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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 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

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

HetCCL: Accelerating LLM Training with Heterogeneous GPUs

Heehoon Kim, Jaehwan Lee, Taejeoung Kim, Jongwon Park, Jinpyo Kim, Pyongwon Suh, Ryan H. Choi, Sangwoo Lee, Jaejin Lee

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Tags: AMD, AMD FirePro W7800, Computer science, Deep learning, GPU cluster, Heterogeneous systems, LLM, nVidia, Tesla V100

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

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

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

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

See all packages

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high performance computing on graphics processing units: hgpu.org

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 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

Deep Kernel Fusion for Transformers

HetCCL: Accelerating LLM Training with Heterogeneous GPUs

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

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

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

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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