BSP model to address key inefficiencies in distributed GPU execution.<n>We observe a 10-20% speedup in end-to-end latency over BSP-based approaches.
  arXiv  Detail & Related papers  (2025-11-04T01:15:44Z)
• STARK: Strategic Team of Agents for Refining Kernels [23.717055490630596]
  We introduce an agentic framework for GPU kernel optimization that explores the design space through multi-agent collaboration.<n>This framework mimics the workflow of expert engineers, enabling LLMs to reason about hardware trade-offs, incorporate profiling feedback, and refine kernels iteratively.<n>We evaluate our approach on KernelBench, a benchmark for LLM-based kernel optimization, and demonstrate substantial improvements over baseline agents.
  arXiv  Detail & Related papers  (2025-10-19T20:41:46Z)
• xLLM Technical Report [57.13120905321185]
  We introduce xLLM, an intelligent and efficient Large Language Model (LLM) inference framework.<n>xLLM builds a novel decoupled service-engine architecture.<n>xLLM-Engine co-optimizes system and algorithm designs to fully saturate computing resources.
  arXiv  Detail & Related papers  (2025-10-16T13:53:47Z)
• Omniwise: Predicting GPU Kernels Performance with LLMs [0.06666419797034795]
  We introduce Omniwise, the first end-to-end, self-supervised fine-tuning pipeline that applies large language models (LLMs) to GPU kernel performance prediction.<n>It can predict key performance metrics, including memory bandwidth, cache hit rates, GFLOPs, and arithmetic intensity, directly from kernel code without the need for code execution or profiling tools.<n>Our approach achieves over 90% of predictions within 10% relative error on GPU kernels executed on AMD MI250 and MI300X architectures.
  arXiv  Detail & Related papers  (2025-06-25T23:36:44Z)
• GPU Kernel Scientist: An LLM-Driven Framework for Iterative Kernel Optimization [0.0]
  This paper introduces an automated methodology for iteratively refining accelerator kernels.<n>Our methodology employs LLMs in a multi-stage, evolutionary process.<n>We detail how this approach navigates the challenges of the AMD MI300 target architecture.
  arXiv  Detail & Related papers  (2025-06-25T19:59:34Z)
• CUDA-LLM: LLMs Can Write Efficient CUDA Kernels [9.287036563375617]
  Large Language Models (LLMs) have demonstrated strong capabilities in general-purpose code generation.<n>We propose a novel framework called textbfFeature SearchReinforcement (FSR) FSR jointly optimize compilation and functional correctness.
  arXiv  Detail & Related papers  (2025-06-10T10:51:03Z)
• NGPU-LM: GPU-Accelerated N-Gram Language Model for Context-Biasing in Greedy ASR Decoding [54.88765757043535]
  This work rethinks data structures for statistical n-gram language models to enable fast and parallel operations for GPU-optimized inference.<n>Our approach, named NGPU-LM, introduces customizable greedy decoding for all major ASR model types with less than 7% computational overhead.<n>The proposed approach can eliminate more than 50% of the accuracy gap between greedy and beam search for out-of-domain scenarios while avoiding significant slowdown caused by beam search.
  arXiv  Detail & Related papers  (2025-05-28T20:43:10Z)
• Liger Kernel: Efficient Triton Kernels for LLM Training [6.373771349397682]
  Training Large Language Models (LLMs) efficiently at scale presents a formidable challenge, driven by their ever-increasing computational demands.<n>We introduce Liger- Kernel, an open-sourced set of Triton kernels developed specifically for LLM training.<n>With kernel optimization techniques like kernel operation fusing and input chunking, our kernels achieve on average a 20% increase in training throughput and a 60% reduction in GPU memory usage.
  arXiv  Detail & Related papers  (2024-10-14T18:17:01Z)
• Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment [56.44025052765861]
  Large language models (LLMs) have revolutionized Natural Language Processing (NLP), but their size creates computational bottlenecks. We introduce a novel approach to create accurate, sparse foundational versions of performant LLMs. We show a total speedup on CPUs for sparse-quantized LLaMA models of up to 8.6x.
  arXiv  Detail & Related papers  (2024-05-06T16:03:32Z)
• FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUs [57.12856172329322]
  We envision a decentralized system unlocking the potential vast untapped consumer-level GPU. This system faces critical challenges, including limited CPU and GPU memory, low network bandwidth, the variability of peer and device heterogeneity.
  arXiv  Detail & Related papers  (2023-09-03T13:27:56Z)
• Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures [67.47328776279204]
  This work introduces a framework to develop efficient, portable Deep Learning and High Performance Computing kernels. We decompose the kernel development in two steps: 1) Expressing the computational core using Processing Primitives (TPPs) and 2) Expressing the logical loops around TPPs in a high-level, declarative fashion. We demonstrate the efficacy of our approach using standalone kernels and end-to-end workloads that outperform state-of-the-art implementations on diverse CPU platforms.
  arXiv  Detail & Related papers  (2023-04-25T05:04:44Z)
• 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.