Lange, M., Palamara, S., Lassila, T. orcid.org/0000-0001-8947-1447 et al. (3 more authors) (2016) Improved Hybrid/GPU Algorithm for Solving Cardiac Electrophysiology Problems on Purkinje Networks. International Journal for Numerical Methods in Biomedical Engineering. ISSN 2040-7939
Abstract
Cardiac Purkinje fibres provide an important pathway to the coordinated contraction of the heart. We present a numerical algorithm for the solution of electrophysiology problems across the Purkinje network that is efficient enough to be used in in-silico studies on realistic Purkinje networks with physiologically detailed models of ion exchange at the cell membrane. The algorithm is based on operator splitting and is provided with three different implementations: pure CPU, hybrid CPU/GPU, and pure GPU. Compared to our previous work, we modify the explicit gap junction term at network bifurcations in order to improve its mathematical consistency. Due to this improved consistency of the model, we are able to perform an empirical convergence study against analytical solutions. The study verified that all three implementations produce equivalent convergence rates, which shows that the algorithm produces equivalent result across different hardware platforms. Finally, we compare the efficiency of all three implementations on Purkinje networks of increasing spatial resolution using membrane models of increasing complexity. Both hybrid and pure-GPU implementations outperform the pure-CPU implementation, but their relative performance difference depends on the size of the Purkinje network and the complexity of the membrane model used.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 Wiley. This is an author produced version of a paper subsequently published in International Journal for Numerical Methods in Biomedical Engineering. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | cardiac electrophysiology; Purkinje networks; graphics processing units; hybrid algorithms |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 19 Sep 2016 10:11 |
Last Modified: | 28 Sep 2017 11:20 |
Published Version: | http://dx.doi.org/10.1002/cnm.2835 |
Status: | Published |
Publisher: | Wiley |
Refereed: | Yes |
Identification Number: | 10.1002/cnm.2835 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:104791 |