Accelerating magnetic induction tomography‐based imaging through heterogeneous parallel computing

Walker, David W.; Kramer, Stephan C.; Biebl, Fabian R. A.; Ledger, Paul D.; Brown, Malcolm

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 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.

Citation

Walker, D. W., Kramer, S. C., Biebl, F. R. A., Ledger, P. D., & Brown, M. (in press). Accelerating magnetic induction tomography‐based imaging through heterogeneous parallel computing. Concurrency and Computation: Practice and Experience, e5265. https://doi.org/10.1002/cpe.5265

• Journal Article Type: Article
• Acceptance Date: Mar 7, 2019
• Online Publication Date: Apr 11, 2019
• Deposit Date: Jun 12, 2023
• Journal: Concurrency and Computation: Practice and Experience
• Print ISSN: 1532-0626
• Electronic ISSN: 1532-0634
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Pages: e5265
• DOI: https://doi.org/10.1002/cpe.5265
• Keywords: Computational Theory and Mathematics; Computer Networks and Communications; Computer Science Applications; Theoretical Computer Science; Software