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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 95
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Paper 91

Parallel Paradigm for Ultraparallel Multi-Scale Brain Blood Flow Simulations

L. Grinberg and G.E. Karniadakis

Division of Applied Mathematics, Brown University, Providence RI, United States of America

Full Bibliographic Reference for this paper
L. Grinberg, G.E. Karniadakis, "Parallel Paradigm for Ultraparallel Multi-Scale Brain Blood Flow Simulations", in , (Editors), "Proceedings of the Second International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 91, 2011. doi:10.4203/ccp.95.91
Keywords: computational fluid dynamics, high-performance computing, high-order spectral method, domain decomposition, multilevel parallelism, arterial flow.

and methodologies in designing effective scalable solvers. We anticipate that in addition to hierarchical or multi-level domain decompositions, the major effort will be invested into the design of task-parallel codes and robust interface conditions.

In this paper we present one approach in building a scalable solver NekTarG for solution of multi-scale and large size problems [1]. NekTarG has been designed for multi-scale blood modeling. The macro-vascular scales describing the flow dynamics in large vessels are coupled to the mesovascular scales unfolding dynamics of individual blood-cells. The meso-vascular events are coupled to the microvascular ones accounting for blood perfusion, clot formation, adhesion of the blood cells to the arterial wall, etc.

The scalable-solver NekTarG integrates already existing, well tested and optimized codes (modules). Each module is targeting a single scale flow problem, i.e. at the macro-vascular scale, meso- or even micro-vascular scales. The modules are coupled using the multi-level communicating interface (MCI). The MCI has been designed for data-parallel and task-parallel decompositions, it minimizes the inter-module communication, and as such is also appropriate for grid computing.

To verify the feasibility of our approach and test the performance of NekTarG we solved a 8.2B degree of freedom benchmark flow problem on upto 122,800 cores. NekTarG has been also employed in simulations of blood flow in very large network of the brain arteries with an aneurysm. The good weak and strong scaling of the code obtained in benchmark tests as well as in arterial flow simulations in the brain are presented. We also describe computational methods of coupling a continuum high-order spectral/hp element deterministic solver to a stochastic solver DPD-LAMMPS, which is based on the dissipative particle dynamics method and present our first results.

L. Grinberg, G.E. Karniadakis, "A new Domain Decomposition Method With Overlapping Patches for Ultrascale Simulations: Application to Biological Flows", Journal of Computational Physics, 229(15), 5541-5563, 2010. doi:10.1016/

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