high performance computing on graphics processing units: hgpu.org

Programming

A Nearest Neighbor Data Structure for Graphics Hardware

Lawrence Cayton

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Tags: Algorithms, Computer science, CUDA, Data Structures and Algorithms, Databases, Nearest neighbour, nVidia, Tesla C2050

January 11, 2012 by hgpu

Petaflop biofluidics simulations on a two million-core system

Massimo Bernaschi, Satoshi Matsuoka, Mauro Bisson, Massimiliano Fatica, Toshio Endo, Simone Melchionna

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Tags: Algorithms, Biology, CUDA, GPU cluster, Medicine, nVidia, Optimization, Tesla M2050

January 11, 2012 by hgpu

Power-performance comparison of single-task driven many-cores

Fuat Keceli, Tali Moreshet, Uzi Vishkin

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 280

January 11, 2012 by hgpu

High Precision Integer Multiplication with a GPU Using Strassen’s Algorithm with Multiple FFT Sizes

Niall Emmart, Charles C. Weems

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Tags: Algorithms, Computer science, CUDA, FFT, nVidia, nVidia GeForce GTX 295, nVidia GeForce GTX 480, Optimization

January 11, 2012 by hgpu

People detection method using graphics processing units for a mobile robot with an omnidirectional camera

Sungil Kang, Annah Roh, Bodam Nam, Hyunki Hong

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Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 460, Optical flow

January 10, 2012 by hgpu

Graphics Processor Unit (GPU) Acceleration of Finite-Difference Frequency-Domain (FDFD) Method

V. Demir

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Tags: Algorithms, CUDA, Electrodynamics, FDTD, Finite-difference time-domain, nVidia, Tesla C1060

January 10, 2012 by hgpu

Parallelizing Kernel Polynomial Method Applying Graphics Processing Units

Shixun Zhang, Shinichi Yamagiwa, Masahiko Okumura, Seiji Yunoki

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Tags: Algorithms, Chemistry, Condensed matter, CUDA, nVidia, Physics, Tesla C2050

January 10, 2012 by hgpu

Acceleration of AES encryption on CUDA GPU

Keisuke Iwai, Naoki Nishikawa, Takakazu Kurokawa

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Tags: AES, Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Security

January 10, 2012 by hgpu

Top-k Queries Processing With Uncertain Data on Graphics Processing Units

Haozhe Chang, Tingting Qin, Xiaoguang Liu, Gang Wang, Airu Yin

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 480

January 9, 2012 by hgpu

Designing Numerical Solvers for Next Generation High Performance Computing

Mark James Mawson

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Tags: Algorithms, CUDA, Fluid dynamics, Lattice Boltzmann model, nVidia, nVidia Quadro FX 6000, PC cluster, Review, Thesis

January 9, 2012 by hgpu

LU Factorization for Accelerator-based Systems

Emmanuel Agullo, Cedric Augonnet, Jack Dongarra, Mathieu Faverge, Julien Langou, Hatem Ltaief, Stanimire Tomov

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Tags: Algorithms, Computer science, CUDA, Factorization, Linear Algebra, nVidia, Tesla S1070, Tesla S2050

January 9, 2012 by hgpu

Neural Network Simulation: The recognition application

Panagiotis Afxentis, Alicia Sanchez Crespo, Ying Zhang

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Tags: Algorithms, CNN, Computer science, CUDA, Image recognition, Neural networks, nVidia, nVidia GeForce GTX 570

January 9, 2012 by hgpu

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

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

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

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

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

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

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

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

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

An MLIR pipeline for offloading Fortran to FPGAs via OpenMP

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Programming

A Nearest Neighbor Data Structure for Graphics Hardware

Petaflop biofluidics simulations on a two million-core system

Power-performance comparison of single-task driven many-cores

High Precision Integer Multiplication with a GPU Using Strassen’s Algorithm with Multiple FFT Sizes

People detection method using graphics processing units for a mobile robot with an omnidirectional camera

Graphics Processor Unit (GPU) Acceleration of Finite-Difference Frequency-Domain (FDFD) Method

Parallelizing Kernel Polynomial Method Applying Graphics Processing Units

Acceleration of AES encryption on CUDA GPU

Top-k Queries Processing With Uncertain Data on Graphics Processing Units

Designing Numerical Solvers for Next Generation High Performance Computing

LU Factorization for Accelerator-based Systems

Neural Network Simulation: The recognition application

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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