high performance computing on graphics processing units: hgpu.org

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Experiences in Data-Parallel Simulation and Analysis of Complex Systems with Irregular Graph Structures

Arno Leist

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Tags: Algorithms, Cell processor, Computer science, CUDA, Graph theory, Neural networks, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 580, Phase transition, Thesis

January 23, 2012 by hgpu

Analysis Acceleration in TMVA for the ATLAS Experiment at CERN using GPU Computing

Hazel McKendrick

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Tags: CUDA, High Energy Physics - Experiment, Machine learning, Neural networks, nVidia, Physics, Tesla C1060, Tesla C2050, Thesis

January 23, 2012 by hgpu

GPU-based implementation of a cerebellar spiking network model for realtime robot control

Tadashi Yamazaki, Jun Igarashi

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Tags: Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX 580

January 17, 2012 by hgpu

Parallel Implementations of Hopfield Neural Networks On GPU

Li Liang

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Tags: Algorithms, Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX 480, Optimization

January 13, 2012 by hgpu

Implementation of Virtual Embryology using the Thrust library for CUDA

Anna Aichmayr Markus Dauschan

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Tags: Biology, CUDA, Neural networks, nVidia, Python

January 13, 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

Hyper neural network on OpenCL

Frantisek Sindelar

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Tags: Algorithms, Compression, Computer science, Genetic programming, Neural networks, nVidia, nVidia Quadro NVS 140 M, OpenCL, Thesis

January 6, 2012 by hgpu

A Parallel Supercomputer Implementation of a Biological Inspired Neural Network and its use for Pattern Recognition

Vincent de Ladurantaye, Jean Lavoie, Jocelyn Bergeron, Maxime Parenteau, Huizhong Lu, Ramin Pichevar, Jean Rouat

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Tags: Biology, CUDA, MPI, Neural networks, nVidia, nVidia GeForce GTX 285, Pattern recognition

January 5, 2012 by hgpu

Efficient mapping of the training of Convolutional Neural Networks to a CUDA-based cluster

Jonatan Ward, Sergey Andreev, Francisco Heredia, Bogdan Lazar, Zlatka Manevska

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Tags: Algorithms, CNN, Computer science, CUDA, GPU cluster, Neural networks, nVidia, nVidia GeForce GTX 570, Performance

January 4, 2012 by hgpu

Improving the speed of neural networks on CPUs

Vincent Vanhoucke, Andrew Senior, Mark Z. Mao

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Tags: Computer science, CUDA, Neural networks, nVidia, Speech recognition, Tesla C2070, Tutorial

December 28, 2011 by hgpu

An Efficient Simulation Environment for Modeling Large-Scale Cortical Processing

Micah Richert, Jayram Moorkanikara Nageswaran, Nikil Dutt, Jeffrey L. Krichmar

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Source codes

Tags: Biology, CUDA, Neural networks, Neuroscience, nVidia, Package, Tesla C1060

December 19, 2011 by hgpu

Speed sign detection and recognition by convolutional neural networks

Maurice Peemen, Bart Mesman, Henk Corporaal

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Tags: Algorithms, CNN, Computer science, Computer vision, CUDA, Image recognition, Neural networks, nVidia, nVidia GeForce GTX 460

December 15, 2011 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

Experiences in Data-Parallel Simulation and Analysis of Complex Systems with Irregular Graph Structures

Analysis Acceleration in TMVA for the ATLAS Experiment at CERN using GPU Computing

GPU-based implementation of a cerebellar spiking network model for realtime robot control

Parallel Implementations of Hopfield Neural Networks On GPU

Implementation of Virtual Embryology using the Thrust library for CUDA

Neural Network Simulation: The recognition application

Hyper neural network on OpenCL

A Parallel Supercomputer Implementation of a Biological Inspired Neural Network and its use for Pattern Recognition

Efficient mapping of the training of Convolutional Neural Networks to a CUDA-based cluster

Improving the speed of neural networks on CPUs

An Efficient Simulation Environment for Modeling Large-Scale Cortical Processing

Speed sign detection and recognition by convolutional neural networks

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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