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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Source codes

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

September 16, 2018 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

wav2letter++: The Fastest Open-source Speech Recognition System

A Framework for Fast and Efficient Neural Network Compression

SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks

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

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