Platform and algorithm effects on computational fluid dynamics applications in life sciences

Abstract

High Performance Computing (HPC) is a mainstream mode of exploration and analysis in different fields, not only technical but also social and life sciences. A well-established HPC domain is medicine, and cardiovascular sciences in particular. The adoption of CFD as a tool for diagnosis, prognosis, and treatment planning in the clinical routine is however still an open challenge. This computational tool, required by Computer Aided Clinical Trials and Surgical Planning, calls for significant computational resources to face both large volume of patients and diverse timelines ranging from election to emergency scenarios. Traditional local clusters may be not adequate to deliver the computational needs. Alternative solutions like grids and on-demand cloud resources need to be seriously considered. This paper proposes methodologies and protocols to identify the optimal choice of computing platforms for hemodynamics computations that will be increasingly needed in the future and the optimal scheduling of the tasks across the selected resources. We focus on hemodynamics in patient-specific settings and present extensive results on different platforms. We propose a way to measure and estimate performance and running time under realistic scenarios tailored to the utility function of the simulation. We discuss in detail the optimal (parallel) partitioning of the domain of a problem of interest with different mathematical approaches. We show that an overlapping splitting is generally advantageous and the detection of optimal overlapping has the potential to significantly reduce computational costs of the entire solution process and the communication volume across the platforms.