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Distributed and parallel sparse convex optimization for radio interferometry with PURIFY

Distributed and parallel sparse convex optimization for radio interferometry with PURIFY
Distributed and parallel sparse convex optimization for radio interferometry with PURIFY
Next generation radio interferometric telescopes are entering an era of big data with extremely large data sets. While these telescopes can observe the sky in higher sensitivity and resolution than before, computational challenges in image reconstruction need to be overcome to realize the potential of forthcoming telescopes. New methods in sparse image reconstruction and convex optimization techniques (cf. compressive sensing) have shown to produce higher fidelity reconstructions of simulations and real observations than traditional methods. This article presents distributed and parallel algorithms and implementations to perform sparse image reconstruction, with significant practical considerations that are important for implementing these algorithms for Big Data. We benchmark the algorithms presented, showing that they are considerably faster than their serial equivalents. We then pre-sample gridding kernels to scale the distributed algorithms to larger data sizes, showing application times for 1 Gb to 2.4 Tb data sets over 25 to 100 nodes for up to 50 billion visibilities, and find that the run-times for the distributed algorithms range from 100 milliseconds to 3 minutes per iteration. This work presents an important step in working towards computationally scalable and efficient algorithms and implementations that are needed to image observations of both extended and compact sources from next generation radio interferometers such as the SKA. The algorithms are implemented in the latest versions of the SOPT (https://github.com/astro-informatics/sopt) and PURIFY (https://github.com/astro-informatics/purify) software packages {(Versions 3.1.0)}, which have been released alongside of this article.
astro-ph.IM
Pratley, Luke
ef3709da-0dac-4ce5-bf00-ac3dc2384f9e
McEwen, Jason D.
64c6269a-fe40-41d7-8b0c-d3c9ad920175
d'Avezac, Mayeul
a0960f68-b43a-4fde-950b-e6be14b59656
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Perez-Suarez, David
a36db022-9b74-4adf-b226-633a96e6212f
Christidi, Ilektra
26873470-827a-4d30-8d8a-f804efcf536f
Guichard, Roland
43b36cab-645f-47ff-af1d-d1c8111d276a
Pratley, Luke
ef3709da-0dac-4ce5-bf00-ac3dc2384f9e
McEwen, Jason D.
64c6269a-fe40-41d7-8b0c-d3c9ad920175
d'Avezac, Mayeul
a0960f68-b43a-4fde-950b-e6be14b59656
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Perez-Suarez, David
a36db022-9b74-4adf-b226-633a96e6212f
Christidi, Ilektra
26873470-827a-4d30-8d8a-f804efcf536f
Guichard, Roland
43b36cab-645f-47ff-af1d-d1c8111d276a

Pratley, Luke, McEwen, Jason D., d'Avezac, Mayeul, Cai, Xiaohao, Perez-Suarez, David, Christidi, Ilektra and Guichard, Roland (2019) Distributed and parallel sparse convex optimization for radio interferometry with PURIFY. arXiv.

Record type: Article

Abstract

Next generation radio interferometric telescopes are entering an era of big data with extremely large data sets. While these telescopes can observe the sky in higher sensitivity and resolution than before, computational challenges in image reconstruction need to be overcome to realize the potential of forthcoming telescopes. New methods in sparse image reconstruction and convex optimization techniques (cf. compressive sensing) have shown to produce higher fidelity reconstructions of simulations and real observations than traditional methods. This article presents distributed and parallel algorithms and implementations to perform sparse image reconstruction, with significant practical considerations that are important for implementing these algorithms for Big Data. We benchmark the algorithms presented, showing that they are considerably faster than their serial equivalents. We then pre-sample gridding kernels to scale the distributed algorithms to larger data sizes, showing application times for 1 Gb to 2.4 Tb data sets over 25 to 100 nodes for up to 50 billion visibilities, and find that the run-times for the distributed algorithms range from 100 milliseconds to 3 minutes per iteration. This work presents an important step in working towards computationally scalable and efficient algorithms and implementations that are needed to image observations of both extended and compact sources from next generation radio interferometers such as the SKA. The algorithms are implemented in the latest versions of the SOPT (https://github.com/astro-informatics/sopt) and PURIFY (https://github.com/astro-informatics/purify) software packages {(Versions 3.1.0)}, which have been released alongside of this article.

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More information

Published date: 11 March 2019
Additional Information: 25 pages, 5 figures
Keywords: astro-ph.IM

Identifiers

Local EPrints ID: 438776
URI: http://eprints.soton.ac.uk/id/eprint/438776
PURE UUID: 844144ae-982c-4f20-8c23-4a0a6e9a6bb5

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Date deposited: 24 Mar 2020 17:30
Last modified: 13 Jul 2020 16:39

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Contributors

Author: Luke Pratley
Author: Jason D. McEwen
Author: Mayeul d'Avezac
Author: Xiaohao Cai
Author: David Perez-Suarez
Author: Ilektra Christidi
Author: Roland Guichard

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