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On Vectorization of Deep Convolutional Neural Networks for Vision Tasks

Jimmy SJ. Ren, Li Xu

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Tags: CNN, Computer science, Computer vision, CUDA, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 780 Ti, Package

January 30, 2015 by hgpu

Convolutional Neural Networks for Human Activity Recognition using Mobile Sensors

Ming Zeng, Le T. Nguyen, Bo Yu, Ole J. Mengshoel, Jiang Zhu, Pang Wu, Joy Zhang

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Tags: CNN, CUDA, Image recognition, Neural networks, nVidia, Sensing, Speech recognition, Tesla K20

January 13, 2015 by hgpu

Image Super-Resolution Using Deep Convolutional Networks

Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang

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Tags: CNN, Computer science, CUDA, Machine learning, Neural networks, nVidia, Package

January 10, 2015 by hgpu

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille

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Tags: Algorithms, CNN, Computer science, Computer vision, CUDA, Neural networks, nVidia, nVidia GeForce GTX Titan

December 26, 2014 by hgpu

Fast Convolutional Nets With fbfft: A GPU Performance Evaluation

Nicolas Vasilache, Jeff Johnson, Michael Mathieu, Soumith Chintala, Serkan Piantino, Yann LeCun

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Tags: Algorithms, CNN, Computer science, CUDA, FFT, Machine learning, Neural and Evolutionary Computing, Neural networks, nVidia, Package, Tesla K40

December 26, 2014 by hgpu

Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Classification

Hongsheng Li, Rui Zhao, Xiaogang Wang

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Tags: Algorithms, CNN, Computer science, Computer vision, CUDA, Machine learning, Neural networks, nVidia, Tesla K40

December 16, 2014 by hgpu

Real-Time Grasp Detection Using Convolutional Neural Networks

Joseph Redmon, Anelia Angelova

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Tags: CNN, Computer science, CUDA, Machine learning, Neural networks, nVidia, Tesla K20

December 12, 2014 by hgpu

Deep Dynamic Neural Networks for Gesture Segmentation and Recognition

Di Wu, Ling Shao

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Tags: CNN, Computer science, CUDA, Neural networks, nVidia, Package, Python, Rendering

October 4, 2014 by hgpu

3D Object Recognition using Convolutional Neural Networks with Transfer Learning between Input Channels

Luis A. Alexandre

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Tags: CNN, Computer science, Computer vision, CUDA, Image recognition, Neural networks, nVidia, nVidia GeForce GTX 680

May 21, 2014 by hgpu

Accelerating large-scale simulations of cortical neuronal network development

Fumitaka Kawasaki, Michael Stiber

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Tags: CNN, Computer science, CUDA, Neural networks, Neuroscience, nVidia, Tesla C1060

March 23, 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

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

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

On Vectorization of Deep Convolutional Neural Networks for Vision Tasks

Convolutional Neural Networks for Human Activity Recognition using Mobile Sensors

Image Super-Resolution Using Deep Convolutional Networks

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Fast Convolutional Nets With fbfft: A GPU Performance Evaluation

Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Classification

Real-Time Grasp Detection Using Convolutional Neural Networks

Deep Dynamic Neural Networks for Gesture Segmentation and Recognition

3D Object Recognition using Convolutional Neural Networks with Transfer Learning between Input Channels

Accelerating large-scale simulations of cortical neuronal network development

Neural Network Simulation: The recognition application

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

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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