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Accelerating sino-atrium computer simulations with graphic processing units

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 4th International Conference on Biomedical Engineering and Biotechnology, 18–21 August 2015, Shanghai, China

Guest editors: Feng Liu, Dong-Hoon Lee, Ricardo Lagoa and Sandeep Kumar

Article type: Research Article

Authors: Zhang, Honga; * | Xiao, Zhenga | Lin, Shien-fongb

Affiliations: [a] School of Electrical Engineering, Xi’an Jiaotong University, 710049, Xi’an, China | [b] Institute of Biomedical Engineering, National Chiao-Tung University, Hsinchu, Taiwan

Correspondence: [*] Address for correspondence: Hong Zhang, School of Electrical Engineering, Xi’an Jiaotong University, No. 28 Xianning West Road, Xi’an, 710049, China. Tel.: +86 29 82668630; Fax: 82668630; E-mail: [email protected].

Keywords: Sino-atrial node, atrium, graphic processing units, optimization, computer simulations, dynamic model

DOI: 10.3233/BME-151365

Journal: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S739-S746, 2015

Published: 2015
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Sino-atrial node cells (SANCs) play a significant role in rhythmic firing. To investigate their role in arrhythmia and interactions with the atrium, computer simulations based on cellular dynamic mathematical models are generally used. However, the large-scale computation usually makes research difficult, given the limited computational power of Central Processing Units (CPUs). In this paper, an accelerating approach with Graphic Processing Units (GPUs) is proposed in a simulation consisting of the SAN tissue and the adjoining atrium. By using the operator splitting method, the computational task was made parallel. Three parallelization strategies were then put forward. The strategy with the shortest running time was further optimized by considering block size, data transfer and partition. The results showed that for a simulation with 500 SANCs and 30 atrial cells, the execution time taken by the non-optimized program decreased 62% with respect to a serial program running on CPU. The execution time decreased by 80% after the program was optimized. The larger the tissue was, the more significant the acceleration became. The results demonstrated the effectiveness of the proposed GPU-accelerating methods and their promising applications in more complicated biological simulations.

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