high performance computing on graphics processing units: hgpu.org

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GeePS: Scalable deep learning on distributed GPUs with a GPU-specialized parameter server

Henggang Cui, Hao Zhang, Gregory R. Ganger, Phillip B. Gibbons, Eric P. Xing

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Tags: Caffe, Computer science, CUDA, Deep learning, Neural networks, nVidia, Tesla K20

April 16, 2016 by hgpu

CUED-RNNLM – An Open-Source Toolkit for Efficient Training and Evaluation of Recurrent Neural Network Language Models

X. Chen, X. Liu, Y. Qian, M.J.F. Gales, P.C. Woodland

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, NLP, nVidia, nVidia GeForce GTX Titan, Package, RNN, Speech recognition

April 12, 2016 by hgpu

Optimizing Performance of Recurrent Neural Networks on GPUs

Jeremy Appleyard, Tomas Kocisky, Phil Blunsom

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Tags: Computer science, CUDA, Deep learning, Lua, Neural networks, nVidia, Package, RNN, Tesla M40

April 9, 2016 by hgpu

dMath: A Scalable Linear Algebra and Math Library for Heterogeneous GP-GPU Architectures

Steven Eliuk, Cameron Upright, Anthony Skjellum

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Linear Algebra, Matrix multiplication, Neural and Evolutionary Computing, Neural networks, nVidia, OpenMPI, Tesla K80

April 9, 2016 by hgpu

Are Very Deep Neural Networks Feasible on Mobile Devices?

S. Rallapalli, H. Qiu, A. J. Bency, S. Karthikeyan, R. Govindan, B.S.Manjunath, R. Urgaonkar

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Tags: CNN, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia Jetson TK1

April 6, 2016 by hgpu

Classiffication-based Financial Markets Prediction using Deep Neural Networks

Matthew Dixon, Diego Klabjan, Jin Hoon Bang

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Tags: Computer science, Deep learning, Finance, Intel Xeon Phi, Neural networks

April 3, 2016 by hgpu

Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets

Pascal Vincent, Alexandre de Brebisson, Xavier Bouthillier

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Tags: Algorithms, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan Black, Package

March 25, 2016 by hgpu

Accelerating Deep Neural Network Training with Inconsistent Stochastic Gradient Descent

Linnan Wang, Yi Yang, Martin Renqiang Min, Srimat Chakradhar

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Tags: Caffe, Computer science, CUDA, Data parallelism, Deep learning, Heterogeneous systems, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan X

March 25, 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

Automatic Detection and Denoising of Signals in Large Geophysical Datasets

Gabriel O. Trisca

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Tags: Aparapi, Computer science, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan, OpenCL, RNN, Seismology, Sensing, Signal denoising, Tesla K20, Thesis

March 20, 2016 by hgpu

D-face: Parallel Implementation of CNN Based Face Classifier using Drone Data On K40 & Jetson TK1

Harish, Ayyappa, Santosh, P K Baruah, Kaliuday Balleda, Sairam K M Menon

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Tags: CNN, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia Jetson TK1, Tesla K40

March 8, 2016 by hgpu

Proteus: Exploiting Numerical Precision Variability in Deep Neural Networks

P. Judd, J. Albericio, N. Enright Jerger, A. Moshovos, T. Hetherington, T. Aamodt

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Tags: Computer science, Deep learning, GPGPU-sim, Neural networks

March 3, 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_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

See all packages

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high performance computing on graphics processing units: hgpu.org

GeePS: Scalable deep learning on distributed GPUs with a GPU-specialized parameter server

CUED-RNNLM – An Open-Source Toolkit for Efficient Training and Evaluation of Recurrent Neural Network Language Models

Optimizing Performance of Recurrent Neural Networks on GPUs

dMath: A Scalable Linear Algebra and Math Library for Heterogeneous GP-GPU Architectures

Are Very Deep Neural Networks Feasible on Mobile Devices?

Classiffication-based Financial Markets Prediction using Deep Neural Networks

Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets

Accelerating Deep Neural Network Training with Inconsistent Stochastic Gradient Descent

Recurrent neural networks for language modeling

Automatic Detection and Denoising of Signals in Large Geophysical Datasets

D-face: Parallel Implementation of CNN Based Face Classifier using Drone Data On K40 & Jetson TK1

Proteus: Exploiting Numerical Precision Variability in Deep Neural Networks

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

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

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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