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18 July 2018 Design and performance of a scalable GPU-based AO RTC prototype
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Abstract
In the context of the Green Flash project we have assembled a full scale demonstrator for an E-ELT first light AO RTC, based on the GPU technology. Such facility, designed to drive in real-time the AO system, is composed of a real-time core, processing streaming data from sensors and controlling deformable optics, and a supervisor module, optimizing the control loop by providing updated versions of the control matrix at a regular rate depending on the observing conditions evolution. This RTC prototype is designed to assess the system performance in various configurations from single conjugate AO for the E-ELT, i.e. about 10 Gb/s of streaming data from a single sensor and a required performance of about 100 GMAC/s; to the dimensioning of a MCAO system with 100 Gb/s of streaming data and 1.5 TMAC/s performance. Both concepts rely on the same architecture, the latter being a scaled version of the former. We chose a very low level approach using a persistent kernel strategy on the GPUs to handle all the computation steps including pixel calibration, slopes and command vector computation. This approach simplifies the latency management by reducing the communication but led us to re-implement some GPUs standard features : communication mechanisms (guard, peer-to-peer), algorithms (generalized matrix-vector multiplication, reduce/all reduce) and new synchronization mechanisms on a multi node - multi GPUs system. In order to assess the performance of the full AO RTC prototype under realistic conditions, we have concurrently implemented a real-time simulator able to feed the real-time core with data by emulating the sensors data transfer protocols and interacting with simulated deformable optics. The real-time simulator is able to deliver high precision simulated data and simulate the whole retro-action loop for the SCAO case, enabling a full scale / full feature test of the prototype. In this paper, we report on the design and characterization the AO RTC prototype performance in SCAO mode and discuss a strategy for its integration in tmographic AO mode.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Julien Bernard, Denis Perret, Arnaud Sevin, Maxime Laine, Tristan Buey, and Damien Gratadour "Design and performance of a scalable GPU-based AO RTC prototype", Proc. SPIE 10703, Adaptive Optics Systems VI, 107034B (18 July 2018); https://doi.org/10.1117/12.2313196
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