Outsmarting the Atmospheric Turbulence for Ground-Based Telescopes Using the Stochastic Levenberg-Marquardt Method

Yuxi Hong, El Houcine Bergou, Nicolas Doucet, Hao Zhang, Jesse Cranney, Hatem Ltaief, Damien Gratadour, Francois Rigaut, David Keyes*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

One of the main challenges for ground-based optical astronomy is to compensate for atmospheric turbulence in near real-time. The goal is to obtain images as close as possible to the diffraction limit of the telescope. This challenge is addressed on the latest generation of giant optical telescopes by deploying multi-conjugate adaptive optics (MCAO) systems performing predictive tomography of the turbulence and multi-layer compensation. Such complex systems require a high fidelity estimate of the turbulence profile above the telescope, to be updated regularly during operations as turbulence conditions evolve. In this paper, we modify the traditional Levenberg-Marquardt (LM) algorithm by considering stochastically chosen subsystems of the full problem to identify the required parameters efficiently, while coping with the real-time challenge. While LM operates on the full set data samples, the resulting Stochastic LM (SLM) method randomly selects subsamples to compute corresponding approximate gradients and Hessians. Hence, SLM reduces the algorithmic complexity per iteration and shortens the overall time to solution, while maintaining LM’s numerical robustness. We present a new convergence analysis for SLM, implement the algorithm with optimized GPU kernels, and deploy it on shared-memory systems with multiple GPU accelerators. We assess SLM in the adaptive optics system configurations in the context of the MCAO-Assisted Visible Imager & Spectrograph (MAVIS) instrument for the Very Large Telescope (VLT). We demonstrate performance superiority of SLM over the traditional LM algorithm and the classical stochastic first-order methods. At the scale of VLT AO, SLM finishes the optimization process and accurately retrieves the parameters (e.g., turbulence strength and wind speed profiles) in less than a second using up to eight NVIDIA A100 GPUs, which permits high acuity real-time throughput over a night of observations.

Original languageEnglish
Title of host publicationEuro-Par 2021
Subtitle of host publicationParallel Processing - 27th International Conference on Parallel and Distributed Computing, Proceedings
EditorsLeonel Sousa, Nuno Roma, Pedro Tomás
PublisherSpringer Science and Business Media Deutschland GmbH
Pages565-579
Number of pages15
ISBN (Print)9783030856649
DOIs
Publication statusPublished - 2021
Event27th International European Conference on Parallel and Distributed Computing, Euro-Par 2021 - Lisbon, Portugal
Duration: 1 Sept 20213 Sept 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12820 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference27th International European Conference on Parallel and Distributed Computing, Euro-Par 2021
Country/TerritoryPortugal
CityLisbon
Period1/09/213/09/21

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