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RASR/NN: The RWTH Neural Network Toolkit for Speech Recognition

Simon Wiesler, Alexander Richard, Pavel Golik, Ralf Schluter, Hermann Ney

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Tags: Algorithms, Computer science, CUDA, Neural networks, nVidia, Speech recognition, Tesla K20

March 9, 2014 by hgpu

Optimized Deep Learning Architectures with Fast Matrix Operation Kernels on Parallel Platform

Ying Zhang, Saizheng Zhang

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Tags: Computer science, CUBLAS, CUDA, Machine learning, Neural networks, nVidia

February 2, 2014 by hgpu

Implementing Neural Networks Efficiently

Ronan Collobert, Koray Kavukcuoglu, Clement Farabet

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Tags: Algorithms, Computer science, CUDA, Machine learning, Neural networks, nVidia, Tesla M2090

December 29, 2013 by hgpu

GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training

Thomas Paine, Hailin Jin, Jianchao Yang, Zhe Lin, Thomas Huang

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Tags: Computer science, Computer vision, CUDA, Data parallelism, Neural and Evolutionary Computing, Neural networks, nVidia, Tesla K20

December 24, 2013 by hgpu

Single Server Multi-GPU Training of ConvNets

Omry Yadan, Keith Adams, Yaniv Taigman, Marc'Aurelio Ranzato

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Tags: Computer science, Computer vision, Machine learning, Neural networks

December 23, 2013 by hgpu

GPU and CPU Cooperative Accelerated Road Detection

Peng Xiong, Cheng Xu, Zheng Tian, Tao Li

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Tags: Computer science, Computer vision, Neural networks, OpenCV

December 3, 2013 by hgpu

A Novel CPU/GPU Simulation Environment for Large-Scale Biologically-Realistic Neural Modeling

Roger V. Hoang, Devyani Tanna, Laurence C. Jayet Bray, Sergiu M. Dascalu, Frederick C. Harris Jr.

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Tags: Biology, CUDA, Neural networks, Neuroscience, nVidia, nVidia GeForce GTX 460, nVidia GeForce GTX 480, nVidia GeForce GTX 680, Package, Tesla C2050

September 14, 2013 by hgpu

GPU-based simulation of brain neuron models

H. A. Du Nguyen

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Tags: CUDA, Neural networks, Neuroscience, nVidia, nVidia GeForce GT 640, Review, Tesla C2075, Thesis

September 7, 2013 by hgpu

Multiple Time Scales Recurrent Neural Network for Complex Action Acquisition

Martin Peniak, Davide Marocco, Jun Taniy, Yuichi Yamashitay, Kerstin Fischerz, Angelo Cangelosi

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Tags: Computer science, CUDA, Neural networks, nVidia, Package

August 27, 2013 by hgpu

Characterization of Lossy SIW Resonators Based on Multilayer Perceptron Neural Networks on Graphics Processing Unit

Giandomenico Amendola, Giovanni Angiulli, Emilio Arnieri, Luigi Boccia, Domenico De Carlo

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Tags: CUDA, Electrodynamics, Neural networks, nVidia, Tesla C2070

July 25, 2013 by hgpu

GPU Implementation of Real-Time Biologically Inspired Face Detection using CUDA

Zeinab Farhoudi, Ali Broumandnia, Elham Askary, Sara Motamed

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Tags: Computer science, Computer vision, CUDA, Image processing, Neural networks, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce 9800 GTX

July 8, 2013 by hgpu

Artificial Neural Network Simulation on CUDA

John Pendlebury, Huanhuan Xiong, Ray Walshe

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

June 21, 2013 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

RASR/NN: The RWTH Neural Network Toolkit for Speech Recognition

Optimized Deep Learning Architectures with Fast Matrix Operation Kernels on Parallel Platform

Implementing Neural Networks Efficiently

GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training

Single Server Multi-GPU Training of ConvNets

GPU and CPU Cooperative Accelerated Road Detection

A Novel CPU/GPU Simulation Environment for Large-Scale Biologically-Realistic Neural Modeling

GPU-based simulation of brain neuron models

Multiple Time Scales Recurrent Neural Network for Complex Action Acquisition

Characterization of Lossy SIW Resonators Based on Multilayer Perceptron Neural Networks on Graphics Processing Unit

GPU Implementation of Real-Time Biologically Inspired Face Detection using CUDA

Artificial Neural Network Simulation on CUDA

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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