Pipelining Computational Stages of the Tomographic Reconstructor for Multi-Object Adaptive Optics on a Multi-GPU System
Type
Conference PaperAuthors
Charara, Ali
Ltaief, Hatem

Gratadour, Damien
Keyes, David E.

Sevin, Arnaud
Abdelfattah, Ahmad

Gendron, Eric
Morel, Carine
Vidal, Fabrice
KAUST Department
Extreme Computing Research CenterComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Applied Mathematics and Computational Science Program
Computer Science Program
Date
2014-11Permanent link to this record
http://hdl.handle.net/10754/575827
Metadata
Show full item recordAbstract
The 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.Citation
Charara, A., Ltaief, H., Gratadour, D., Keyes, D., Sevin, A., Abdelfattah, A., … Vidal, F. (2014). Pipelining Computational Stages of the Tomographic Reconstructor for Multi-Object Adaptive Optics on a Multi-GPU System. SC14: International Conference for High Performance Computing, Networking, Storage and Analysis. doi:10.1109/sc.2014.27Journal
SC14: International Conference for High Performance Computing, Networking, Storage and AnalysisConference/Event name
International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2014ae974a485f413a2113503eed53cd6c53
10.1109/SC.2014.27