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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

Equivalence Checking of ML GPU Kernels

Kshitij Dubey, Benjamin Driscoll, Anjiang Wei, Neeraj Kayal, Rahul Sharma, Alex Aiken

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Tags: Computer science, CUDA, Deep learning, LLM, Machine learning, Matrix multiplication, nVidia, PTX

January 12, 2026 by hgpu

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

Songqiao Su, Xiaofei Sun, Xiaoya Li, Albert Wang, Jiwei Li, Chris Shum

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Tags: Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, nVidia A100, Package

December 21, 2025 by hgpu

Accurate Models of NVIDIA Tensor Cores

Faizan A. Khattak, Mantas Mikaitis

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia B200, nVidia H100, nVidia V100, Package

December 15, 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

High Performance Matrix Multiplication

Ethan Davis

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Tags: BLAS, Computer science, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, nVidia, OpenMP, Package, Performance, Python, Tesla V100

September 14, 2025 by hgpu

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

Daichi Mukunoki

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Tags: Computer science, Matrix multiplication, nVidia, nVidia GeForce RTX 5060 Ti, Package, Performance

August 10, 2025 by hgpu

Libra: Synergizing CUDA and Tensor Cores for High-Performance Sparse Matrix Multiplication

Jinliang Shi, Shigang Li, Youxuan Xu, Xueying Wang, Rongtian Fu, Zhi Ma, Tong Wu

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce RTX 4090, nVidia H100, Package, Sparse matrix

July 6, 2025 by hgpu

A Novel Compiler Transformation for Fast Sparse Matrix Multiplication in GPUs

Hossein Albakri, Kazem Cheshmi

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Tags: Compilers, Computer science, CUBLAS, CUDA, Machine learning, Matrix multiplication, Neural networks, nVidia, nVidia A100, Programming Languages, Sparse matrix

June 22, 2025 by hgpu

WgPy: GPU-accelerated NumPy-like array library for web browsers

Masatoshi Hidaka, Tatsuya Harada

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce RTX 4070, Python

March 10, 2025 by hgpu

On the Partitioning of GPU Power among Multi-Instances

Tirth Vamja, Kaustabha Ray, Felix George, UmaMaheswari C Devi

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Tags: Computer science, CUDA, Energy-efficient computing, Machine learning, Matrix multiplication, nVidia, nVidia A100, nVidia V100, Performance

February 3, 2025 by hgpu

Utilizing Tensor Cores in Futhark

Kristoffer August Kortbæk, Rune Ejnar Bang Lejbølle

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, High-level Languages, Matrix multiplication, nVidia, nVidia A100, Package, PTX

December 24, 2024 by hgpu

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

Nsight Python: a Python kernel profiling interface based on NVIDIA Nsight Tools

Nsight Python: A Python-First Profiling Toolkit for Seamless GPU Kernel Analysis (Tool)

Awesome LLM-Driven Kernel Generation

Towards Automated Kernel Generation in the Era of LLMs

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high performance computing on graphics processing units: hgpu.org

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

Equivalence Checking of ML GPU Kernels

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

Accurate Models of NVIDIA Tensor Cores

Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

High Performance Matrix Multiplication

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

Libra: Synergizing CUDA and Tensor Cores for High-Performance Sparse Matrix Multiplication

A Novel Compiler Transformation for Fast Sparse Matrix Multiplication in GPUs

WgPy: GPU-accelerated NumPy-like array library for web browsers

On the Partitioning of GPU Power among Multi-Instances

Utilizing Tensor Cores in Futhark

Recent source codes

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

Nsight Python: a Python kernel profiling interface based on NVIDIA Nsight Tools

Awesome LLM-Driven Kernel Generation

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