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Accelerating magnetic induction tomography‐based imaging through heterogeneous parallel computing
Author(s) -
Walker David W.,
Kramer Stephan C.,
Biebl Fabian R. A.,
Ledger Paul D.,
Brown Malcolm
Publication year - 2019
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5265
Subject(s) - computer science , inverse problem , eddy current , parallelism (grammar) , exploit , computational science , parallel computing , inverse , implementation , physics , mathematical analysis , geometry , mathematics , computer security , quantum mechanics , programming language
Summary 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.