GPU-accelerated Red Blood Cells Simulations with Transport Dissipative Particle Dynamics

TL;DR

This paper introduces a GPU-accelerated simulation package for red blood cells using transport Dissipative Particle Dynamics, enabling efficient multiscale modeling of cell mechanics and chemical transport.

Contribution

It presents a novel GPU-based implementation of tDPD for red blood cell simulations, achieving significant speedup and scalability improvements over CPU methods.

Findings

Achieved a 10.1x speedup on a single GPU compared to 16 CPU cores.

Demonstrated 91% weak scaling efficiency across 256 nodes.

Validated the model's accuracy in capturing cell membrane properties.

Abstract

Mesoscopic numerical simulations provide a unique approach for the quantification of the chemical influences on red blood cell functionalities. The transport Dissipative Particles Dynamics (tDPD) method can lead to such effective multiscale simulations due to its ability to simultaneously capture mesoscopic advection, diffusion, and reaction. In this paper, we present a GPU-accelerated red blood cell simulation package based on a tDPD adaptation of our red blood cell model, which can correctly recover the cell membrane viscosity, elasticity, bending stiffness, and cross-membrane chemical transport. The package essentially processes all computational workloads in parallel by GPU, and it incorporates multi-stream scheduling and non-blocking MPI communications to improve inter-node scalability. Our code is validated for accuracy and compared against the CPU counterpart for speed. Strong scaling and weak scaling are also presented to characterizes scalability. We observe a speedup of 10.1 on one GPU over all 16 cores within a single node, and a weak scaling efficiency of 91% across 256 nodes. The program enables quick-turnaround and high-throughput numerical simulations for investigating chemical-driven red blood cell phenomena and disorders.