high performance computing on graphics processing units: hgpu.org

Khaled Hamidouche, John Bachan, Pak Markthub, Peter-Jan Gootzen, Elena Agostini, Sylvain Jeaugey, Aamir Shafi, Georgios Theodorakis, Manjunath Gorentla Venkata

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Tags: Computer science, CUDA, Hardware Architecture, Machine learning, nVidia, nVidia H100

November 30, 2025 by hgpu

KernelBand: Boosting LLM-based Kernel Optimization with a Hierarchical and Hardware-aware Multi-armed Bandit

Dezhi Ran, Shuxiao Xie, Mingfang Ji, Ziyue Hua, Mengzhou Wu, Yuan Cao, Yuzhe Guo, Yu Hao, Linyi Li, Yitao Hu, Tao Xie

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Tags: Code generation, Computer science, CUDA, LLM, nVidia, nVidia A100, nVidia GeForce RTX 4090, Triton

November 30, 2025 by hgpu

AIvailable: A Software-Defined Architecture for LLM-as-a-Service on Heterogeneous and Legacy GPUs

Pedro Antunes, Ana Rita Ortigoso, Gabriel Vieira, Daniel Fuentes, Luís Frazão, Nuno Costa, António Pereira

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Tags: AMD Radeon Pro W6600, ATI, Computer science, CUDA, Heterogeneous systems, LLM, nVidia, nVidia GeForce GTX 1660, nVidia GeForce RTX 3070, ROCm

November 23, 2025 by hgpu

ProofWright: Towards Agentic Formal Verification of CUDA

Bodhisatwa Chatterjee, Drew Zagieboylo, Sana Damani, Siva Hari, Christos Kozyrakis

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

November 23, 2025 by hgpu

Iris: First-Class Multi-GPU Programming Experience in Triton

Muhammad Awad, Muhammad Osama, Brandon Potter

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Source codes

Tags: AMD Radeon Instinct MI300X, ATI, Benchmarking, Computer science, CUDA, HIP, nVidia, Package, Python, Triton

November 23, 2025 by hgpu

The Anatomy of a Triton Attention Kernel

Burkhard Ringlein, Jan van Lunteren, Radu Stoica, Thomas Parnell

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Tags: AMD Radeon Instinct MI250, AMD Radeon Instinct MI300X, ATI, Computer science, CUDA, DSL, HIP, LLM, nVidia, nVidia H100, Performance, Programming Languages, Triton

November 23, 2025 by hgpu

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high performance computing on graphics processing units: hgpu.org

Programming

Accurate Models of NVIDIA Tensor Cores

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

Accelerating Molecular Simulations with Triton: Fused GPU Kernels for TensorNet Neural Potentials

Microbenchmarking NVIDIA’s Blackwell Architecture: An in-depth Architectural Analysis

QiMeng-Kernel: Macro-Thinking Micro-Coding Paradigm for LLM-Based High-Performance GPU Kernel Generation

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

GPU-Initiated Networking for NCCL

KernelBand: Boosting LLM-based Kernel Optimization with a Hierarchical and Hardware-aware Multi-armed Bandit

AIvailable: A Software-Defined Architecture for LLM-as-a-Service on Heterogeneous and Legacy GPUs

ProofWright: Towards Agentic Formal Verification of CUDA

Iris: First-Class Multi-GPU Programming Experience in Triton

The Anatomy of a Triton Attention Kernel

Recent source codes

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

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