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利用GPU集群对肺部气流进行患者特异性建模以应用于医学实践。

Patient-specific modelling of pulmonary airflow using GPU cluster for the application in medical practice.

作者信息

Miki T, Wang X, Aoki T, Imai Y, Ishikawa T, Takase K, Yamaguchi T

机构信息

Department of Biomedical Engineering, Tohoku University, Aoba 6-6-01, Sendai, 980-8579, Japan.

出版信息

Comput Methods Biomech Biomed Engin. 2012;15(7):771-8. doi: 10.1080/10255842.2011.560842. Epub 2011 Aug 2.

Abstract

In this paper, we propose a novel patient-specific method of modelling pulmonary airflow using graphics processing unit (GPU) computation that can be applied in medical practice. To overcome the barriers imposed by computation speed, installation price and footprint to the application of computational fluid dynamics, we focused on GPU computation and the lattice Boltzmann method (LBM). The GPU computation and LBM are compatible due to the characteristics of the GPU. As the optimisation of data access is essential for the performance of the GPU computation, we developed an adaptive meshing method, in which an airway model is covered by isotropic subdomains consisting of a uniform Cartesian mesh. We found that 4(3) size subdomains gave the best performance. The code was also tested on a small GPU cluster to confirm its performance and applicability, as the price and footprint are reasonable for medical applications.

摘要

在本文中,我们提出了一种新颖的针对特定患者的使用图形处理单元(GPU)计算来模拟肺气流的方法,该方法可应用于医学实践。为了克服计算速度、安装价格和占用空间对计算流体动力学应用所造成的障碍,我们重点关注了GPU计算和格子玻尔兹曼方法(LBM)。由于GPU的特性,GPU计算和LBM是兼容的。由于数据访问的优化对于GPU计算的性能至关重要,我们开发了一种自适应网格划分方法,其中气道模型由由均匀笛卡尔网格组成的各向同性子域覆盖。我们发现4(3)尺寸的子域具有最佳性能。该代码还在一个小型GPU集群上进行了测试,以确认其性能和适用性,因为其价格和占用空间对于医学应用来说是合理的。

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