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dc.contributor.authorCharara, Ali
dc.contributor.authorLtaief, Hatem
dc.contributor.authorGratadour, Damien
dc.contributor.authorKeyes, David E.
dc.contributor.authorSevin, Arnaud
dc.contributor.authorAbdelfattah, Ahmad
dc.contributor.authorGendron, Eric
dc.contributor.authorMorel, Carine
dc.contributor.authorVidal, Fabrice
dc.date.accessioned2015-08-24T09:27:15Z
dc.date.available2015-08-24T09:27:15Z
dc.date.issued2014-11
dc.identifier.doi10.1109/SC.2014.27
dc.identifier.urihttp://hdl.handle.net/10754/575827
dc.description.abstractThe European Extremely Large Telescope project (E-ELT) is one of Europe's highest priorities in ground-based astronomy. ELTs are built on top of a variety of highly sensitive and critical astronomical instruments. In particular, a new instrument called MOSAIC has been proposed to perform multi-object spectroscopy using the Multi-Object Adaptive Optics (MOAO) technique. The core implementation of the simulation lies in the intensive computation of a tomographic reconstruct or (TR), which is used to drive the deformable mirror in real time from the measurements. A new numerical algorithm is proposed (1) to capture the actual experimental noise and (2) to substantially speed up previous implementations by exposing more concurrency, while reducing the number of floating-point operations. Based on the Matrices Over Runtime System at Exascale numerical library (MORSE), a dynamic scheduler drives all computational stages of the tomographic reconstruct or simulation and allows to pipeline and to run tasks out-of order across different stages on heterogeneous systems, while ensuring data coherency and dependencies. The proposed TR simulation outperforms asymptotically previous state-of-the-art implementations up to 13-fold speedup. At more than 50000 unknowns, this appears to be the largest-scale AO problem submitted to computation, to date, and opens new research directions for extreme scale AO simulations. © 2014 IEEE.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectComputational Astronomy
dc.subjectDense Linear Algebra
dc.subjectDynamic Scheduler
dc.subjectGPU Computing
dc.subjectMulti-Objects Adaptive Optics
dc.titlePipelining Computational Stages of the Tomographic Reconstructor for Multi-Object Adaptive Optics on a Multi-GPU System
dc.typeConference Paper
dc.contributor.departmentExtreme Computing Research Center
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer Science Program
dc.identifier.journalSC14: International Conference for High Performance Computing, Networking, Storage and Analysis
dc.conference.date16 November 2014 through 21 November 2014
dc.conference.nameInternational Conference for High Performance Computing, Networking, Storage and Analysis, SC 2014
kaust.personLtaief, Hatem
kaust.personKeyes, David E.
kaust.personAbdelfattah, Ahmad
kaust.personCharara, Ali


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