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dc.contributor.authorAbdelfattah, Ahmad
dc.contributor.authorGendron, Éric
dc.contributor.authorGratadour, Damien
dc.contributor.authorKeyes, David E.
dc.contributor.authorLtaief, Hatem
dc.contributor.authorSevin, Arnaud
dc.contributor.authorVidal, Fabrice
dc.date.accessioned2015-08-04T07:23:52Z
dc.date.available2015-08-04T07:23:52Z
dc.date.issued2014-08-11
dc.identifier.citationAbdelfattah, A., Gendron, E., Gratadour, D., Keyes, D., Ltaief, H., Sevin, A., & Vidal, F. (2014). High Performance Pseudo-analytical Simulation of Multi-Object Adaptive Optics over Multi-GPU Systems. Euro-Par 2014 Parallel Processing, 704–715. doi:10.1007/978-3-319-09873-9_59
dc.identifier.isbn9783319098722
dc.identifier.issn03029743
dc.identifier.doi10.1007/978-3-319-09873-9_59
dc.identifier.urihttp://hdl.handle.net/10754/564877
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.
dc.publisherSpringer Nature
dc.titleHigh performance pseudo-analytical simulation of multi-object adaptive optics over multi-GPU systems
dc.typeConference Paper
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentExtreme Computing Research Center
dc.identifier.journalEuro-Par 2014 Parallel Processing
dc.conference.date25 August 2014 through 29 August 2014
dc.conference.name20th International Conference on Parallel Processing, Euro-Par 2014
dc.conference.locationPorto
dc.contributor.institutionLESIA, Observatoire de Paris, Universite Paris Diderot, Paris, France
kaust.personAbdelfattah, Ahmad
kaust.personKeyes, David E.
kaust.personLtaief, Hatem
dc.date.published-online2014-08-11
dc.date.published-print2014


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