High performance pseudo-analytical simulation of multi-object adaptive optics over multi-GPU systems

Handle URI:
http://hdl.handle.net/10754/564877
Title:
High performance pseudo-analytical simulation of multi-object adaptive optics over multi-GPU systems
Authors:
Abdelfattah, Ahmad ( 0000-0001-5054-4784 ) ; Gendron, Éric; Gratadour, Damien; Keyes, David E. ( 0000-0002-4052-7224 ) ; Ltaief, Hatem ( 0000-0002-6897-1095 ) ; Sevin, Arnaud; Vidal, Fabrice
Abstract:
Multi-object adaptive optics (MOAO) is a novel adaptive optics (AO) technique dedicated to the special case of wide-field multi-object spectrographs (MOS). It applies dedicated wavefront corrections to numerous independent tiny patches spread over a large field of view (FOV). The control of each deformable mirror (DM) is done individually using a tomographic reconstruction of the phase based on measurements from a number of wavefront sensors (WFS) pointing at natural and artificial guide stars in the field. The output of this study helps the design of a new instrument called MOSAIC, a multi-object spectrograph proposed for the European Extremely Large Telescope (E-ELT). We have developed a novel hybrid pseudo-analytical simulation scheme that allows us to accurately simulate in detail the tomographic problem. The main challenge resides in the computation of the tomographic reconstructor, which involves pseudo-inversion of a large dense symmetric matrix. The pseudo-inverse is computed using an eigenvalue decomposition, based on the divide and conquer algorithm, on multicore systems with multi-GPUs. Thanks to a new symmetric matrix-vector product (SYMV) multi-GPU kernel, our overall implementation scores significant speedups over standard numerical libraries on multicore, like Intel MKL, and up to 60% speedups over the standard MAGMA implementation on 8 Kepler K20c GPUs. At 40,000 unknowns, this appears to be the largest-scale tomographic AO matrix solver submitted to computation, to date, to our knowledge and opens new research directions for extreme scale AO simulations. © 2014 Springer International Publishing Switzerland.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Extreme Computing Research Center; Applied Mathematics and Computational Science Program
Publisher:
Springer Nature
Journal:
Euro-Par 2014 Parallel Processing
Conference/Event name:
20th International Conference on Parallel Processing, Euro-Par 2014
Issue Date:
1-Jan-2014
DOI:
10.1007/978-3-319-09873-9_59
Type:
Conference Paper
ISSN:
03029743
ISBN:
9783319098722
Appears in Collections:
Conference Papers; Applied Mathematics and Computational Science Program; Extreme Computing Research Center; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAbdelfattah, Ahmaden
dc.contributor.authorGendron, Éricen
dc.contributor.authorGratadour, Damienen
dc.contributor.authorKeyes, David E.en
dc.contributor.authorLtaief, Hatemen
dc.contributor.authorSevin, Arnauden
dc.contributor.authorVidal, Fabriceen
dc.date.accessioned2015-08-04T07:23:52Zen
dc.date.available2015-08-04T07:23:52Zen
dc.date.issued2014-01-01en
dc.identifier.isbn9783319098722en
dc.identifier.issn03029743en
dc.identifier.doi10.1007/978-3-319-09873-9_59en
dc.identifier.urihttp://hdl.handle.net/10754/564877en
dc.description.abstractMulti-object adaptive optics (MOAO) is a novel adaptive optics (AO) technique dedicated to the special case of wide-field multi-object spectrographs (MOS). It applies dedicated wavefront corrections to numerous independent tiny patches spread over a large field of view (FOV). The control of each deformable mirror (DM) is done individually using a tomographic reconstruction of the phase based on measurements from a number of wavefront sensors (WFS) pointing at natural and artificial guide stars in the field. The output of this study helps the design of a new instrument called MOSAIC, a multi-object spectrograph proposed for the European Extremely Large Telescope (E-ELT). We have developed a novel hybrid pseudo-analytical simulation scheme that allows us to accurately simulate in detail the tomographic problem. The main challenge resides in the computation of the tomographic reconstructor, which involves pseudo-inversion of a large dense symmetric matrix. The pseudo-inverse is computed using an eigenvalue decomposition, based on the divide and conquer algorithm, on multicore systems with multi-GPUs. Thanks to a new symmetric matrix-vector product (SYMV) multi-GPU kernel, our overall implementation scores significant speedups over standard numerical libraries on multicore, like Intel MKL, and up to 60% speedups over the standard MAGMA implementation on 8 Kepler K20c GPUs. At 40,000 unknowns, this appears to be the largest-scale tomographic AO matrix solver submitted to computation, to date, to our knowledge and opens new research directions for extreme scale AO simulations. © 2014 Springer International Publishing Switzerland.en
dc.publisherSpringer Natureen
dc.titleHigh performance pseudo-analytical simulation of multi-object adaptive optics over multi-GPU systemsen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentExtreme Computing Research Centeren
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.identifier.journalEuro-Par 2014 Parallel Processingen
dc.conference.date25 August 2014 through 29 August 2014en
dc.conference.name20th International Conference on Parallel Processing, Euro-Par 2014en
dc.conference.locationPortoen
dc.contributor.institutionLESIA, Observatoire de Paris, Universite Paris Diderot, Paris, Franceen
kaust.authorAbdelfattah, Ahmaden
kaust.authorKeyes, David E.en
kaust.authorLtaief, Hatemen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.