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cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

Jinwu Chen, Qidie Wu, Bin Li, Lin Ma, Xin Si, Yang Hu, Shouyi Yin, Jun Yang

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Tags: AI, Code generation, Computer science, CUDA, nVidia, nVidia A100, Package, PyTorch

December 21, 2025 by hgpu

PystachIO: Efficient Distributed GPU Query Processing with PyTorch over Fast Networks & Fast Storage

Jigao Luo, Nils Boeschen, Muhammad El-Hindi, Carsten Binnig

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Tags: Computer science, CUDA, Databases, GPU cluster, nVidia, nVidia A100, nVidia DGX-1, PyTorch

December 21, 2025 by hgpu

Accelerating Molecular Simulations with Triton: Fused GPU Kernels for TensorNet Neural Potentials

Manpreet Singh

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Tags: Biology, Chemistry, CUDA, Molecular dynamics, nVidia, nVidia A100, PyTorch, Triton

December 15, 2025 by hgpu

QiMeng-Kernel: Macro-Thinking Micro-Coding Paradigm for LLM-Based High-Performance GPU Kernel Generation

Xinguo Zhu, Shaohui Peng, Jiaming Guo, Yunji Chen, Qi Guo, Yuanbo Wen, Hang Qin, Ruizhi Chen, Qirui Zhou, Ke Gao, Yanjun Wu, Chen Zhao, Ling Li

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

November 30, 2025 by hgpu

KernelBand: Boosting LLM-based Kernel Optimization with a Hierarchical and Hardware-aware Multi-armed Bandit

Dezhi Ran, Shuxiao Xie, Mingfang Ji, Ziyue Hua, Mengzhou Wu, Yuan Cao, Yuzhe Guo, Yu Hao, Linyi Li, Yitao Hu, Tao Xie

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Tags: Code generation, Computer science, CUDA, LLM, nVidia, nVidia A100, nVidia GeForce RTX 4090, Triton

November 30, 2025 by hgpu

PRAGMA: A Profiling-Reasoned Multi-Agent Framework for Automatic Kernel Optimization

Kelun Lei, Hailong Yang, Huaitao Zhang, Xin You, Kaige Zhang, Zhongzhi Luan, Yi Liu, Depei Qian

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

November 16, 2025 by hgpu

MT4G: A Tool for Reliable Auto-Discovery of NVIDIA and AMD GPU Compute and Memory Topologies

Stepan Vanecek, Manuel Walter Mussbacher, Dominik Groessler, Urvij Saroliya, Martin Schulz

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Tags: AMD Radeon Instinct MI100, AMD Radeon Instinct MI210, AMD Radeon Instinct MI300X, ATI, Benchmarking, Computer science, CUDA, HIP, nVidia, nVidia A100, nVidia GeForce RTX 2080, nVidia H100, nVidia Quadro P 6000, nVidia V100, Package, PTX

November 16, 2025 by hgpu

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

Zijian Zhang, Rong Wang, Shiyang Li, Yuebo Luo, Mingyi Hong, Caiwen Ding

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce RTX 3090, nVidia GeForce RTX 4090, nVidia RTX 6000 Ada, Package, Performance

November 9, 2025 by hgpu

Enhancing Transformer Performance and Portability through Auto-tuning Frameworks

Patricia Siwinska,Jie Lei,Adrian Castello,Pedro Alonso-Jord́a,Enrique S. Quintana-Orti

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Tags: Auto-Tuning, Computer science, CUDA, Deep learning, Heterogeneous systems, LLM, nVidia, nVidia A100, Package, performance portability

November 2, 2025 by hgpu

Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

Leonardo Solis-Vasquez, Andreas F. Tillack, Diogo Santos-Martins, Andreas Koch, Stefano Forli

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Tags: Chemistry, Computer science, CUDA, Deep learning, Matrix multiplication, nVidia, nVidia A100, nVidia B200, nVidia H100, Package

October 26, 2025 by hgpu

STARK: Strategic Team of Agents for Refining Kernels

Juncheng Dong, Yang Yang, Tao Liu, Yang Wang, Feng Qi, Vahid Tarokh, Kaushik Rangadurai, Shuang Yang

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

October 26, 2025 by hgpu

Tutoring LLM into a Better CUDA Optimizer

Matyáš Brabec, Jiří Klepl, Michal Töpfer, Martin Kruliš

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Tags: Code generation, Computer science, CUDA, LLM, nVidia, nVidia A100, nVidia H100, nVidia V100, Package

October 26, 2025 by hgpu

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

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

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Generating Literature-Driven Scientific Theories at Scale

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high performance computing on graphics processing units: hgpu.org

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

PystachIO: Efficient Distributed GPU Query Processing with PyTorch over Fast Networks & Fast Storage

Accelerating Molecular Simulations with Triton: Fused GPU Kernels for TensorNet Neural Potentials

QiMeng-Kernel: Macro-Thinking Micro-Coding Paradigm for LLM-Based High-Performance GPU Kernel Generation

KernelBand: Boosting LLM-based Kernel Optimization with a Hierarchical and Hardware-aware Multi-armed Bandit

PRAGMA: A Profiling-Reasoned Multi-Agent Framework for Automatic Kernel Optimization

MT4G: A Tool for Reliable Auto-Discovery of NVIDIA and AMD GPU Compute and Memory Topologies

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

Enhancing Transformer Performance and Portability through Auto-tuning Frameworks

Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

STARK: Strategic Team of Agents for Refining Kernels

Tutoring LLM into a Better CUDA Optimizer

Recent source codes

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

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

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Vortex-Optimized Light-weight Toolchain (VOLT)

SciDef: Automated Definition Extraction from Scientific Literature

bioagent-bench: Benchmark for evaluating LLM agents in bioinformatics

Benchmark suite for LLM inference on NVIDIA consumer GPUs

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

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