Use this URL to cite or link to this record in EThOS: | https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.745139 |
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Title: | Reducing adaptive optics latency using many-core processors | ||||||
Author: | Barr, David |
ISNI:
0000 0004 7232 3802
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Awarding Body: | Heriot-Watt University | ||||||
Current Institution: | Heriot-Watt University | ||||||
Date of Award: | 2017 | ||||||
Availability of Full Text: |
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Abstract: | |||||||
Atmospheric turbulence reduces the achievable resolution of ground based optical telescopes. Adaptive optics systems attempt to mitigate the impact of this turbulence and are required to update their corrections quickly and deterministically (i.e. in realtime). The technological challenges faced by the future extremely large telescopes (ELTs) and their associated instruments are considerable. A simple extrapolation of current systems to the ELT scale is not sufficient. My thesis work consisted in the identification and examination of new many-core technologies for accelerating the adaptive optics real-time control loop. I investigated the Mellanox TILE-Gx36 and the Intel Xeon Phi (5110p). The TILE-Gx36 with 4x10 GbE ports and 36 processing cores is a good candidate for fast computation of the wavefront sensor images. The Intel Xeon Phi with 60 processing cores and high memory bandwidth is particularly well suited for the acceleration of the wavefront reconstruction. Through extensive testing I have shown that the TILE-Gx can provide the performance required for the wavefront processing units of the ELT first light instruments. The Intel Xeon Phi (Knights Corner) while providing good overall performance does not have the required determinism. We believe that the next generation of Xeon Phi (Knights Landing) will provide the necessary determinism and increased performance. In this thesis, we show that by using currently available novel many-core processors it is possible to reach the performance required for ELT instruments.
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Supervisor: | Schwartz, Noah ; Thomson, Robert | Sponsor: | Not available | ||||
Qualification Name: | Thesis (Eng.D.) | Qualification Level: | Doctoral | ||||
EThOS ID: | uk.bl.ethos.745139 | DOI: | Not available | ||||
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