Parallelizing a finite element solver in computational hemodynamics

Abstract

In the last 20 years, a new approach has emerged to investigate the physiopathology of circulation. By merging medical images with validated numerical models, it is possible to support doctors’ decision-making process. The iCardioCloud project aims at establishing a computational framework to perform a complete patient-specific numerical analysis, specially oriented to aortic diseases (like dissections or aneurysms) and to deliver a compelling synthesis. The project can be considered a pioneering example of a Computer Aided Clinical Trial: i.e., a comprehensive analysis of patients where the level of knowledge extracted by traditional measures and statistics is enhanced through the massive use of numerical modeling. From a computer engineering point of view, iCardioCloud faces multiple challenges. First, the number of problems to solve for each patient is significantly huge – this is typical of computational fluid dynamics (CFD) – and it requires parallel methods. In addition, working in a clinical environment demands efficiency as the timeline requires rapid quantitative answers (as may happen in an emergency scenario). It is therefore mandatory to employ high-end parallel systems, such as large clusters or supercomputers. Here we discuss a parallel implementation of an application within the iCardioCloud project, built with a black-box approach – i.e., by assembling and configuring existing packages and libraries and in particular LifeV, a finite element library developed to solve CFD problems. The goal of this paper is to describe the software architecture underlying LifeV and to assess its performance and the most appropriate parallel paradigm. This paper is an extension of a previous work presented at the PBio 2015 Conference. This revision extends the description of the software architecture and discusses several new serial and parallel optimizations to the application. We discuss the introduction of hybrid parallelism in order to mitigate some performance problems previously experienced.