Multi-GPU Development of a Neural Networks Based Reconstructor for Adaptive Optics

Aberrations introduced by the atmospheric turbulence in large telescopes are compensated using adaptive optics systems, where the use of deformable mirrors and multiple sensors relies on complex control systems. Recently, the development of larger scales of telescopes as the E-ELT or TMT has created...

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Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2018-01, Vol.2018 (2018), p.1-9
Main Authors: de Cos Juez, Francisco Javier, Calvo-Rolle, José Luis, Sánchez-Rodríguez, María Luisa, González-Gutiérrez, Carlos
Format: Article
Language:English
Subjects:
Acoustics
Adaptive optics
Adaptive systems
Algorithms
Artificial neural networks
Atmosphere
Atmospheric turbulence
Complexity
Control systems
Deformable mirrors
Deformation
Formability
International conferences
Lasers
Machine learning
Neural networks
Researchers
Sensors
Signal processing
Software
Stars & galaxies
Telescope
Telescopes
Turbulence
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Summary:Aberrations introduced by the atmospheric turbulence in large telescopes are compensated using adaptive optics systems, where the use of deformable mirrors and multiple sensors relies on complex control systems. Recently, the development of larger scales of telescopes as the E-ELT or TMT has created a computational challenge due to the increasing complexity of the new adaptive optics systems. The Complex Atmospheric Reconstructor based on Machine Learning (CARMEN) is an algorithm based on artificial neural networks, designed to compensate the atmospheric turbulence. During recent years, the use of GPUs has been proved to be a great solution to speed up the learning process of neural networks, and different frameworks have been created to ease their development. The implementation of CARMEN in different Multi-GPU frameworks is presented in this paper, along with its development in a language originally developed for GPU, like CUDA. This implementation offers the best response for all the presented cases, although its advantage of using more than one GPU occurs only in large networks.
ISSN:1076-2787
1099-0526
DOI:10.1155/2018/5348265