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Application of Scalable Parallel Algorithm in Cardiovascular-Cerebrovascular Hemodynamics Simulation

Abstract 

Studies have shown that stroke and coronary heart disease (CAD) affect each other by changes in cerebrovascular and coronary hemodynamics. For example, CAD is one of the main causes of death in patients with transient cerebral ischemia or stroke. Therefore, the evaluation of the patient's cardiovascular-cerebrovascular coupling hemodynamic function is very important in clinical application. Due to computational complexity, current researches on hemodynamics are usually base on a single site, such as coronary arteries, cerebrovascular, or abdominal arteries, etc., and hard to study the mutual influence of changes in blood flow between various sites in-depth. The development of high scalable parallel algorithms and hardware makes it possible to simulate the coupling heart-brain hemodynamics. In this report, we present a scalable domain decomposition-based algorithm for solving unsteady, complex cardiovascular and cerebrovascular blood flow. We will present the parallel performance of the algorithm with a large number of processor cores.