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

Ahmad Abdelfattah, Éric Gendron, Damien Gratadour, David E. Keyes, Hatem Ltaief, Arnaud Sevin, Fabrice Vidal

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

3 Scopus citations

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.
Original languageEnglish (US)
Title of host publicationEuro-Par 2014 Parallel Processing
PublisherSpringer Nature
Pages704-715
Number of pages12
ISBN (Print)9783319098722
DOIs
StatePublished - Aug 11 2014

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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