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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

Analyzing Modern NVIDIA GPU cores

Rodrigo Huerta, Mojtaba Abaie Shoushtary, José-Lorenzo Cruz, Antonio González

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Tags: Computer science, Hardware Architecture, nVidia, nVidia RTX A6000

March 30, 2025 by hgpu

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

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

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

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high performance computing on graphics processing units: hgpu.org

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

Analyzing Modern NVIDIA GPU cores

Recent source codes

Agentic Code Optimization via Compiler-LLM Cooperation

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

Device Virtual Machine (DVM)

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

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

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

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

True 4-Bit Quantized CNN Training on CPU

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

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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