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    Scalable soft real-time supervisor for tomographic AO

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    Type
    Conference Paper
    Authors
    Doucet, Nicolas
    Gratadour, Damien
    Ltaief, Hatem
    Kriemann, Ronald
    Gendron, Eric
    Keyes, David E. cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Applied Mathematics and Computational Science Program
    Extreme Computing Research Center
    Date
    2018-07-19
    Online Publication Date
    2018-07-19
    Print Publication Date
    2018-07-17
    Permanent link to this record
    http://hdl.handle.net/10754/631517
    
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    Abstract
    Implementations of AO tomography for the next generation of Extremely Large Telescopes (ELTs) is challenging because of the extremely large number of degrees of freedom of such systems, in particular when it comes to the tomographic reconstructor computation, due to its size. The computation of this matrix, via the supervisor module, requires leveraging high performance computing techniques, on shared or distributed memory systems, to comply with the specifications of tomographic AO systems, which prescribe an update rate of the order of few minutes. In the scope of the Green-Flash project, we are exploring several approaches to optimize the execution of this soft real-time supervision pipeline. This includes low-rank techniques to reduce the computational load. We have tested several compression schemes to optimize the linear algebra involved in the tomographic reconstructor as well as the computation of the covariance matrices involved in this process. We present, in this paper, the scalable and portable pipeline we have developed to address these issues. Performance in terms of time to solution and scalability are reported. Additionally, the case of low-rank algorithms is stressed as both an attempt to address the computation challenge of the tomographic reconstructor for the supervisor module, and a way to reduce the computational load (hence the overall RTC system latency) at the level of the real-time data pipeline.
    Citation
    Doucet N, Gratadour D, Ltaief H, Kriemann R, Gendron E, et al. (2018) Scalable soft real-time supervisor for tomographic AO. Adaptive Optics Systems VI. Available: http://dx.doi.org/10.1117/12.2313273.
    Publisher
    SPIE-Intl Soc Optical Eng
    Journal
    Adaptive Optics Systems VI
    Conference/Event name
    Adaptive Optics Systems VI 2018
    DOI
    10.1117/12.2313273
    Additional Links
    https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10703/2313273/Scalable-soft-real-time-supervisor-for-tomographic-AO/10.1117/12.2313273.full
    ae974a485f413a2113503eed53cd6c53
    10.1117/12.2313273
    Scopus Count
    Collections
    Conference Papers; Applied Mathematics and Computational Science Program; Extreme Computing Research Center; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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