Accelerating magnetic induction tomography‐based imaging through heterogeneous parallel computing. (11th April 2019)

• Record Type: Journal Article
• Authors: Walker, David W.; Kramer, Stephan C.; Biebl, Fabian R. A.; Ledger, Paul D.; Brown, Malcolm
• Is Part Of: Concurrency and computation. Volume 31:Number 17(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-04-11
• Subjects: computational electromagnetics -- magnetic induction tomography -- parallel applications; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5265
• Languages: English
• ISSNs: 1532-0626

Abstract

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 withdeal.II, a widely‐used open source finite element library.