Real-time implementation of an iterative solver for atmospheric tomography

TL;DR

This paper presents a parallel, real-time implementation of an iterative atmospheric tomography solver for adaptive optics in extremely large telescopes, emphasizing performance optimization on CPUs and GPUs.

Contribution

It introduces an optimized, parallel implementation of the FEWHA iterative solver for atmospheric tomography suitable for ELT-sized systems.

Findings

Significant reduction in computation time with parallelization

Effective implementation on both CPUs and GPUs

Feasibility of real-time atmospheric tomography for ELTs

Abstract

The image quality of the new generation of earthbound Extremely Large Telescopes (ELTs) is heavily influenced by atmospheric turbulences. To compensate these optical distortions a technique called adaptive optics (AO) is used. Many AO systems require the reconstruction of the refractive index fluctuations in the atmosphere, called atmospheric tomography. The standard way of solving this problem is the Matrix Vector Multiplication, i.e., the direct application of a (regularized) generalized inverse of the system operator. However, over the last years the telescope sizes have increased significantly and the computational efficiency become an issue. Promising alternatives are iterative methods such as the Finite Element Wavelet Hybrid Algorithm (FEWHA), which is based on wavelets. Due to its efficient matrix-free representation of the underlying operators, the number of floating point operations and memory usage decreases significantly. In this paper, we focus on performance optimization techniques, such as parallel programming models, for the implementation of this iterative method on CPUs and GPUs. We evaluate the computational performance of our optimized, parallel version of FEWHA for ELT-sized test configurations.