Meeting the Real-Time Challenges of Ground-Based Telescopes Using Low-Rank Matrix Computations

Authors: Hatem Ltaief (King Abdullah University of Science and Technology (KAUST)); Jesse Cranney (Australian National University); Damien Gratadour (Australian National University, Paris Observatory); Yuxi Hong (King Abdullah University of Science and Technology (KAUST)); Laurent Gatineau (NEC Corporation); and David Keyes (King Abdullah University of Science and Technology (KAUST))

Abstract: 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 a real-time environment by controlling thousands of actuators to shape the surface of deformable mirrors deployed on ground-based telescopes to compensate for the distortions. The command vectors that trigger how each individual actuator should act to tilt 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 accelerator-based 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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