Outsmarting the Atmospheric Turbulence for Ground-Based Telescopes Using the Stochastic Levenberg-Marquardt Method
Bergou, El Houcine
Keyes, David E.
KAUST DepartmentComputer Science
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Extreme Computing Research Center
Applied Mathematics and Computational Science Program
Office of the President
Online Publication Date2021-08-25
Print Publication Date2021
Embargo End Date2022-05-02
Permanent link to this recordhttp://hdl.handle.net/10754/669020
MetadataShow full item record
AbstractOne 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.
CitationHong, Y., Bergou, E. H., Doucet, N., Zhang, H., Cranney, J., Ltaief, H., … Keyes, D. (2021). Outsmarting the Atmospheric Turbulence for Ground-Based Telescopes Using the Stochastic Levenberg-Marquardt Method. Lecture Notes in Computer Science, 565–579. doi:10.1007/978-3-030-85665-6_35
SponsorsThe authors would like to thank NVIDIA for remote hardware access.
PublisherSpringer International Publishing
Conference/Event name27th International European Conference on Parallel and Distributed Computing, Euro-Par 2021