GPU-accelerated red blood cells simulations with transport dissipative particle dynamics

AL Blumers and YH Tang and Z Li and XJ Li and GE Karniadakis, COMPUTER PHYSICS COMMUNICATIONS, 217, 171-179 (2017).

DOI: 10.1016/j.cpc.2017.03.016

Mesoscopic numerical simulations provide a unique approach for the quantification of the chemical influences on red blood cell functionalities. The transport Dissipative Particle 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 characterize 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. Program summary Program Title: usERMESO 2.0 Program Files doi: Licensing provisions: GNU General Public License, Version 3 Programming language: C/C++, CUDA C/C++ MPI. Nature of problem: Particle-based simulation of a red blood cell suspension with chemical transport property. Solution method: Each red blood cell is represented by a 3-D triangular mesh with bonded potential under area and volume constraints. The solvent is approximated with coarse-grained particles. The time evolution of the system is integrated using Velocity-Verlet algorithm. Restrictions: The code is compatible with NVIDIA GPGPUs with compute capability 3.0 and above. Unusual features: The code is implemented on GPGPUs with significantly improved speed. Additional Comments: Github repository link (C) 2017 Elsevier B.V. All rights reserved.

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