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Multi-GPU Based Lattice Boltzmann Method for Hemodynamic Simulation in Patient-Specific Cerebral Aneurysm

Published online by Cambridge University Press:  30 April 2015

Changsheng Huang,
Baochang Shi,
Zhaoli Guo and
Zhenhua Chai
Changsheng Huang
Affiliation:
School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan 430074, China
Baochang Shi*
Affiliation:
School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan 430074, China
Zhaoli Guo
Affiliation:
State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, Wuhan 430074, China
Zhenhua Chai
Affiliation:
School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan 430074, China
*
*Corresponding author. Email addresses: hcs@hust.edu.cn (C. Huang), zlguo@hust.edu.cn (Z. Guo), shibc@hust.edu.cn (B. Shi), hustczh@hust.edu.cn (Z. Chai)

Abstract

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Conducting lattice Boltzmann method on GPU has been proved to be an effective manner to gain a significant performance benefit, thus the GPU or multi-GPU based lattice Boltzmann method is considered as a promising and competent candidate in the study of large-scale complex fluid flows. In this work, a multi-GPU based lattice Boltzmann algorithm coupled with the sparse lattice representation and message passing interface is presented. Some numerical tests are also carried out, and the results show that a parallel efficiency close to 90% can be achieved on a single-node cluster equipped with four GPU cards. Then the proposed algorithm is adopted to study the hemodynamics of patient-specific cerebral aneurysm with stent implanted. It is found that the stent can apparently reduce the aneurysmal inflow and improve the hemodynamic environment. This work also shows that the lattice Boltzmann method running on the GPU platform is a powerful tool to study the fluid mechanism within the aneurysms and enable us to better understand the pathogenesis and treatment of cerebral aneurysms.

Keywords

65Y0576Z0576M28Lattice Boltzmann methodGPU computingMPIcerebral aneurysmblood flow

Information

Type
Research Article
Information
Communications in Computational Physics , Volume 17 , Issue 4 , April 2015, pp. 960 - 974
Copyright
Copyright © Global-Science Press 2015 

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