high performance computing on graphics processing units: hgpu.org

Martin Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mane, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viegas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, Xiaoqiang Zheng

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Tags: Computer science, Computer vision, CUDA, Deep learning, Heterogeneous systems, Information Retrieval, Machine learning, Neural networks, nVidia, Package, Speech recognition

November 11, 2015 by hgpu

Data Stream Classification using Random Feature Functions and Novel Method Combinations

Diego Marron, Jesse Read, Albert Bifet, Nacho Navarro

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Tags: Computer science, CUDA, Deep learning, Nearest neighbour, Neural networks, nVidia, Tesla K40

November 8, 2015 by hgpu

Deep Learning for Computer Vision: A comparison between Convolutional Neural Networks and Hierarchical Temporal Memories on object recognition tasks

Vincenzo Lomonaco

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Tags: Artificial intelligence, Computer science, Computer vision, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Pattern recognition, Tesla C2075, Thesis

November 8, 2015 by hgpu

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

high performance computing on graphics processing units: hgpu.org

Auxiliary Image Regularization for Deep CNNs with Noisy Labels

Neural GPUs Learn Algorithms

Semantic Segmentation of Colon Glands with Deep Convolutional Neural Networks and Total Variation Segmentation

GPU-based Acceleration of Deep Convolutional Neural Networks on Mobile Platforms

Learning Representation for Scene Understanding: Epitomes, CRFs, and CNNs

Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications

Recurrent Neural Networks Hardware Implementation on FPGA

Supervised Hashing with Deep Neural Networks

Large Scale Artificial Neural Network Training Using Multi-GPUs

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Data Stream Classification using Random Feature Functions and Novel Method Combinations

Deep Learning for Computer Vision: A comparison between Convolutional Neural Networks and Hierarchical Temporal Memories on object recognition tasks

Recent source codes

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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

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Parallel Gaussian process with kernel approximation in CUDA

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