Journal article 843 views 206 downloads
Accelerating magnetic induction tomography‐based imaging through heterogeneous parallel computing
David W. Walker,
Stephan C. Kramer,
Fabian R. A. Biebl,
Paul Ledger,
Malcolm Brown
Concurrency and Computation: Practice and Experience, Volume: 31, Issue: 17, Start page: e5265
Swansea University Author: Paul Ledger
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DOI (Published version): 10.1002/cpe.5265
Abstract
Magnetic Induction Tomography (MIT) is a non‐invasive imaging technique, which has applications in both industrial and clinical settings. In essence, it is capable of reconstructing the electromagnetic parameters of an object from measurements made on its surface. With the exploitation of parallelis...
Published in: | Concurrency and Computation: Practice and Experience |
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ISSN: | 1532-0626 1532-0634 |
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2019
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URI: | https://cronfa.swan.ac.uk/Record/cronfa49975 |
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2020-12-09T11:31:56.3899339 v2 49975 2019-04-12 Accelerating magnetic induction tomography‐based imaging through heterogeneous parallel computing 068dd31af167bcda33878951b2a01e97 Paul Ledger Paul Ledger true false 2019-04-12 FGSEN Magnetic Induction Tomography (MIT) is a non‐invasive imaging technique, which has applications in both industrial and clinical settings. In essence, it is capable of reconstructing the electromagnetic parameters of an object from measurements made on its surface. With the exploitation of parallelism, it is possible to achieve high quality inexpensive MIT images for biomedical applications on clinically relevant time scales. In this paper we investigate the performance of different parallel implementations of the forward eddy current problem, which is the main computational component of the inverse problem through which measured voltages are converted into images. We show that a heterogeneous parallel method that exploits multiple CPUs and GPUs can provide a high level of parallel scaling, leading to considerably improved runtimes. We also show how multiple GPUs can be used in conjunction with deal.II, a widely‐used open source finite element library. Journal Article Concurrency and Computation: Practice and Experience 31 17 e5265 1532-0626 1532-0634 31 12 2019 2019-12-31 10.1002/cpe.5265 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University 2020-12-09T11:31:56.3899339 2019-04-12T09:23:06.2495450 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised David W. Walker 1 Stephan C. Kramer 2 Fabian R. A. Biebl 3 Paul Ledger 4 Malcolm Brown 5 0049975-12042019145926.pdf walker2019.pdf 2019-04-12T14:59:26.9300000 Output 957345 application/pdf Accepted Manuscript true 2020-04-11T00:00:00.0000000 true eng |
title |
Accelerating magnetic induction tomography‐based imaging through heterogeneous parallel computing |
spellingShingle |
Accelerating magnetic induction tomography‐based imaging through heterogeneous parallel computing Paul Ledger |
title_short |
Accelerating magnetic induction tomography‐based imaging through heterogeneous parallel computing |
title_full |
Accelerating magnetic induction tomography‐based imaging through heterogeneous parallel computing |
title_fullStr |
Accelerating magnetic induction tomography‐based imaging through heterogeneous parallel computing |
title_full_unstemmed |
Accelerating magnetic induction tomography‐based imaging through heterogeneous parallel computing |
title_sort |
Accelerating magnetic induction tomography‐based imaging through heterogeneous parallel computing |
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068dd31af167bcda33878951b2a01e97 |
author_id_fullname_str_mv |
068dd31af167bcda33878951b2a01e97_***_Paul Ledger |
author |
Paul Ledger |
author2 |
David W. Walker Stephan C. Kramer Fabian R. A. Biebl Paul Ledger Malcolm Brown |
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Journal article |
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Concurrency and Computation: Practice and Experience |
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31 |
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17 |
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e5265 |
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Swansea University |
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10.1002/cpe.5265 |
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Faculty of Science and Engineering |
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description |
Magnetic Induction Tomography (MIT) is a non‐invasive imaging technique, which has applications in both industrial and clinical settings. In essence, it is capable of reconstructing the electromagnetic parameters of an object from measurements made on its surface. With the exploitation of parallelism, it is possible to achieve high quality inexpensive MIT images for biomedical applications on clinically relevant time scales. In this paper we investigate the performance of different parallel implementations of the forward eddy current problem, which is the main computational component of the inverse problem through which measured voltages are converted into images. We show that a heterogeneous parallel method that exploits multiple CPUs and GPUs can provide a high level of parallel scaling, leading to considerably improved runtimes. We also show how multiple GPUs can be used in conjunction with deal.II, a widely‐used open source finite element library. |
published_date |
2019-12-31T04:01:16Z |
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1763753146247544832 |
score |
11.021648 |