high performance computing on graphics processing units: hgpu.org

Georgios Smaragdos, Georgios Chatzikonstantis, Rahul Kukreja, Harrys Sidiropoulos, Dimitrios Rodopoulos, Ioannis Sourdis, Zaid Al-Ars, Christoforos Kachris, Dimitrios Soudris, Chris I. De Zeeuw, Christos Strydis

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Tags: Biophysics, Computer science, Heterogeneous systems, Intel Xeon Phi, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX Titan X, OpenMP

December 10, 2016 by hgpu

Learning Structured Sparsity in Deep Neural Networks

Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX Titan Black, nVidia Quadro K600, Tesla K40

August 16, 2016 by hgpu

RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks

Patrick Doetsch, Albert Zeyer, Paul Voigtlaender, Ilya Kulikov, Ralf Schluter, Hermann Ney

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Tags: Computer science, CUDA, Machine learning, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX 980, Package, RNN, Speech recognition

August 4, 2016 by hgpu

Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups

Yani Ioannou, Duncan Robertson, Roberto Cipolla, Antonio Criminisi

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Tags: Computer science, CUDA, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX Titan Z

May 23, 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

Neural GPUs Learn Algorithms

Lukasz Kaiser, Ilya Sutskever

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural and Evolutionary Computing, Neural networks, nVidia, Tesla K40

December 1, 2015 by hgpu

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high performance computing on graphics processing units: hgpu.org

Understanding the Power of Evolutionary Computation for GPU Code Optimization

Performance Analysis and Improvement of Parallel Differential Evolution

An Overview on the Latest Nature-Inspired and Metaheuristics-Based Image Registration Algorithms

GEVO: GPU Code Optimization using Evolutionary Computation

BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism

A hybrid CPU-GPU parallelization scheme of variable neighborhood search for inventory optimization problems

BrainFrame: A heterogeneous accelerator platform for neuron simulations

Learning Structured Sparsity in Deep Neural Networks

RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks

Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups

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

Neural GPUs Learn Algorithms

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PipeCNN: An OpenCL-based FPGA Accelerator for Convolutional Neural Networks

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Magicube

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segTACO

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