计算血流动力学中并行有限元求解器黑盒法的评估

F. Auricchio, M. Ferretti, A. Lefieux, M. Musci, A. Reali, S. Trimarchi, A. Veneziani
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引用次数: 2

摘要

在过去的20年里,血流量的数值近似作为一种研究循环生理病理的工具出现了,从概念验证到临床阶段。通过将医学图像与数值模型合并,可以支持外科医生和一般医生的决策过程。特别是,iCardioCloud项目旨在建立一个框架,对夹层、闭塞和动脉瘤等主动脉疾病进行完整的患者特异性血流动力学分析。从计算机科学的角度来看,这样的项目面临着多重挑战。首先,就每个病人需要解决的方程数量而言,这个问题的维度通常是巨大的,因此它需要大量的并行方法。此外,临床时间要求效率,因为至少在紧急情况下,应在数据检索后几个小时内获得结果。因此,必须在高端并行系统(如大型集群甚至超级计算机)上开发良好的实现。不幸的是,在这样的机器上获得有效的实现并不简单。在本文中,我们讨论了用黑盒方法获得的并行实现,该方法是通过组装现有的包和库,特别是为计算流体动力学开发的有限元库LifeV来建立的。最终目标是评估是否可以有效地解决应用程序,以及哪种并行范式最适合计算需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessment of a Black-Box Approach for a Parallel Finite Elements Solver in Computational Hemodynamics
Numerical approximation of blood flow has emerged in the last 20 years as a tool to investigate physiopathology of the circulation, moving from a proof-of-concept to a clinical stage. By merging medical images with numerical models it is possible to support the decision-making process of surgeons and doctors in general. In particular, the iCardioCloud project aims at establishing a framework to perform a complete patient-specific hemodynamics analysis for aortic diseases such as dissections, occlusions and aneurysms. From a computer science standpoint, such a project faces multiple challenges. First of all the dimension of the problem in terms of number of equations to be solved for each patient is in general huge and thus it requires massively parallel methods. In addition, clinical timeline demands for efficiency, since availability of results -- at least in an emergency scenario -- should be granted in few hours from data retrieval. Therefore it is mandatory to develop a good implementation on high-end parallel systems, such as large clusters or even supercomputers. Unfortunately, it is not straightforward to obtain an efficient implementation on such machines. In this paper we discuss a parallel implementation obtained with a black-box approach, that is set up by assembling existing packages and libraries and in particular LifeV, a finite element library developed for Computational Fluid Dynamics. The ultimate goal is to assess if the application can be solved efficiently and which is the parallel paradigm that best matches the computational requirements.
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