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KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Changxin Ke, Rui Zhang, Zixiang Fang, Zhenghong Li, Yuanbo Wen, Jiashuo Shen, Shuo Wang, Jiaming Guo, Ling Li, Qi Guo, Yunji Chen

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Tags: Computer science, CUDA, LLM, Machine learning, nVidia, nVidia A100, Package, Triton

October 4, 2026 by hgpu

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

Liam Cooper, Shinnung Jeong, Hyeran Jeon, Jeffrey Young, Hyesoon Kim

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Tags: Computer science, CUDA, Hardware Architecture, LLM, nVidia, nVidia A100, nVidia H100, nVidia L40s, Package

September 28, 2026 by hgpu

Stencil Computation at the Intersection of AI and HPC

Timothee Ewart, Mauricio Araya-Polo

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Tags: AI, AMD, AMD Radeon Instinct MI325X, Computer science, HPC, Intel Arc B580, Intel B70, Intel Data Center GPU Max 1550, nVidia, nVidia A100, nVidia H100, nVidia RTX PRO 6000, performance portability, Stencil computation, SYCL, Triton

September 14, 2026 by hgpu

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

Yaozhi Zheng, Yilei Jiang, Manyuan Zhang, Yuxuan Wan, Kaituo Feng, Tianshuo Peng, Bo Zhang, Xiangyu Yue

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Tags: Code generation, Computer science, Heterogeneous systems, nVidia, nVidia A100, Package

July 13, 2026 by hgpu

Optimizing CUDA like a Human: Micro-Profiling Tools as Expert Surrogates for LLM-Based GPU Kernel Optimization

Jiading Gai, Shuai Zhang, Kaj Bostrom, Jin Huang, Vihang Patil, Haoyang Fang, Bernie Wang, Huzefa Rangwala, George Karypis

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Tags: Code generation, Computer science, CUDA, LLM, nVidia, nVidia A100, Triton

June 28, 2026 by hgpu

The Correctness Illusion in LLM-Generated GPU Kernels

Dipankar Sarkar

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Tags: Benchmarking, Code generation, Computer science, CUDA, LLM, nVidia, nVidia A10, nVidia A100, nVidia GeForce RTX 3060, nVidia H100, nVidia L40s, Triton

June 28, 2026 by hgpu

CUDABeaver: Benchmarking LLM-Based Automated CUDA Debugging

Shiyang Li, Haoyang Chen, Mattia Fazzini, Caiwen Ding

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Tags: Benchmarking, Computer science, CUDA, LLM, Machine learning, nVidia, nVidia A100, nVidia H200, nVidia RTX PRO 6000

May 20, 2026 by hgpu

CuBridge: An LLM-Based Framework for Understanding and Reconstructing High-Performance Attention Kernels

Xing Ma, Yangjie Zhou, Wu Sun, Zihan Liu, Jingwen Leng, Yun Lin, Shixuan Sun, Minyi Guo, Jin Song Dong

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Tags: Computer science, CUDA, Deep learning, LLM, nVidia, nVidia A100, nVidia H100

May 11, 2026 by hgpu

FACT: Compositional Kernel Synthesis with a Three-Stage Agentic Workflow

Sina Heidari, Dimitrios S. Nikolopoulos

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Tags: Computer science, CUBLAS, CUDA, Deep learning, nVidia, nVidia A100

May 3, 2026 by hgpu

MobileKernelBench: Can LLMs Write Efficient Kernels for Mobile Devices?

Xingze Zou, Jing Wang, Yuhua Zheng, Xueyi Chen, Haolei Bai, Lingcheng Kong, Syed A.R. Abu-Bakar, Zhaode Wang, Chengfei Lv, Haoji Hu, Huan Wang

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Tags: Benchmarking, Code generation, Computer science, CUDA, LLM, nVidia, nVidia A100

March 22, 2026 by hgpu

An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU

Ruijia Yang, Zeyi Wen

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Tags: AMD, AMD Radeon RX 7900 XT, Computer science, Heterogeneous systems, nVidia, nVidia A100, nVidia GeForce RTX 4090, Triton

March 18, 2026 by hgpu

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

Qitong Sun, Jun Han, Tianlin Li, Zhe Tang, Sheng Chen, Fei Yang, Aishan Liu, Xianglong Liu, Yang Liu

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Tags: Computer science, CUDA, LLM, nVidia, nVidia A100, Package, Performance, PyTorch

March 15, 2026 by hgpu

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high performance computing on graphics processing units: hgpu.org

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

Stencil Computation at the Intersection of AI and HPC

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

Optimizing CUDA like a Human: Micro-Profiling Tools as Expert Surrogates for LLM-Based GPU Kernel Optimization

The Correctness Illusion in LLM-Generated GPU Kernels

CUDABeaver: Benchmarking LLM-Based Automated CUDA Debugging

CuBridge: An LLM-Based Framework for Understanding and Reconstructing High-Performance Attention Kernels

FACT: Compositional Kernel Synthesis with a Three-Stage Agentic Workflow

MobileKernelBench: Can LLMs Write Efficient Kernels for Mobile Devices?

An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

Recent source codes

Introduction to Computer Vision

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

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Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

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