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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

Generative Video Compression: Towards 0.01% Compression Rate for Video Transmission

Xiangyu Chen, Jixiang Luo, Jingyu Xu, Fangqiu Yi, Chi Zhang, Xuelong Li

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Tags: AI, Compression, Computer science

January 4, 2026 by hgpu

AccelOpt: A Self-Improving LLM Agentic System for AI Accelerator Kernel Optimization

Genghan Zhang, Shaowei Zhu, Anjiang Wei, Zhenyu Song, Allen Nie, Zhen Jia, Nandita Vijaykumar, Yida Wang, Kunle Olukotun

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Tags: AI, Benchmarking, Computer science, LLM, Machine learning, Performance

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

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

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

Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translation

Le Chen1, Nuo Xu, Winson Chen, Bin Lei, Pei-Hung Lin, Dunzhi Zhou, Rajeev Thakur, Caiwen Ding, Ali Jannesari, Chunhua Liao

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Tags: Code generation, Computer science, CUDA, LLM, nVidia, nVidia H200

December 21, 2025 by hgpu

BoltzGen:Toward Universal Binder Design

Hannes Stark, Felix Faltings, MinGyu Choi, Yuxin Xie, Eunsu Hur, Timothy O’Donnell, Anton Bushuiev, Talip Uçar, Saro Passaro, Weian Mao, Mateo Reveiz, Roman Bushuiev, Tomáš Pluskal, Josef Sivic, Karsten Kreis, Arash Vahdat, Shamayeeta Ray, Jonathan T. Goldstein, Andrew Savinov, Jacob A. Hambalek, Anshika Gupta, Diego A. Taquiri-Diaz, Yaotian Zhang, A. Katherine Hatstat, Angelika Arada, Nam Hyeong Kim, Ethel Tackie-Yarboi, Dylan Boselli, Lee Schnaider, Chang C. Liu, Gene-Wei Li, Denes Hnisz, David M. Sabatini, William F. DeGrado, Jeremy Wohlwend, Gabriele Corso, Regina Barzilay, Tommi Jaakkola

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Tags: Bioinformatics, Biology, Biomolecules, nVidia, nVidia A100, Package

December 21, 2025 by hgpu

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

Songqiao Su, Xiaofei Sun, Xiaoya Li, Albert Wang, Jiwei Li, Chris Shum

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Tags: Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, nVidia A100, Package

December 21, 2025 by hgpu

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

Jinwu Chen, Qidie Wu, Bin Li, Lin Ma, Xin Si, Yang Hu, Shouyi Yin, Jun Yang

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Tags: AI, Code generation, Computer science, CUDA, nVidia, nVidia A100, Package, PyTorch

December 21, 2025 by hgpu

ML Inference Scheduling with Predictable Latency

Haidong Zhao, Nikolaos Georgantas

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Tags: Computer science, Machine learning, nVidia, nVidia L4, Task scheduling

December 21, 2025 by hgpu

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

Agentic Code Optimization via Compiler-LLM Cooperation

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

True 4-Bit Quantized CNN Training on CPU

True 4-Bit Quantized Convolutional Neural Network Training on CPU: Achieving Full-Precision Parity

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

Hunting CUDA Bugs at Scale with cuFuzz

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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