high performance computing on graphics processing units: hgpu.org

Thorsten Kurth, Sean Treichler, Joshua Romero, Mayur Mudigonda, Nathan Luehr, Everett Phillips, Ankur Mahesh, Michael Matheson, Jack Deslippe, Massimiliano Fatica, Prabhat, Michael Houston

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Tags: CUDA, Deep learning, Earth and Space Sciences, Neural networks, nVidia, TensorFlow, Tesla V100, Weather prediction

October 6, 2018 by hgpu

Scalability Analysis of Synchronous Data-Parallel Artificial Neural Network (ANN) Learners

Chang Sun

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Tags: Computer science, Machine learning, Neural networks, nVidia, OpenCL, Tesla P100, Thesis

September 23, 2018 by hgpu

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

Jack Turner, Jose Cano, Valentin Radu, Elliot J. Crowley, Michael O'Boyle, Amos Storkey

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Tags: ARM, Compression, Computer science, Deep learning, Machine learning, Neural networks, OpenCL, OpenMP, Package

September 16, 2018 by hgpu

A deep learning approach to autonomous lunar landing

Ilaria Bloise, Marcello Orlandelli

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Tags: Algorithms, Artificial intelligence, Computer science, Deep learning, LSTM, Machine learning, Neural networks, nVidia, RNN, Tesla K20, Thesis

September 16, 2018 by hgpu

Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications

Geir Thore Berge, Ole-Christoffer Granmo, Tor Oddbjorn Tveit, Morten Goodwin, Lei Jiao, Bernt Viggo Matheussen

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Tags: CUDA, Deep learning, Keras, Medicine, Neural networks, nVidia, Package, TensorFlow

September 16, 2018 by hgpu

Large Scale Language Modeling: Converging on 40GB of Text in Four Hours

Raul Puri, Robert Kirby, Nikolai Yakovenko, Bryan Catanzaro

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Tags: Computer science, CUDA, Deep learning, LSTM, Mixed precision, Neural networks, nVidia, Package, Tesla V100, Torch

August 11, 2018 by hgpu

Parallax: Automatic Data-Parallel Training of Deep Neural Networks

Soojeong Kim, Gyeong-In Yu, Hojin Park, Sungwoo Cho, Eunji Jeong, Hyeonmin Ha, Sanha Lee, Joo Seong Jeong, Byung-Gon Chun

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan XP, OpenMPI, Package

August 11, 2018 by hgpu

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high performance computing on graphics processing units: hgpu.org

Workload-aware Automatic Parallelization for Multi-GPU DNN Training

Towards Efficient Large-Scale Graph Neural Network Computing

A Survey of FPGA-based Accelerators for Convolutional Neural Networks

AI Benchmark: Running Deep Neural Networks on Android Smartphones

Sparse Winograd Convolutional neural networks on small-scale systolic arrays

Exascale Deep Learning for Climate Analytics

Scalability Analysis of Synchronous Data-Parallel Artificial Neural Network (ANN) Learners

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

A deep learning approach to autonomous lunar landing

Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications

Large Scale Language Modeling: Converging on 40GB of Text in Four Hours

Parallax: Automatic Data-Parallel Training of Deep Neural Networks

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torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

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Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

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