high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications

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

Weinan Dai, Hanlin Wu, Qiying Yu, Huan-ang Gao, Jiahao Li, Chengquan Jiang, Weiqiang Lou, Yufan Song, Hongli Yu, Jiaze Chen, Wei-Ying Ma, Ya-Qin Zhang, Jingjing Liu, Mingxuan Wang, Xin Liu, Hao Zhou

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Tags: Code generation, Computer science, CUDA, Deep learning, nVidia, nVidia H20, Package

March 4, 2026 by hgpu

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

Shiyang Li, Zijian Zhang, Winson Chen, Yuebo Luo, Mingyi Hong, Caiwen Ding

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Tags: Benchmarking, Code generation, Computer science, CUBLAS, CUDA, LLM, nVidia, nVidia H200, nVidia RTX PRO 6000

March 4, 2026 by hgpu

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

Jiace Zhu, Wentao Chen, Qi Fan, Zhixing Ren, Junying Wu, Xing Zhe Chai, Chotiwit Rungrueangwutthinon, Yehan Ma, An Zou

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Tags: Benchmarking, Computer science, CUDA, LLM, nVidia, nVidia A40, nVidia GeForce RTX 4090, Package

March 4, 2026 by hgpu

Joint Training on AMD and NVIDIA GPUs

Jon Hu, Thomas Jia, Jing Zhu, Zhendong Yu

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Tags: AMD, AMD Radeon Instinct MI325X, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia H200, ROCm

March 1, 2026 by hgpu

A Survey of Recent Developments in SYCL Compiler Implementations

Huy Trinh

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Tags: Compilers, Computer science, Heterogeneous systems, SYCL

March 1, 2026 by hgpu

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

Linus Bantel, Moritz Strack, Alexander Strack, Dirk Pflüger

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Tags: Code generation, Computer science, LLM, OpenMP

March 1, 2026 by hgpu

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

Haichuan Hu, Ye Shang, Guoqing Xie, Congqing He, Quanjun Zhang

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Tags: Code generation, Computer science, Heterogeneous systems, LLM, Package, Software Engineering

March 1, 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

HPC++: An LLVM-Based Automatic Parallelization Framework with Heterogeneous CPU–GPU Execution

Vahram Martirosyan

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Tags: Computer science, HPC, LLVM, OpenCL

February 23, 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

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

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

Applications

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

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

Joint Training on AMD and NVIDIA GPUs

A Survey of Recent Developments in SYCL Compiler Implementations

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

HPC++: An LLVM-Based Automatic Parallelization Framework with Heterogeneous CPU–GPU Execution

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

Recent source codes

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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