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Biomedical Engineering / Biomedizinische Technik

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Volume 56, Issue 3 (Jun 2011)

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Volume 57 (2012)

Orthogonal recursive bisection as data decomposition strategy for massively parallel cardiac simulations

Matthias Reumann
  • IBM T. J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY 10598, USA
  • IBM Research Collaboratory for Life Sciences – Melbourne, 187 Grattan Street, Carlton, Victoria 3010, Australia
  • Department of Computer Science and Software Engineering, University of Melbourne, Carlton, Victoria, Australia
  • Email:
/ Blake G. Fitch
  • IBM T. J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY 10598, USA
/ Aleksandr Rayshubskiy
  • IBM T. J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY 10598, USA
/ Michael C. Pitman
  • IBM T. J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY 10598, USA
/ John J. Rice
  • IBM T. J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY 10598, USA
Published Online: 2011-06-10 | DOI: https://doi.org/10.1515/bmt.2011.100

Abstract

We present the orthogonal recursive bisection algorithm that hierarchically segments the anatomical model structure into subvolumes that are distributed to cores. The anatomy is derived from the Visible Human Project, with electrophysiology based on the FitzHugh-Nagumo (FHN) and ten Tusscher (TT04) models with monodomain diffusion. Benchmark simulations with up to 16,384 and 32,768 cores on IBM Blue Gene/P and L supercomputers for both FHN and TT04 results show good load balancing with almost perfect speedup factors that are close to linear with the number of cores. Hence, strong scaling is demonstrated. With 32,768 cores, a 1000 ms simulation of full heart beat requires about 6.5 min of wall clock time for a simulation of the FHN model. For the largest machine partitions, the simulations execute at a rate of 0.548 s (BG/P) and 0.394 s (BG/L) of wall clock time per 1 ms of simulation time. To our knowledge, these simulations show strong scaling to substantially higher numbers of cores than reported previously for organ-level simulation of the heart, thus significantly reducing run times. The ability to reduce runtimes could play a critical role in enabling wider use of cardiac models in research and clinical applications.

Keywords: cardiac models; high performance computing; load balancing; massively parallel distributed memory supercomputer; performance scaling

About the article

Corresponding author: Matthias Reumann, IBM Research Collaboratory for Life Sciences – Melbourne, 187 Grattan Street (Ground Floor), Carlton, Victoria 3010, Australia Phone: +61-4-15826891 Fax: +61-3-83449130


Received: 2011-05-11

Accepted: 2011-05-18

Published Online: 2011-06-10

Published in Print: 2011-06-01



Citation Information: Biomedizinische Technik/Biomedical Engineering, ISSN (Online) 1862-278X, ISSN (Print) 0013-5585, DOI: https://doi.org/10.1515/bmt.2011.100. Export Citation

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