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ActTune: Action-Aware Precision and GPU Operating-Point Adaptation for Energy-Efficient Vision-Language-Action Inference

Zou Qingyun, Bin Gao, Wenju Zhao, Weng-Fai Wong, Bingsheng He, Tulika Mitra

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Tags: Computer science, Computer vision, LLM, nVidia, nVidia RTX A6000, Robotics

October 14, 2026 by hgpu

KernelFoundry: Hardware-aware evolutionary GPU kernel optimization

Nina Wiedemann, Quentin Leboutet, Michael Paulitsch, Diana Wofk, Benjamin Ummenhofer

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Tags: Code generation, Computer science, CUDA, LLM, nVidia, nVidia RTX A6000, SYCL

March 18, 2026 by hgpu

Catalyst-Agent: Autonomous heterogeneous catalyst screening and optimization with an LLM Agent

Achuth Chandrasekhar, Janghoon Ock, Amir Barati Farimani

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Tags: Chemistry, Computer science, LLM, nVidia, nVidia RTX A6000

March 8, 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

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

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Yifan Zhao, Egan Johnson, Prasanth Chatarasi, Vikram Adve, Sasa Misailovic

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Tags: AMD Radeon Instinct MI300X, ATI, Computer science, CUDA, Deep learning, nVidia, nVidia A100, nVidia RTX A5000, nVidia RTX A6000, Package, Performance, ROCm

October 19, 2025 by hgpu

Fuzz4cuda: Fuzzing Your Nvidia Gpu Libraries Through Debug Interface

Yuhao Zhou, Peng Jia, Jiayong Liu, Ximing Fan

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Tags: Computer science, CUDA, nVidia, nVidia RTX A6000, PTX, Security

August 24, 2025 by hgpu

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Chris S. Lin, Joyce Qu, Gururaj Saileshwar

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia RTX A6000, Package, Security

August 17, 2025 by hgpu

AGFT: An Adaptive GPU Frequency Tuner for Real-Time LLM Inference Optimization

Zicong Ye, Kunming Zhang, Guoming Tang

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Tags: Computer science, Energy-efficient computing, LLM, nVidia, nVidia A800, nVidia RTX A6000

August 10, 2025 by hgpu

LiteGD: Lightweight and dynamic GPU Dispatching for Large-scale Heterogeneous Clusters

Kunming Zhang, Hanlong Liao, Guoming Tang

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Tags: Computer science, GPU cluster, Heterogeneous systems, Machine learning, nVidia, nVidia A800, nVidia GeForce RTX 4090, nVidia H100, nVidia RTX A6000, nVidia V100

June 22, 2025 by hgpu

All You Need Is Binary Search! A Practical View on Lightweight Database Indexing on GPUs

Justus Henneberg, Felix Schuhknecht

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Tags: Computer science, CUDA, Databases, nVidia, nVidia RTX A6000, Performance

June 8, 2025 by hgpu

PyGraph: Robust Compiler Support for CUDA Graphs in PyTorch

Abhishek Ghosh, Ajay Nayak, Ashish Panwar, Arkaprava Basu

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Tags: Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia RTX A6000, PyTorch

March 30, 2025 by hgpu

ATTUNE: Agentic Throughput Tuning for LLM Training

HPCAgent-Bench: A Framework for Sound Benchmarking of Code-Optimizing Agents, Harnesses, and Skills

Brian2 Atlas

A Unified Intermediate Representation and Execution Architecture for Heterogeneous and Distributed Neural Simulation

AgentGarten: Code Worlds for Evolving Agents

KernelLens: Automated Triton-to-C++ Compiler for Enterprise Inference Runtimes

KernelLens: Deploying Triton Kernels to C++ Inference Engines

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

Automatically Generating ML Compiler Backends from Tensor Accelerator ISA Descriptions

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

pytest-gpu-proof: Enabling Cloud-CPU Continuous Integration for GPU Code with Local GPU Attestation

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

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high performance computing on graphics processing units: hgpu.org

ActTune: Action-Aware Precision and GPU Operating-Point Adaptation for Energy-Efficient Vision-Language-Action Inference

KernelFoundry: Hardware-aware evolutionary GPU kernel optimization

Catalyst-Agent: Autonomous heterogeneous catalyst screening and optimization with an LLM Agent

KernelBlaster: Continual Cross-Task CUDA Optimization via Memory-Augmented In-Context Reinforcement Learning

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

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Fuzz4cuda: Fuzzing Your Nvidia Gpu Libraries Through Debug Interface

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

AGFT: An Adaptive GPU Frequency Tuner for Real-Time LLM Inference Optimization

LiteGD: Lightweight and dynamic GPU Dispatching for Large-scale Heterogeneous Clusters

All You Need Is Binary Search! A Practical View on Lightweight Database Indexing on GPUs

PyGraph: Robust Compiler Support for CUDA Graphs in PyTorch

Recent source codes

ATTUNE: Agentic Throughput Tuning for LLM Training

HPCAgent-Bench: A Framework for Sound Benchmarking of Code-Optimizing Agents, Harnesses, and Skills

Brian2 Atlas

AgentGarten: Code Worlds for Evolving Agents

KernelLens: Automated Triton-to-C++ Compiler for Enterprise Inference Runtimes

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

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