Real-Time Massively Distributed Multi-object Adaptive Optics Simulations for the European Extremely Large Telescope

Hatem Ltaief, Ali Charara, Damien Gratadour, Nicolas Doucet, Bilel Hadri, Eric Gendron, Saber Feki, David Keyes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

The European Extremely Large Telescope (E-ELT) is one of today's most challenging projects in ground-based astronomy. Addressing one of the key science cases for the E-ELT, the study of the early Universe, requires the implementation of multi-object adaptive optics (MOAO), a dedicated concept relying on turbulence tomography. We use a novel pseudo-Analytical approach to simulate the performance of tomographic reconstruction of the atmospheric turbulence in an MOAO system on real datasets. We simulate simultaneously 4K galaxies in a common field of view on massively parallel supercomputers during a single night of observations. We are able to generate a first-ever high-resolution galaxy map at almost a real-Time throughput. This simulation scale opens new research horizons in numerical methods for experimental astronomy, some core components of the pipeline standing as pathfinders toward actual operations and future astronomic discoveries on the E-ELT.
Original languageEnglish (US)
Title of host publication2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages75-84
Number of pages10
ISBN (Print)9781538643686
DOIs
StatePublished - Aug 6 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The authors would like to thank Aniello Esposito from Cray Inc. for his help in running the code and the vendor Cray for systems remote accesses in the context of the Cray Center of Excellence awarded to the Extreme Computing Research Center at KAUST. For computer time, this research used the resources from KAUST Supercomputing Laboratory for Shaheen-2 core hours allocation.

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