Abstract
METIS, the Mid-infrared ELT Imager and Spectrograph, will operate an internal Single Conjugate Adaptive Optics (SCAO) system, which will mainly serve the science cases targeting exoplanets and disks around bright stars. The Extremely Large Telescope (ELT) is expected to have its first light in 2028, and the entire instrument recently passed its final design phase. The adaptive optics (AO) of METIS SCAO is designed to correct for atmospheric distortions, and is essential for diffraction-limited observations with METIS. The computational and data transfer requirements for these next generation ELT AO Real-Time Computers (RTCs) are enormous, and require advanced data processing and pipelining techniques. METIS SCAO will use a pyramid wavefront sensor (WFS), which captures incoming wavefronts at 1 kHz with a raw throughput of 148 MB/s. The RTC will ingest these WFS images on a frame-by-frame basis, compute the corrections and send them to the deformable mirror M4 and the tip/tilt mirror M5. The RTC is split up into two distinct systems: the Hard Real-Time Computer (HRTC) and the Soft Real-Time Computer (SRTC). The HRTC is responsible for computing the time sensitive wavefront control loop, while the SRTC is responsible for supervising and optimising the HRTC. A working prototype for the HRTC has been completed and operates with an RTC computation time of roughly 372 mu s. This computation is memory limited and runs on two NVIDIA A100 GPUs. This paper shows a breakdown of the HRTC on a CUDA kernel level, focusing on the tasks that run on the GPUs. We also present the performance of the HRTC and possible improvements for it.

Abstract
Context. The deployment of meter-scale (hitherto pre-focal) adaptive deformable mirrors finds some prominent examples in the leading ground-based visible to near-infrared facilities (e.g. the Very Large Telescope (VLT), the Large Binocular Telescope (LBT), or the Magellan Telescope) and is being adopted by several others (e.g. the Multiple Mirror Telescope (MMT) or Subaru). Furthermore, two out of the three giant segmented-mirror telescopes now under design will feature them. In all these cases, the proprietary technology is based on voice-coils and is limited in force, stroke, and velocity. Aims. Because of the nature of their purpose, that is, adaptive wave-front correction, any kind of optimality relies on the control of a subset of principal wave-front components or eigenmodes, for short, a basis of functions in a mathematical sense. Here we provide algorithmic procedures for generating such eigenbases, also called Karhunen–Loève (KL) modes, that integrate force limitations in their definitions whilst maintaining standard orthonormality, statistical independence, and deformable mirror span. Methods. The double-diagonalisation method was revisited to build KL modes ranked by the force applied on the actuators. Results. We analysed this new KL basis for von Kármán turbulence statistics and present the fitting error and the distribution of positions and forces. We further illustrate their use in the case of the quaternary mirror control for the European Extremely Large Telescope, and we include the outer actuator minioning and force policy constraints. © The Authors 2024.