Literature DB >> 21768044

Performance of hybrid programming models for multiscale cardiac simulations: preparing for petascale computation.

Bernard J Pope1, Blake G Fitch, Michael C Pitman, John J Rice, Matthias Reumann.   

Abstract

Future multiscale and multiphysics models that support research into human disease, translational medical science, and treatment can utilize the power of high-performance computing (HPC) systems. We anticipate that computationally efficient multiscale models will require the use of sophisticated hybrid programming models, mixing distributed message-passing processes [e.g., the message-passing interface (MPI)] with multithreading (e.g., OpenMP, Pthreads). The objective of this study is to compare the performance of such hybrid programming models when applied to the simulation of a realistic physiological multiscale model of the heart. Our results show that the hybrid models perform favorably when compared to an implementation using only the MPI and, furthermore, that OpenMP in combination with the MPI provides a satisfactory compromise between performance and code complexity. Having the ability to use threads within MPI processes enables the sophisticated use of all processor cores for both computation and communication phases. Considering that HPC systems in 2012 will have two orders of magnitude more cores than what was used in this study, we believe that faster than real-time multiscale cardiac simulations can be achieved on these systems.

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Year:  2011        PMID: 21768044     DOI: 10.1109/TBME.2011.2161580

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  2 in total

1.  Parallel multiple instance learning for extremely large histopathology image analysis.

Authors:  Yan Xu; Yeshu Li; Zhengyang Shen; Ziwei Wu; Teng Gao; Yubo Fan; Maode Lai; Eric I-Chao Chang
Journal:  BMC Bioinformatics       Date:  2017-08-03       Impact factor: 3.169

2.  Fast acceleration of 2D wave propagation simulations using modern computational accelerators.

Authors:  Wei Wang; Lifan Xu; John Cavazos; Howie H Huang; Matthew Kay
Journal:  PLoS One       Date:  2014-01-30       Impact factor: 3.240

  2 in total

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