high performance computing on graphics processing units: hgpu.org

hgpu.org » Theano

A Comparative Measurement Study of Deep Learning as a Service Framework

Yanzhao Wu, Ling Liu, Calton Pu, Wenqi Cao, Semih Sahin, Wenqi Wei, Qi Zhang

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Tags: Caffe, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080 Ti, Tesla M60, Theano

November 3, 2018 by hgpu

Doctor AI: Interpretable Deep Learning for Modeling Electronic Health Records

Edward Choi

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Tags: CUDA, Deep learning, Medicine, nVidia, nVidia GeForce GTX Titan X, Package, RNN, Tesla K80, Theano, Thesis

September 9, 2018 by hgpu

Synkhronos: a Multi-GPU Theano Extension for Data Parallelism

Adam Stooke, Pieter Abbeel

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Tags: Computer science, CUDA, Data parallelism, Deep learning, nVidia, nVidia DGX-1, Package, Tesla P100, Theano

October 15, 2017 by hgpu

End-to-end Deep Learning of Optimization Heuristics

Chris Cummins, Pavlos Petoumenos, Zheng Wang, Hugh Leather

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Tags: ATI, ATI Radeon HD 5970, ATI Radeon HD 7970, Compilers, Computer science, Deep learning, Keras, Machine learning, nVidia GeForce GTX 480, OpenCL, Optimization, Package, TensorFlow, Tesla K20, Theano

September 16, 2017 by hgpu

Accelerating recurrent neural network training using sequence bucketing and multi-GPU data parallelization

Viacheslav Khomenko, Oleg Shyshkov, Olga Radyvonenko, Kostiantyn Bokhan

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Tags: Algorithms, Computer science, Deep learning, LSTM, Neural networks, nVidia, Tesla K40, Theano

September 7, 2017 by hgpu

Deep Architectures for Neural Machine Translation

Antonio Valerio Miceli Barone, Jindrich Helcl, Rico Sennrich, Barry Haddow, Alexandra Birch

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Tags: Computer science, CUDA, Deep learning, NLP, nVidia, nVidia GeForce GTX Titan X, Package, Theano

August 1, 2017 by hgpu

Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets

Maciej Wielgosz, Andrzej Skoczen, Matej Mertik

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Tags: CUDA, Deep learning, Instrumentation and Detectors, Keras, LSTM, Machine learning, Neural networks, nVidia, Physics, RNN, Tesla K80, Theano

February 7, 2017 by hgpu

Critical Comparison of the Classification Ability of Deep Convolutional Neural Network Frameworks with Support Vector Machine Techniques in the Image Classification Process

Robert T. Kelly

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Tags: Computer science, Deep learning, Image processing, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 970, Theano, Thesis, Torch

February 5, 2017 by hgpu

Classification Performance of Convolutional Neural Networks

Niklas Mattsson

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Tags: Computer science, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 960, nVidia Tegra TX1, Performance, Python, Theano, Thesis

November 1, 2016 by hgpu

Training a Feedback Loop for Hand Pose Estimation

Markus Oberweger, Paul Wohlhart, Vincent Lepetit

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Tags: Computer science, Computer vision, CUDA, Neural networks, nVidia, nVidia GeForce GTX 780 Ti, Package, Theano

October 4, 2016 by hgpu

IODA: an Input/Output Deep Architecture for image labeling

J. Lerouge, R. Herault, Clement Chatelain, F. Jardin, R. Modzelewski

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Python, Tesla C1060, Theano

July 18, 2016 by hgpu

TheanoLM – An Extensible Toolkit for Neural Network Language Modeling

Seppo Enarvi, Mikko Kurimo

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, NLP, nVidia, Package, Python, RNN, Speech recognition, Theano

May 7, 2016 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

A Comparative Measurement Study of Deep Learning as a Service Framework

Doctor AI: Interpretable Deep Learning for Modeling Electronic Health Records

Synkhronos: a Multi-GPU Theano Extension for Data Parallelism

End-to-end Deep Learning of Optimization Heuristics

Accelerating recurrent neural network training using sequence bucketing and multi-GPU data parallelization

Deep Architectures for Neural Machine Translation

Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets

Critical Comparison of the Classification Ability of Deep Convolutional Neural Network Frameworks with Support Vector Machine Techniques in the Image Classification Process

Classification Performance of Convolutional Neural Networks

Training a Feedback Loop for Hand Pose Estimation

IODA: an Input/Output Deep Architecture for image labeling

TheanoLM – An Extensible Toolkit for Neural Network Language Modeling

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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