TY - GEN
T1 - Meeting the Real-Time Challenges of Ground-Based Telescopes Using Low-Rank Matrix Computations
AU - Ltaief, Hatem
AU - Cranney, Jesse
AU - Gratadour, Damien
AU - Hong, Yuxi
AU - Gatineau, Laurent
AU - Keyes, David
N1 - Publisher Copyright:
© 2021 IEEE Computer Society. All rights reserved.
PY - 2021/11/14
Y1 - 2021/11/14
N2 - 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.
AB - 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.
KW - Ground-Based Telescopes
KW - Matrix-Vector Multiplication.
KW - Real-Time Computational Astronomy
KW - Tile Low-Rank Approximation
UR - http://www.scopus.com/inward/record.url?scp=85119993532&partnerID=8YFLogxK
U2 - 10.1145/3458817.3476225
DO - 10.1145/3458817.3476225
M3 - Conference contribution
T3 - International Conference for High Performance Computing, Networking, Storage and Analysis, SC
BT - Proceedings of SC 2021
PB - IEEE Computer Society
T2 - 33rd International Conference for High Performance Computing, Networking, Storage and Analysis: Science and Beyond, SC 2021
Y2 - 14 November 2021 through 19 November 2021
ER -