Efficient supervision strategy for tomographic AO systems on E-ELT
Keyes, David E.
KAUST DepartmentComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Extreme Computing Research Center
Applied Mathematics and Computational Science Program
Permanent link to this recordhttp://hdl.handle.net/10754/666385
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AbstractA critical subsystem of the tomographic AO RTC is the supervisor module. Its role is to feed the challenging real-time data pipeline with a new reconstructor matrix at a regular rate, computed from a statistical analysis of the measurements, to optimize the performance of the AO system. This process involves solving a system of linear equations defined by the covariance matrix of the wave front sensors' measurements, the size of which may be up to 90k-90k for the E-ELT's First light instruments using tomographic AO modules. The computational load for the solver of this dense symmetric matrix system is quite significant at this scale but can be efficiently handled using state-of-the-art energy-efficient manycore x86 or accelerator-based architectures, such as KNLs or GPUs, respectively. As part of the Green Flash project, we develop a supervisor module and demonstrate its portability by deploying it on each aforementioned hardware system. Finally, we describe different implementations and their trade-offs in terms of performance and accuracy and show preliminary results on the possible impact of hierarchically low-rank approximation methods on the overall supervisor module.
CitationDoucet, N., Gratadour, D., LTaief, H., Gendron, E., Sevin, A., Ferreira, F., … Keyes, D. (2017). Efficient Supervision Strategy for Tomographic AO Systems on E-ELT. Proceedings of the Adaptive Optics for Extremely Large Telescopes 5. doi:10.26698/ao4elt5.0099
SponsorsThis work is sponsored through a grant from project 671662, a.k.a. Green Flash, funded by European Commission under program H2020-EU.1.2.2 coordinated in H2020-FETHPC-2014. The authors would like to thank the Intel and NVIDIA vendors for their hardware donations and/or systems’ remote access, the Intel Parallel Computing Center and the NVIDIA GPU Research Center awarded to the Extreme Computing Research Center at KAUST
Conference/Event name5th Adaptive Optics for Extremely Large Telescopes, AO4ELT 2017