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A Two-Stage GPU Kernel Tuner Combining Semantic Refactoring and Search-Based Optimization

Qiuyi Qu, Yicheng Sui, Yufei Sun, Rui Chen, Xiaofei Zhang, Yuzhi Zhang, Haofeng Wang, Ge Lan, Ning Zhang

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Tags: Computation and Language, Computer science, CUDA, LLM, nVidia, nVidia Titan RTX, OpenCL

January 25, 2026 by hgpu

Towards Automated Kernel Generation in the Era of LLMs

Yang Yu, Peiyu Zang, Chi Hsu Tsai, Haiming Wu, Yixin Shen, Jialing Zhang, Haoyu Wang, Zhiyou Xiao, Jingze Shi, Yuyu Luo, Wentao Zhang, Chunlei Men, Guang Liu, Yonghua Lin

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Tags: AMD, Benchmarking, Computer science, CUDA, LLM, nVidia, ROCm, Triton

January 25, 2026 by hgpu

Sawtooth Wavefront Reordering: Enhanced CuTile FlashAttention on NVIDIA GB10

Yifan Zhu, Yekai Pan, Chen Ding

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Tags: Computer science, CUDA, nVidia, nVidia GB10, Programming techniques

January 25, 2026 by hgpu

AKG kernel Agent: A Multi-Agent Framework for Cross-Platform Kernel Synthesis

Jinye Du, Quan Yuan, Zuyao Zhang, Yanzhi Yi, Jiahui Hu, Wangyi Chen, Yiyang Zhu, Qishui Zheng, Wenxiang Zou, Xiangyu Chang, Zuohe Zheng, Zichun Ye, Chao Liu, Shanni Li, Renwei Zhang, Yiping Deng, Xinwei Hu, Xuefeng Jin, Jie Zhao

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Tags: Code generation, Computer science, CUDA, DSL, LLM, nVidia, nVidia A100, Triton

January 12, 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

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

Erel Kaplan, Tomer Bitan, Lian Ghrayeb, Le Chen, Tom Yotam, Niranjan Hasabnis, Gal Oren

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Tags: Code generation, Computer science, CUDA, LLM, nVidia, nVidia GeForce RTX 4060, OpenMP, Package

January 12, 2026 by hgpu

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

Gang Liao, Hongsen Qin, Ying Wang, Alicia Golden, Michael Kuchnik, Yavuz Yetim, Jia Jiunn Ang, Chunli Fu, Yihan He, Samuel Hsia, Zewei Jiang, Dianshi Li, Uladzimir Pashkevich, Varna Puvvada, Feng Shi, Matt Steiner, Ruichao Xiao, Nathan Yan, Xiayu Yu, Zhou Fang, Abdul Zainul-Abedin, Ketan Singh, Hongtao Yu, Wenyuan Chi, Barney Huang, Sean Zhang, Noah Weller, Zach Marine, Wyatt Cook, Carole-Jean Wu, Gaoxiang Liu

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Tags: AI, AMD Radeon Instinct MI300X, AMD Radeon Instinct MI350X, ATI, Computer science, CUDA, Deep learning, Heterogeneous systems, LLM, nVidia, nVidia A100, nVidia H100, PTX, ROCm, Triton

January 4, 2026 by hgpu

GPU Kernel Optimization Beyond Full Builds: An LLM Framework with Minimal Executable Programs

Ruifan Chu, Anbang Wang, Xiuxiu Bai, Shuai Liu, and Xiaoshe Dong

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Tags: Computer science, CUDA, HIP, HPC, LLM, nVidia, nVidia GeForce RTX 2080 Ti, Performance

January 4, 2026 by hgpu

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Yaoyao Ding, Bohan Hou, Xiao Zhang, Allan Lin, Tianqi Chen, Cody Hao Yu, Yida Wang, Gennady Pekhimenko

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Tags: Computer science, CUDA, nVidia, nVidia A100, nVidia H100, nVidia L40s, Package, Programming Languages, PTX, Triton

December 29, 2025 by hgpu

Optimal Software Pipelining and Warp Specialization for Tensor Core GPUs

Rupanshu Soi, Rohan Yadav, Fredrik Kjolstad, Alex Aiken, Maryam Mehri Dehnavi, Michael Garland, Michael Bauer

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Tags: Computer science, CUDA, nVidia, nVidia B200, nVidia H100, Programming Languages

December 29, 2025 by hgpu

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

Pierre Abillama, Changwoo Lee, Juechu Dong, David Blaauw, Dennis Sylvester, Hun-Seok Kim

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Tags: AI, Computer science, CUDA, Machine learning, Memory, nVidia, nVidia A40, nVidia Jetson Orin Nano, Package

December 29, 2025 by hgpu

PEAK: A Performance Engineering AI-Assistant for GPU Kernels Powered by Natural Language Transformations

Muhammad Usman Tariq, Abhinav Jangda, Angelica Moreira, Madan Musuvathi, Tyler Sorensen

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Tags: AI, AMD, AMD Radeon Instinct MI200, ATI, Computer science, CUDA, HIP, HLSL, LLM, NLP, nVidia, nVidia RTX A6000

December 29, 2025 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

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

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

bioagent-bench: Benchmark for evaluating LLM agents in bioinformatics

BioAgent Bench: An AI Agent Evaluation Suite for Bioinformatics

Benchmark suite for LLM inference on NVIDIA consumer GPUs

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

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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