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@article{Walker:2019:10.1002/cpe.5265,
author = {Walker, DW and Kramer, SC and Biebl, FRA and Ledger, PD and Brown, M},
doi = {10.1002/cpe.5265},
journal = {Concurrency Computation},
title = {Accelerating magnetic induction tomography-based imaging through heterogeneous parallel computing},
url = {http://dx.doi.org/10.1002/cpe.5265},
year = {2019}
}

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TY  - JOUR
AB  - © 2019 John Wiley  &  Sons, Ltd. 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.
AU  - Walker,DW
AU  - Kramer,SC
AU  - Biebl,FRA
AU  - Ledger,PD
AU  - Brown,M
DO  - 10.1002/cpe.5265
PY  - 2019///
SN  - 1532-0626
TI  - Accelerating magnetic induction tomography-based imaging through heterogeneous parallel computing
T2  - Concurrency Computation
UR  - http://dx.doi.org/10.1002/cpe.5265
ER  -

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Stephan Kramer

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