Abstract
We propose a highly versatile computational framework for the simulation of cellular blood flow focusing on extreme performance without compromising accuracy or complexity. The tool couples the lattice Boltzmann solver Palabos for the simulation of blood plasma, a novel finite-element method (FEM) solver for the resolution of deformable blood cells, and an immersed boundary method for the coupling of the two phases. The design of the tool supports hybrid CPU–GPU executions (fluid, fluid–solid interaction on CPUs, deformable bodies on GPUs), and is non-intrusive, as each of the three components can be replaced in a modular way. The FEM-based kernel for solid dynamics outperforms other FEM solvers and its performance is comparable to state-of-the-art mass–spring systems. We perform an exhaustive performance analysis on Piz Daint at the Swiss National Supercomputing Centre and provide case studies focused on platelet transport, implicitly validating the accuracy of our tool. The tests show that this versatile framework combines unprecedented accuracy with massive performance, rendering it suitable for upcoming exascale architectures.
Highlights
Blood flow plays an important role in most of the fundamental functions of living organisms
We finalize the framework by introducing an integrated environment that obeys all the above-mentioned criteria, i.e. we introduce a novel GPU implementation of the finite-element method (FEM)-based solid solver and thoroughly present the highperformance computing (HPC) design of the framework, especially the intricate communication patterns between the various modules and processes
On top of the Palabos core library, we have developed the nodal projective finite elements method (npFEM) solver, which is written in C++ and CUDA, and it is derived from the open-source library ShapeOp [43]
Summary
Blood flow plays an important role in most of the fundamental functions of living organisms. In the last two decades, computational tools for the direct numerical simulation (DNS) of cellular blood flow have been developed [2]. With the occurrence of more mature tools, there is an increased focus on developing/analysing lab-on-a-chip systems [13,14] and drug delivery systems [15,16]. Despite such advances, we believe that there is a tremendous space for improvement in terms of fidelity, high performance and clinically relevant scales
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