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PTXBench: Benchmark and Adapt LLMs for GPU Kernel Optimization with Architecture-specific PTX

Genghan Zhang, Yixin Dong, Chengze Fan, Zhichen Zeng, Yueming Yuan, Shaowei Zhu, Kunle Olukotun

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Tags: Benchmarking, Computer science, LLM, nVidia, nVidia B200, nVidia H100, Package, PTX

August 30, 2026 by hgpu

Validation-Centric AI-Assisted GPU Porting of a 250,000+ Line Legacy Weather Simulation Code

Tetsuya Hoshino, Masaya Kato, Kazuhisa Tsuboki, Daichi Mukunoki, Takahiro Katagiri, Toshihiro Hanawa

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Tags: AI, Code generation, Computer science, Fortran, MPI, nVidia, nVidia GH200, nVidia H100, OpenACC, OpenMP

August 17, 2026 by hgpu

Enhancing the Performance Analysis of NCCL GPU Collectives

Jurij Cerar

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Tags: Computer science, CUDA, nVidia, nVidia H100, Performance, Tesla T4, Thesis

July 13, 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

Leveraging AI Ecosystem for Portable and Sustainable GPU Kernels in HPC

Yanbo Zhao, Zhaonan Meng, Sai Krishna Teja Varma Manthena, Xu Liu, Ajay Panyala, Jiajia Li

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Tags: AMD, AMD Radeon Instinct MI300X, Computer science, DSL, HPC, nVidia, nVidia GeForce RTX 4090, nVidia H100, PTX, Python, ROCm, Triton

June 17, 2026 by hgpu

KEET: Explaining Performance of GPU Kernels Using LLM Agents

Joshua H. Davis, Klaudiusz Rydzy, Srinivasan Ramesh, Aadit Nilay, Daniel Nichols, Swapna Raj, Nikhil Jain, Abhinav Bhatele

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Tags: Computer science, CUDA, LLM, nVidia, nVidia H100, Performance

May 11, 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

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

Divakar Kumar Yadav, Tian Zhao, Deepak Kumar

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Tags: Computer science, CUBLAS, CUDA, LLM, nVidia, nVidia B200, nVidia H100, nVidia RTX PRO 6000, Package, Performance, Triton

May 3, 2026 by hgpu

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

Jaber Jaber, Osama Jaber

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia B200, nVidia H100, Package, Triton

March 26, 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

Fine-Tuning GPT-5 for GPU Kernel Generation

Ali Tehrani, Yahya Emara, Essam Wissam, Wojciech Paluch, Waleed Atallah, Łukasz Dudziak, Mohamed S. Abdelfattah

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Tags: Code generation, Computer science, CUDA, LLM, nVidia, nVidia H100, Triton

February 23, 2026 by hgpu

Improving HPC Code Generation Capability of LLMs via Online Reinforcement Learning with Real-Machine Benchmark Rewards

Ryo Mikasa, Shun-ichiro Hayashi, Daichi Mukunoki, Tetsuya Hoshino, Takahiro Katagiri

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Tags: Benchmarking, Code generation, Computer science, CUDA, HPC, LLM, Matrix multiplication, nVidia, nVidia H100, OpenMP, Performance

February 16, 2026 by hgpu

high performance computing on graphics processing units: hgpu.org

PTXBench: Benchmark and Adapt LLMs for GPU Kernel Optimization with Architecture-specific PTX

Validation-Centric AI-Assisted GPU Porting of a 250,000+ Line Legacy Weather Simulation Code

Enhancing the Performance Analysis of NCCL GPU Collectives

The Correctness Illusion in LLM-Generated GPU Kernels

Leveraging AI Ecosystem for Portable and Sustainable GPU Kernels in HPC

KEET: Explaining Performance of GPU Kernels Using LLM Agents

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

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

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

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

Fine-Tuning GPT-5 for GPU Kernel Generation

Improving HPC Code Generation Capability of LLMs via Online Reinforcement Learning with Real-Machine Benchmark Rewards

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