MAORY RTC, a status update

Baruffolo, A.; Foppiani, Italo; Agapito, Guido; Plantet, Cédric; Busoni, Lorenzo; Cosentino, Giuseppe; Baronchelli, Ivano; Arcidiacono, Carmelo; Feautrier, Philippe; Ciliegi, P.; Esposito, Simone; Ragazzoni, Roberto; Biasi, Roberto; Manetti, Mauro; Véran, Jean-Pierre; Kerley, Daniel A.; Smith, Malcom; Dunn, Jennifer

doi:10.1117/12.2561585

Cited by 1 publication

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“…We ultimately assess its impact along with the accuracy threshold on the numerical accuracy of the output image. Although we primarily focus on MAVIS, our TLR-MVM implementation can be used across a wide range of AO instruments [8,14,21].…”

Section: Introductionmentioning

confidence: 99%

Meeting the real-time challenges of ground-based telescopes using low-rank matrix computations

Ltaief

Cranney

Gratadour

et al. 2021

Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

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Adaptive Optics (AO) is a technology that permits to measure and mitigate the distortion effects of atmospheric turbulence on optical beams. AO must operate in real-time by controlling thousands of actuators to shape the surface of deformable mirrors deployed on ground-based telescopes to compensate for these distortions. The command vectors that trigger how each individual actuator should act to bend a portion of the mirror are obtained from Matrix-Vector Multiplications (MVM). We identify and leverage the data sparsity structure of these control matrices coming from the MAVIS instruments for the European Southern Observatory's Very Large Telescope. We provide performance evaluation on x86 and acceleratorbased systems. We present the impact of tile low-rank (TLR) matrix approximations on time-to-solution for the MVM and assess the produced image quality. We achieve performance improvement up to two orders of magnitude for TLR-MVM compared to regular dense MVM, while maintaining the image quality.

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Section: Introductionmentioning

confidence: 99%