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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

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

Xiao Zhu, Xinyu Zhou, Boyu Zhu, Hanxu Hu, Mingzhe Du, Haotian Zhang, Huiming Wang, Zhijiang Guo

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Tags: Code generation, Computer science, LLM, nVidia, nVidia A100, Package

March 1, 2026 by hgpu

KernelBlaster: Continual Cross-Task CUDA Optimization via Memory-Augmented In-Context Reinforcement Learning

Kris Shengjun Dong, Sahil Modi, Dima Nikiforov, Sana Damani, Edward Lin, Siva Kumar Sastry Hari, Christos Kozyrakis

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Tags: Computer science, CUDA, LLM, nVidia, nVidia A100, nVidia H100, nVidia L40s, nVidia RTX A6000, Performance

February 23, 2026 by hgpu

OptiML: An End-to-End Framework for Program Synthesis and CUDA Kernel Optimization

Arijit Bhattacharjee, Heng Ping, Son Vu Le, Paul Bogdan, Nesreen K. Ahmed, Ali Jannesari

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

February 23, 2026 by hgpu

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

Haolei Bai, Lingcheng Kong, Xueyi Chen, Jianmian Wang, Zhiqiang Tao, Huan Wang

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

February 16, 2026 by hgpu

high performance computing on graphics processing units: hgpu.org

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

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

KernelBlaster: Continual Cross-Task CUDA Optimization via Memory-Augmented In-Context Reinforcement Learning

OptiML: An End-to-End Framework for Program Synthesis and CUDA Kernel Optimization

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

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