Biomedical Engineering / Biomedizinische Technik
Editor-in-Chief: Dössel, Olaf
Editorial Board Member: Augat, Peter / Gehring, Hartmut / Haueisen, Jens / Jockenhoevel, Stefan / Lenarz, Thomas / Leonhardt, Steffen / Niederlag, Wolfgang / Plank, Gernot / Radermacher, Klaus M. / Schkommodau, Erik / Schmitz, Georg / Stieglitz, Thomas / Witte, Herbert / Boenick, Ulrich / Jaramaz, Branislav / Kraft, Marc / Lenthe, Harry / Lo, Benny / Mainardi, Luca / Micera, Silvestro / Penzel, Thomas / Robitzki, Andrea A. / Schaeffter, Tobias / Snedeker, Jess G. / Sörnmo, Leif / Sugano, Nobuhiko / Werner, Jürgen / Wintermantel, Erich /
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Orthogonal recursive bisection as data decomposition strategy for massively parallel cardiac simulations
1IBM T. J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY 10598, USA
2IBM Research Collaboratory for Life Sciences – Melbourne, 187 Grattan Street, Carlton, Victoria 3010, Australia
3Department of Computer Science and Software Engineering, University of Melbourne, Carlton, Victoria, Australia
Citation Information: Biomedizinische Technik/Biomedical Engineering. Volume 56, Issue 3, Pages 129–145, ISSN (Online) 1862-278X, ISSN (Print) 0013-5585, DOI: 10.1515/bmt.2011.100, June 2011
- Published Online:
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.
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