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Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 824569, 13 pages
http://dx.doi.org/10.1155/2012/824569
Research Article

Simulations of Complex and Microscopic Models of Cardiac Electrophysiology Powered by Multi-GPU Platforms

1Computational Modeling, Federal University of Juiz de Fora, 36036-900 Juiz de Fora, MG, Brazil
2Computer Science, Federal University of São João del-Rei, 36307-352 São João del-Rei, MG, Brazil
3Computer Science, Federal University of Minas Gerais, 31270-901 Belo Horizonte, MG, Brazil

Received 3 August 2012; Revised 28 September 2012; Accepted 1 October 2012

Academic Editor: Ling Xia

Copyright © 2012 Bruno Gouvêa de Barros et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Key aspects of cardiac electrophysiology, such as slow conduction, conduction block, and saltatory effects have been the research topic of many studies since they are strongly related to cardiac arrhythmia, reentry, fibrillation, or defibrillation. However, to reproduce these phenomena the numerical models need to use subcellular discretization for the solution of the PDEs and nonuniform, heterogeneous tissue electric conductivity. Due to the high computational costs of simulations that reproduce the fine microstructure of cardiac tissue, previous studies have considered tissue experiments of small or moderate sizes and used simple cardiac cell models. In this paper, we develop a cardiac electrophysiology model that captures the microstructure of cardiac tissue by using a very fine spatial discretization (8 μm) and uses a very modern and complex cell model based on Markov chains for the characterization of ion channel’s structure and dynamics. To cope with the computational challenges, the model was parallelized using a hybrid approach: cluster computing and GPGPUs (general-purpose computing on graphics processing units). Our parallel implementation of this model using a multi-GPU platform was able to reduce the execution times of the simulations from more than 6 days (on a single processor) to 21 minutes (on a small 8-node cluster equipped with 16 GPUs, i.e., 2 GPUs per node).