Dimensioning the MAORY real time computer

Schreiber, Laura; Diolaiti, Emiliano; Arcidiacono, Carmelo; Baruffolo, A.; Bregoli, Giovanni; Cascone, E.; Cosentino, Giuseppe; Esposito, Simone; Felini, Corrado; Foppiani, Italo; Ciliegi, P.; Feautrier, Philippe; Torroni, Paolo

doi:10.1117/12.2231527

Cited by 6 publications

(6 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Extremely Large Telescope (ELT), which is currently built by the European Southern Observatory (ESO), will become the largest optical/near-infrared telescope in the world and will feature several instruments. One of these instruments is the Multi-conjugate Adaptive Optics RelaY (MAORY) [1], which acts as an adaptive optics (AO) module. MAORY will not make observations by itself, rather it will enable other instruments, such as the imaging camera MI-CADO, to take images with an exceptional quality.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance of an iterative wavelet reconstructor for the Multi-conjugate Adaptive Optics RelaY of ESO's ELT

Stadler¹,

Ramlau²

2022

Preprint

View full text Add to dashboard Cite

The Multi-conjugate Adaptive Optics RelaY (MAORY) is one of the key Adaptive Optics (AO) systems on the European Southern Observatory's Extremely Large Telescope. MAORY aims to achieve a good wavefront correction over a large field of view, which involves a tomographic estimation of the 3D atmospheric wavefront disturbance. Mathematically, the reconstruction of turbulent layers in the atmosphere is severely ill-posed, hence, limits the achievable reconstruction accuracy. Moreover, the reconstruction has to be performed in real-time at a few hundred to one thousand Hertz frame rates. Huge amounts of data have to be processed and thousands of actuators of the deformable mirrors have to be controlled by elaborated algorithms. Even with extensive parallelization and pipelining, direct solvers, such as the Matrix Vector Multiplication (MVM) method, are extremely demanding. Thus, research in the last years shifted into the direction of iterative methods. In this paper we focus on the iterative Finite Element Wavelet Hybrid Algorithm (FEWHA). The key feature of FEWHA is a matrix-free representation of all operators involved, which makes the algorithm fast and enables on the fly system updates whenever parameters at the telescope or in the atmosphere change. We provide a performance analysis of the method regarding quality and run-time for the MAORY instrument using the AO software package COMPASS.

show abstract

Section: Introductionmentioning

confidence: 99%

“…We provide a detailed study of the quality as well as the run-time on real-time hardware. Real-time implementations for ESO's ELT AO system have been previously studied [32,33,1,34,35,36]. Suitable architectures have been evaluated within the Greenflash project [37,38].…”

Section: Introductionmentioning

confidence: 99%

Performance of an iterative wavelet reconstructor for the Multi-conjugate Adaptive Optics RelaY of ESO's ELT

Stadler¹,

Ramlau²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In this paper, we focus on the computational performance of FEWHA on different real-time hardware. Real-time implementations for ELT AO systems are also studied in [2,23,37,5,44,8]. Possible real-time architectures have been evaluated within the Greenflash project, see [15,16].…”

Section: Introductionmentioning

confidence: 99%

“…We demonstrate the performance of our implementations on the test configuration of MAORY, which is an adaptive optics module for the ELT operating in MCAO. The MAORY real-time computer is discussed in [37].…”

Section: Introductionmentioning

confidence: 99%

Real-time implementation of an iterative solver for atmospheric tomography

Stadler¹,

Biasi²,

Manetti³

et al. 2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…While it seems to adequately perform with the scale of the Very Large Telescope Adaptive Optics Facilities (VLT AOF), the requirements and performance obtained in terms of time-to-solution to produce the new reconstructor is either not addressed or not sufficient to answer the ELT dimensioning challenge. Additionally, recent work on the Real Time Controller (RTC) dimensioning of various tomographic AO concepts for the E-ELT 5,6 ) lacks of a proper analysis of how the image quality specification can map on both hardware and optimized software for the RTC.…”

Section: Introductionmentioning

confidence: 99%

Efficient Supervision Strategy for Tomographic AO Systems on E-ELT

Doucet¹,

Gratadour²,

Ltaief³

et al. 2017

Proceedings of the Adaptive Optics for Extremely Large Telescopes 5

View full text Add to dashboard Cite

A critical subsystem of the tomographic AO RTC is the supervisor module. Its role is to feed the challenging real-time data pipeline with a new reconstructor matrix at a regular rate, computed from a statistical analysis of the measurements, to optimize the performance of the AO system. This process involves solving a system of linear equations defined by the covariance matrix of the wave front sensors' measurements, the size of which may be up to 90k×90k for the E-ELT's first light instruments using tomographic AO modules. The computational load for the solver of this dense symmetric matrix system is quite significant at this scale but can be efficiently handled using state-of-the-art energy-efficient manycore x86 or accelerator-based architectures, such as KNLs or GPUs, respectively. As part of the Green Flash project, we develop a supervisor module and demonstrate its portability by deploying it on each aforementioned hardware system. Finally, we describe different implementations and their trade-offs in terms of performance and accuracy and show preliminary results on the possible impact of hierarchically low-rank approximation methods on the overall supervisor module.

show abstract

Dimensioning the MAORY real time computer

Cited by 6 publications

References 1 publication

Performance of an iterative wavelet reconstructor for the Multi-conjugate Adaptive Optics RelaY of ESO's ELT

Performance of an iterative wavelet reconstructor for the Multi-conjugate Adaptive Optics RelaY of ESO's ELT

Real-time implementation of an iterative solver for atmospheric tomography

Efficient Supervision Strategy for Tomographic AO Systems on E-ELT

Contact Info

Product

Resources

About