high performance computing on graphics processing units: hgpu.org

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Accelerating Large-Scale Convolutional Neural Networks with Parallel Graphics Multiprocessors

Dominik Scherer, Hannes Schulz, Sven Behnke

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Tags: Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX 285

January 2, 2011 by hgpu

Processing Neocognitron of Face Recognition on High Performance Environment Based on GPU with CUDA Architecture

Gustavo Poli, Jose Hiroki Saito, Joao F. Mari, Marcelo R. Zorza

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Tags: CUDA, Image processing, Image recognition, Neural networks, nVidia, nVidia GeForce 8800 GTX, Video tracking

December 24, 2010 by hgpu

SOMGPU: An unsupervised pattern classifier on Graphical Processing Unit

R. D. Prabhu

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Tags: Computer science, DirectX, Neural networks, nVidia, nVidia GeForce 6150, Self-organizing map

December 8, 2010 by hgpu

Graphics Hardware Implementation of the Parameter-Less Self-organising Map

Alexander Campbell, Erik Berglund, Alexander Streit

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Tags: Cg, Computer science, Neural networks, nVidia, nVidia GeForce 6800, Self-organizing map

November 30, 2010 by hgpu

A dynamically configurable coprocessor for convolutional neural networks

Srimat Chakradhar, Murugan Sankaradas, Venkata Jakkula, Srihari Cadambi

Tags: Computer science, FPGA, Neural networks

November 22, 2010 by hgpu

Parallel implementation of Artificial Neural Network training for speech recognition

Stefano Scanzio, Sandro Cumani, Roberto Gemello, Franco Mana, P. Laface

Tags: Computer science, CUDA, Neural networks, nVidia

November 18, 2010 by hgpu

A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors

Jayram Moorkanikara M. Nageswaran, Nikil Dutt, Jeffrey L. Krichmar, Alex Nicolau, Alexander V. Veidenbaum

Tags: Biology, Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX 280, STDP

November 13, 2010 by hgpu

A Markovian event-based framework for stochastic spiking neural networks

Jonathan Touboul, Olivier Faugeras

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Tags: Algorithms, Mathematics, Neural networks, Neurons and Cognition, Statistics, Statistics Theory

November 13, 2010 by hgpu

Multifold Acceleration of Neural Network Computations Using GPU

Alexander Guzhva, Sergey Dolenko, Igor Persiantsev

Tags: Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX 260

November 8, 2010 by hgpu

Efficient simulation of large-scale spiking neural networks using CUDA graphics processors

Jayram M. Nageswaran, Nikil Dutt, Jeffrey L. Krichmar, Alex Nicolau, Alex Veidenbaum

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Tags: Biology, Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX 280, STDP

November 6, 2010 by hgpu

Using GPUs for Machine Learning Algorithms

Dave Steinkrau, Patrice Y. Simard, Ian Buck

Tags: Computer science, Neural networks, Pattern recognition

November 5, 2010 by hgpu

Approximate Dynamic Programming and Neural Networks on Game Hardware

R. J. Meuth, D. C. Wunsch

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Tags: Computer science, Neural networks, Review

November 4, 2010 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

Accelerating Large-Scale Convolutional Neural Networks with Parallel Graphics Multiprocessors

Processing Neocognitron of Face Recognition on High Performance Environment Based on GPU with CUDA Architecture

SOMGPU: An unsupervised pattern classifier on Graphical Processing Unit

Graphics Hardware Implementation of the Parameter-Less Self-organising Map

A dynamically configurable coprocessor for convolutional neural networks

Parallel implementation of Artificial Neural Network training for speech recognition

A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors

A Markovian event-based framework for stochastic spiking neural networks

Multifold Acceleration of Neural Network Computations Using GPU

Efficient simulation of large-scale spiking neural networks using CUDA graphics processors

Using GPUs for Machine Learning Algorithms

Approximate Dynamic Programming and Neural Networks on Game Hardware

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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