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Parallelized computation for computer simulation of electrocardiograms using personal computers with multi-core CPU and general-purpose GPU

Wenfeng Shen, Daming Wei, Weimin Xu, Xin Zhu, Shizhong Yuan
Biomedical Information Technology Lab, The University of Aizu, Uegami 90, Tsuruga, Ikki-machi, Aizu-Wakamatsu, Fukushima 965-8580, Japan
Computer Methods and Programs in Biomedicine, 2010 Oct; 100(1):87-96. (31 July 2010)

@article{shen2010parallelized,

   title={Parallelized computation for computer simulation of electrocardiograms using personal computers with multi-core CPU and general-purpose GPU},

   author={Shen, W. and Wei, D. and Xu, W. and Zhu, X. and Yuan, S.},

   journal={Computer Methods and Programs in Biomedicine},

   volume={100},

   number={1},

   pages={87–96},

   issn={0169-2607},

   year={2010},

   publisher={Elsevier North-Holland, Inc.}

}

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Biological computations like electrocardiological modelling and simulation usually require high-performance computing environments. This paper introduces an implementation of parallel computation for computer simulation of electrocardiograms (ECGs) in a personal computer environment with an Intel CPU of Core (TM) 2 Quad Q6600 and a GPU of Geforce 8800GT, with software support by OpenMP and CUDA. It was tested in three parallelization device setups: (a) a four-core CPU without a general-purpose GPU, (b) a general-purpose GPU plus 1 core of CPU, and (c) a four-core CPU plus a general-purpose GPU. To effectively take advantage of a multi-core CPU and a general-purpose GPU, an algorithm based on load-prediction dynamic scheduling was developed and applied to setting (c). In the simulation with 1600 time steps, the speedup of the parallel computation as compared to the serial computation was 3.9 in setting (a), 16.8 in setting (b), and 20.0 in setting (c). This study demonstrates that a current PC with a multi-core CPU and a general-purpose GPU provides a good environment for parallel computations in biological modelling and simulation studies.
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