Computational models are increasingly used for diagnosis and treatment of cardiovascular disease. To provide a quantitative hemodynamic understanding that can be effectively used in the clinic, it is crucial to quantify the variability in the outputs from these models due to multiple sources of uncertainty. To quantify this variability, the analyst invariably needs to generate a large collection of high-fidelity model solutions, typically requiring a substantial computational effort. In this paper, we show how an explicit-in-time ensemble cardiovascular solver offers superior performance with respect to the embarrassingly parallel solution with implicit-in-time algorithms, typical of an inner-outer loop paradigm for non-intrusive uncertainty propagation. We discuss in detail the numerics and efficient distributed implementation of a segregated FSI cardiovascular solver on both CPU and GPU systems, and demonstrate its applicability to idealized and patient-specific cardiovascular models, analyzed under steady and pulsatile flow conditions.

计算模型越来越多地用于心血管疾病的诊断和治疗。为了提供可在临床中有效使用的定量血液动力学理解，由于多种不确定性来源，量化这些模型输出的可变性至关重要。为了量化这种可变性，分析师总是需要生成大量高保真模型解决方案，通常需要大量的计算工作。在本文中，我们展示了一个显式的时间集成心血管求解器在使用隐式时间算法的令人尴尬的并行解决方案方面提供了卓越的性能，这是非侵入性不确定性传播的内-外循环范式的典型代表。我们详细讨论了 CPU 和 GPU 系统上隔离 FSI 心血管求解器的数值和高效分布式实现，并证明了其适用于理想化和特定于患者的心血管模型，在稳定和脉动流条件下进行分析。