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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

ESE: Efficient Speech Recognition Engine with Compressed LSTM on FPGA

Song Han, Junlong Kang, Huizi Mao, Yiming Hu, Xin Li, Yubin Li, Dongliang Xie, Hong Luo, Song Yao, Yu Wang, Huazhong Yang, William J. Dally

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Tags: Computer science, CUDA, Deep learning, FPGA, LSTM, Machine learning, nVidia, nVidia GeForce GTX Titan X, Speech recognition

December 6, 2016 by hgpu

Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, Jeffrey Dean

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Tags: Computer science, CUDA, Deep learning, LSTM, Machine learning, NLP, nVidia, Tesla K80

September 27, 2016 by hgpu

Efficient softmax approximation for GPUs

Edouard Grave, Armand Joulin, Moustapha Cisse, David Grangier, Herve Jegou

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

September 17, 2016 by hgpu

Data Analysis of Minimally-Structured Heterogeneous Logs: An experimental study of log template extraction and anomaly detection based on Recurrent Neural Network and Naive Bayes

Chang Liu

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Tags: Computer science, CUDA, Deep learning, LSTM, NLP, nVidia, Package, RNN, Thesis

September 13, 2016 by hgpu

Recurrent neural networks for language modeling

Emil Sauer Lynge

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Tags: Computer science, CUDA, Deep learning, LSTM, Neural networks, NLP, nVidia, nVidia GeForce GTX Titan X, Package, Python, RNN, Thesis

March 22, 2016 by hgpu

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

Francisco Javier Ordonez, Daniel Roggen

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Tags: Computer science, Deep learning, LSTM, Machine learning, Neural networks, Package, Python, RNN, Theano

January 26, 2016 by hgpu

Adaptive and Hybrid Machine Learning Approaches Utilizing General Purpose Computing on Graphical Processing Units

Mohammed Abdelrahim

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Tags: Computer science, CUDA, Heterogeneous systems, LSTM, Machine learning, nVidia, nVidia GeForce GTX 770, OpenMP, Thesis

January 19, 2016 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 plugin for PySCF

Python-Based Quantum Chemistry Calculations with GPU Acceleration

QArray

QArray: a GPU-accelerated constant capacitance model simulator for large quantum dot arrays

Celerity: High-level C++ for Accelerator Clusters

Balancing Tracking Granularity and Parallelism in Many-Task Systems: The Horizons Approach

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

Parallel Gaussian process with kernel approximation in CUDA

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