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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

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

Deep Kernel Fusion for Transformers

Zixi Zhang, Zhiwen Mo, Yiren Zhao, Robert Mullins

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

February 16, 2026 by hgpu

Scaling GPU-to-CPU Migration for Efficient Distributed Execution on CPU Clusters

Ruobing Han, Hyesoon Kim

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Tags: Compilers, Computer science, CUDA, nVidia, nVidia A100, nVidia V100, Triton

February 8, 2026 by hgpu

Inside VOLT: Designing an Open-Source GPU Compiler (Tool)

Shinnung Jeong, Chihyo Ahn, Huanzhi Pu, Jisheng Zhao, Hyesoon Kim, Blaise Pascal Tine

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Tags: Compilers, Computer science, CUDA, FPGA, nVidia, OpenCL, Package

February 8, 2026 by hgpu

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

Wei Liu, Jiawei Xu, Yingru Li, Longtao Zheng, Tianjian Li, Qian Liu, Junxian He

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Tags: Computer science, CUDA, LLM, nVidia, nVidia H100, Package, Triton

February 8, 2026 by hgpu

Private LLM Inference on Consumer Blackwell GPUs: A Practical Guide for Cost-Effective Local Deployment in SMEs

Jonathan Knoop, Hendrik Holtmann

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Tags: Cloud, Computer science, CUDA, LLM, nVidia, nVidia GeForce RTX 5060 Ti, nVidia GeForce RTX 5070 Ti, nVidia GeForce RTX 5090, Package

February 2, 2026 by hgpu

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

Bastian Hagedorn, Alexander Collins, Tony Mongkolsmai, Vinod Grover

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Tags: Computer science, CUDA, nVidia, nVidia B200, Package, Performance, Profiling, Python, Triton

February 2, 2026 by hgpu

SynPerf: A Hybrid Analytical-ML Framework for GPU Performance Prediction

Kaixuan Zhang, Yunfan Cui, Shuhao Zhang, Chutong Ding, Shiyou Qian, Luping Wang, Jian Cao, Guangtao Xue, Cheng Huang, Guodong Yang, Liping Zhang

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Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, nVidia A100, nVidia A40, nVidia H100, nVidia H20, nVidia H200, nVidia H800, nVidia L20, nVidia L40, nVidia RTX 6000 Ada, Performance, Triton

January 25, 2026 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

Vortex-Optimized Light-weight Toolchain (VOLT)

Inside VOLT: Designing an Open-Source GPU Compiler (Tool)

SciDef: Automated Definition Extraction from Scientific Literature

SciDef: Automating Definition Extraction from Academic Literature with Large Language Models

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

Programming

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

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

Deep Kernel Fusion for Transformers

Scaling GPU-to-CPU Migration for Efficient Distributed Execution on CPU Clusters

Inside VOLT: Designing an Open-Source GPU Compiler (Tool)

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

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

SynPerf: A Hybrid Analytical-ML Framework for GPU Performance Prediction

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

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

bioagent-bench: Benchmark for evaluating LLM agents in bioinformatics

Benchmark suite for LLM inference on NVIDIA consumer GPUs

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

Awesome LLM-Driven Kernel Generation

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