high performance computing on graphics processing units: hgpu.org

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BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism

Nicolas Weber, Florian Schmidt, Mathias Niepert, Felipe Huici

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Tags: AI, Artificial intelligence, cache, CNN, Computer science, cpu, CUDA, Deep learning, GPU, Machine learning, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX 1080 Ti

April 25, 2018 by hgpu

A hybrid CPU-GPU parallelization scheme of variable neighborhood search for inventory optimization problems

Nikolaos Antoniadis, Angelo Sifaleras

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Tags: Computer science, Metaheuristics, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX 960, nVidia GeForce GTX 980, OpenACC, OpenMP, Optimization

April 20, 2017 by hgpu

BrainFrame: A heterogeneous accelerator platform for neuron simulations

Georgios Smaragdos, Georgios Chatzikonstantis, Rahul Kukreja, Harrys Sidiropoulos, Dimitrios Rodopoulos, Ioannis Sourdis, Zaid Al-Ars, Christoforos Kachris, Dimitrios Soudris, Chris I. De Zeeuw, Christos Strydis

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Tags: Biophysics, Computer science, Heterogeneous systems, Intel Xeon Phi, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX Titan X, OpenMP

December 10, 2016 by hgpu

Learning Structured Sparsity in Deep Neural Networks

Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX Titan Black, nVidia Quadro K600, Tesla K40

August 16, 2016 by hgpu

RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks

Patrick Doetsch, Albert Zeyer, Paul Voigtlaender, Ilya Kulikov, Ralf Schluter, Hermann Ney

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Tags: Computer science, CUDA, Machine learning, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX 980, Package, RNN, Speech recognition

August 4, 2016 by hgpu

Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups

Yani Ioannou, Duncan Robertson, Roberto Cipolla, Antonio Criminisi

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Tags: Computer science, CUDA, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX Titan Z

May 23, 2016 by hgpu

dMath: A Scalable Linear Algebra and Math Library for Heterogeneous GP-GPU Architectures

Steven Eliuk, Cameron Upright, Anthony Skjellum

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Linear Algebra, Matrix multiplication, Neural and Evolutionary Computing, Neural networks, nVidia, OpenMPI, Tesla K80

April 9, 2016 by hgpu

Neural GPUs Learn Algorithms

Lukasz Kaiser, Ilya Sutskever

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural and Evolutionary Computing, Neural networks, nVidia, Tesla K40

December 1, 2015 by hgpu

Supervised Hashing with Deep Neural Networks

Ziming Zhang, Yuting Chen, Venkatesh Saligrama

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Tags: Computer science, CUDA, Deep learning, Hashing, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX Titan

November 20, 2015 by hgpu

Large Scale Artificial Neural Network Training Using Multi-GPUs

Linnan Wang, Wei Wu, Jianxiong Xiao, Yang Yi

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX Titan X, Tesla K40

November 20, 2015 by hgpu

Deep Convolutional Neural Networks for Smile Recognition

Patrick O. Glauner

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Tags: Computer science, CUDA, Deep learning, Neural and Evolutionary Computing, Neural networks, nVidia, Tesla K40, Thesis

August 28, 2015 by hgpu

Multi-swarm PSO algorithm for the Quadratic Assignment Problem: a massive parallel implementation on the OpenCL platform

Piotr Szwed, Wojciech Chmiel

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Tags: Algorithms, AMD Radeon HD 6750 M, ATI, Computer science, Neural and Evolutionary Computing, OpenCL, Particle swarm optimization

April 23, 2015 by hgpu

Mass_Flow_Estimation: Semi-supervised implementation of a DNN for mass prediction from sparsely labeled images

Mass Estimation from Images using Deep Neural Network and Sparse Ground Truth

KNN: k-Nearest Neighbours algorithm

High Performance Computing via High Level Synthesis

SODECL: library of ordinary differential equation (ODE) and stochastic differential equation (SDE) solvers in OpenCL

SODECL: An Open Source Library for Calculating Multiple Orbits of a System of Stochastic Differential Equations in Parallel

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high performance computing on graphics processing units: hgpu.org

BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism

A hybrid CPU-GPU parallelization scheme of variable neighborhood search for inventory optimization problems

BrainFrame: A heterogeneous accelerator platform for neuron simulations

Learning Structured Sparsity in Deep Neural Networks

RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks

Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups

dMath: A Scalable Linear Algebra and Math Library for Heterogeneous GP-GPU Architectures

Neural GPUs Learn Algorithms

Supervised Hashing with Deep Neural Networks

Large Scale Artificial Neural Network Training Using Multi-GPUs

Deep Convolutional Neural Networks for Smile Recognition

Multi-swarm PSO algorithm for the Quadratic Assignment Problem: a massive parallel implementation on the OpenCL platform

Recent source codes

Mass_Flow_Estimation: Semi-supervised implementation of a DNN for mass prediction from sparsely labeled images

KNN: k-Nearest Neighbours algorithm

SODECL: library of ordinary differential equation (ODE) and stochastic differential equation (SDE) solvers in OpenCL

sisph: Simple Iterative Incompressible Smoothed Particle Hydrodynamics

GraphBLAST: High-Performance Linear Algebra-based Graph Primitives on GPUs

FAST: Framework for Heterogeneous Medical Image Computing and Visualization

ESBMC-GPU

FBLAS: BLAS implementation for Intel FPGA

CuLE: A CUDA port of the Atari Learning Environment (ALE)

MagmaDNN

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Featured events

7th International Workshop on OpenCL, 2019

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

International Conference on Frontiers of Neural Networks (ICFNN), 2019

International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), 2019