Digital Blood in Massively Parallel CPU/GPU Systems for the Study of Platelet Transport

TL;DR

This paper introduces a high-performance, modular computational framework for simulating blood flow and platelet transport, combining advanced solvers optimized for CPU-GPU architectures to achieve unprecedented accuracy and speed.

Contribution

The authors develop a versatile, modular simulation tool integrating lattice Boltzmann, FEM, and immersed boundary methods optimized for hybrid CPU-GPU systems, outperforming existing solvers.

Findings

Achieves high accuracy in blood flow simulation.

Demonstrates superior performance on supercomputing hardware.

Suitable for exascale computing architectures.

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 the blood plasma, a novel finite element method (FEM) solver for the resolution of the 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, the FEM solver 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 the 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. The tests show that this versatile framework combines unprecedented accuracy with massive performance, rendering it suitable for the upcoming exascale architectures.