Strong scaling and speedup to 16,384 processors in cardiac electro-mechanical simulations.
High performance computing is required to make feasible simulations of whole organ models of the heart with biophysically detailed cellular models in a clinical setting. Increasing model detail by simulating electrophysiology and mechanical models increases computation demands. We present scaling results of an electro - mechanical cardiac model of two ventricles and compare them to our previously published results using an electrophysiological model only. 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. Data decomposition for the distribution onto the distributed memory system was carried out by orthogonal recursive bisection. Load weight 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. The ten Tusscher et al. (2004) electrophysiological cell model was used and the Rice et al. (1999) model for the computation of the calcium transient dependent force. Scaling results for 512, 1024, 2048, 4096, 8192 and 16,384 processors were obtained for 1 ms simulation time. The simulations were carried out on an IBM Blue Gene/L supercomputer. The results show linear scaling from 512 to 16,384 processors with speedup factors between 1.82 and 2.14 between partitions. The most optimal load ratio was 1:25 for on all partitions. However, a shift towards load ratios with higher weight for the tissue elements can be recognized as can be expected when adding computational complexity to the model while keeping the same communication setup. This work demonstrates that it is potentially possible to run simulations of 0.5 s using the presented electro-mechanical cardiac model within 1.5 hours.
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
D001698:Biomedical Engineering / Q000379:methods*; D003198:Computer Simulation; D003205:Computing Methodologies; D004594:Electrophysiology / Q000379:methods*; D005260:Female; D006321:Heart / Q000502:physiology; D006329:Heart Conduction System; D006801:Humans; D008953:Models, Anatomic; D008955:Models, Cardiovascular; D008959:Models, Neurological; D009200:Myocardial Contraction; D016571:Neural Networks, Computer; D013314:Stress, Mechanical; D014481:United States; D053443:Visible Human Projects