high performance computing on graphics processing units: hgpu.org

Xingjun Ma, Yixu Wang, Hengyuan Xu, Yutao Wu, Yifan Ding, Yunhan Zhao, Zilong Wang, Jiabin Hua, Ming Wen, Jianan Liu, Ranjie Duan, Yifeng Gao, Yingshui Tan, Yunhao Chen, Hui Xue, Xin Wang, Wei Cheng, Jingjing Chen, Zuxuan Wu, Bo Li, Yu-Gang Jiang

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Tags: Computer science, LLM, Package, Security

February 23, 2026 by hgpu

Improving Code Generation via Small Language Model-as-a-judge

Giuseppe Crupi, Rosalia Tufano, Gabriele Bavota

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Tags: Code generation, Computer science, LLM, nVidia, nVidia GeForce RTX 3090

February 16, 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

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

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

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high performance computing on graphics processing units: hgpu.org

StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

From Prompts to Performance: Evaluating LLMs for Task-based Parallel Code Generation

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

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

Fine-Tuning GPT-5 for GPU Kernel Generation

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

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

Improving Code Generation via Small Language Model-as-a-judge

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

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

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CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

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

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