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Halo Gathering Scalability for Large Scale Multi-dimensional Sznajd Opinion Models Using Data Parallelism with GPUs

K.A. Hawick, D.P. Playne

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Tags: Computer science, CUDA, Data parallelism, Numerical simulation, nVidia, nVidia GeForce GTX 580, Phase transition

July 2, 2012 by hgpu

Parallel Primitives based Spatial Join of Geospatial Data on GPGPUs

Jianting Zhang

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Tags: Algorithms, Computer science, CUDA, Data parallelism, Databases, nVidia, nVidia Quadro FX 6000, Package

June 16, 2012 by hgpu

Time-dependent density-functional theory in massively parallel computer architectures: the OCTOPUS project

Xavier Andrade, Joseba Alberdi-Rodriguez, David A. Strubbe, Micael J. T. Oliveira, Fernando Nogueira, Alberto Castro, Javier Muguerza, Agustin Arruabarrena, Steven G. Louie, Alan Aspuru-Guzik, Angel Rubio, Miguel A. L. Marques

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Tags: ATI, ATI Radeon HD 5870, Data parallelism, Density-functional theory, nVidia, nVidia GeForce GTX 480, OpenCL, Optimization, Package, Physics, Tesla M2090

May 29, 2012 by hgpu

Spatial Data Structures, Sorting and GPU Parallelism for Situated-agent Simulation and Visualisation

A.V. Husselmann, K.A. Hawick

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Tags: Computer science, CUDA, Data parallelism, N-body simulation, nVidia, nVidia GeForce GTX 590, Sorting

May 20, 2012 by hgpu

Heterogeneous Computing in Economics: a Simplified Approach

Matt P. Dziubinski, Stefano Grassi

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Tags: CUDA, Data parallelism, Finance, Heterogeneous systems, nVidia, nVidia GeForce GT 520 M, nVidia GeForce GTS 450, Optimization

May 14, 2012 by hgpu

Enhancing GPU Parallelism in Nature-Inspired Algorithms

Jose M. Cecilia, Andy Nisbet, Martyn Amos, Jose M. Garcia, Manuel Ujaldon

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Tags: Algorithms, Computational modelling, Computer science, CUDA, Data parallelism, nVidia, Optimization, P systems, Tesla C1060, Tesla C2050

May 12, 2012 by hgpu

Efficient Parallelization of Natural Language Applications using GPUs

Chao-Yue Lai

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Tags: Algorithms, Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Optimization, Thesis

May 11, 2012 by hgpu

Enhancing data parallelism for Ant Colony Optimization on GPUs

Jose M. Cecilia, Jose M. Garcia, Andy Nisbet, Martyn Amos, Manuel Ujaldon

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Tags: Algorithms, Computer science, CUDA, Data parallelism, nVidia, Optimization, Tesla C2050

May 10, 2012 by hgpu

Nested Data-Parallelism on the GPU

Lars Bergstrom, John Reppy

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Tags: Algorithms, Compilers, Computer science, CUDA, Data parallelism, nVidia, Package, Programming Languages, Tesla C2050

March 31, 2012 by hgpu

Accelerating the FDTD Method Using SSE and Graphics Processing Units

Matthew J. Livesey

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Tags: Computer science, CUDA, Data parallelism, FDTD, Finite-difference time-domain, nVidia, Tesla M2050, Tesla T10, Thesis

March 28, 2012 by hgpu

Improving Performance of OpenCL on CPUs

Ralf Karrenberg, Sebastian Hack

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Tags: Code generation, Computer science, Data parallelism, OpenCL, Performance

March 27, 2012 by hgpu

CUDA 2D Stencil Computations for the Jacobi Method

Jose Maria Cecilia, Jose Manuel Garcia, Manuel Ujaldon

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Tags: Algorithms, Computer science, CUDA, Data parallelism, Differential equations, Linear Algebra, nVidia, Optimization, Tesla C1060, Tesla C870

March 16, 2012 by hgpu

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

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

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

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

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high performance computing on graphics processing units: hgpu.org

Halo Gathering Scalability for Large Scale Multi-dimensional Sznajd Opinion Models Using Data Parallelism with GPUs

Parallel Primitives based Spatial Join of Geospatial Data on GPGPUs

Time-dependent density-functional theory in massively parallel computer architectures: the OCTOPUS project

Spatial Data Structures, Sorting and GPU Parallelism for Situated-agent Simulation and Visualisation

Heterogeneous Computing in Economics: a Simplified Approach

Enhancing GPU Parallelism in Nature-Inspired Algorithms

Efficient Parallelization of Natural Language Applications using GPUs

Enhancing data parallelism for Ant Colony Optimization on GPUs

Nested Data-Parallelism on the GPU

Accelerating the FDTD Method Using SSE and Graphics Processing Units

Improving Performance of OpenCL on CPUs

CUDA 2D Stencil Computations for the Jacobi Method

Recent source codes

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

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

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

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

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