Orthogonal recursive bisection data decomposition for high performance computing in cardiac model simulations: dependence on anatomical geometry.
Orthogonal recursive bisection (ORB) algorithm can be used as data decomposition strategy to distribute a large data set of a cardiac model to a distributed memory supercomputer. It has been shown previously that good scaling results can be achieved using the ORB algorithm for data decomposition. However, the ORB algorithm depends on the distribution of computational load of each element in the data set. In this work we investigated the dependence of data decomposition and load balancing on different rotations of the anatomical data set to achieve optimization in load balancing. The anatomical data set was given by both ventricles of the Visible Female data set in a 0.2 mm resolution. Fiber orientation was included. The data set was rotated by 90 degrees around x, y and z axis, respectively. By either translating or by simply taking the magnitude of the resulting negative coordinates we were able to create 14 data set of the same anatomy with different orientation and position in the overall volume. Computation load ratios for non - tissue vs. tissue elements used in the data decomposition were 1:1, 1:2, 1:5, 1:10, 1:25, 1:38.85, 1:50 and 1:100 to investigate the effect of different load ratios on the data decomposition. The ten Tusscher et al. (2004) electrophysiological cell model was used in monodomain simulations of 1 ms simulation time to compare performance using the different data sets and orientations. The simulations were carried out for load ratio 1:10, 1:25 and 1:38.85 on a 512 processor partition of the IBM Blue Gene/L supercomputer. Th results show that the data decomposition does depend on the orientation and position of the anatomy in the global volume. The difference in total run time between the data sets is 10 s for a simulation time of 1 ms. This yields a difference of about 28 h for a simulation of 10 s simulation time. However, given larger processor partitions, the difference in run time decreases and becomes less significant. Depending on the processor partition size, future work will have to consider the orientation of the anatomy in the global volume for longer simulation runs.
Reumann
Matthias
M
Computational Biology Center, IBM TJ Watson Research Center, Yorktown Heights, 1101 Kitchawan Road, Route 134, NY 10598, USA. mreumann@ieee.org
Fitch
Blake G
BG
Rayshubskiy
Aleksandr
A
Keller
David U J
DU
Seemann
Gunnar
G
Dossel
Olaf
O
Pitman
Michael C
MC
Rice
John J
JJ
D000465:Algorithms; D003198:Computer Simulation; D003201:Computers; D003205:Computing Methodologies; D004594:Electrophysiology / Q000379:methods; D006321:Heart / Q000502:physiology; D006329:Heart Conduction System / Q000502:physiology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008953:Models, Anatomic; D008955:Models, Cardiovascular; D008962:Models, Theoretical; D009200:Myocardial Contraction; D012984:Software; D014481:United States; D053443:Visible Human Projects