Performance comparison of three types of GPU-accelerated indirect boundary element method for voxel model analysis
Performance comparison of three types of GPU-accelerated indirect boundary element method for voxel model analysis
复制标题
三种GPU加速间接边界元法体素模型分析性能比较
DOI：
10.1002/jnm.1883
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Shoji Hamada
中科院分区：
文献类型：
--
作者：
H.Ishi;Z.Zhang and T.Imamura;Shoji Hamada
一种间接边界元方法，是面向静电场分析的体素模型是加速图形处理单元（GPU）。该方法将立方体体素上的方形墙视为边界面元素，并使用快速多极子方法（FMM）分析大规模模型。在两个常规CPU代码的基础上，为了寻求更高的计算性能，编写了三个GPU代码。这些GPU代码的设计如下：在GPU代码1中，FMM中的直接场和远场分别在GPU和CPU上同时计算;在GPU代码2中，两个场都在GPU上使用旋转-同轴平行-旋转分解算法计算;在GPU代码3中，两个场都在GPU上使用对角平移方案计算。人体模型中的电场是通过施加50 Hz磁场或通过两个电极注入直流（DC）电流产生的，并且使用具有三个GPU和六个CPU内核的个人计算机成功计算。使用双精度浮点算法，对390万个曲面元素进行分析，需要89.4秒来求解其控制线性系统。GPU代码1、2和3分别表现出最少的内存使用、最大的加速比和最快的计算时间。这些结果显示了异构CPU-GPU系统上计算性能的权衡关系的示例。版权所有© 2013约翰威利父子有限公司.
An indirect boundary element method that is geared to electrostatic field analysis in voxel models is accelerated by graphics processing units (GPUs). The method considers square walls on cubic voxels as boundary surface elements and uses the fast multipole method (FMM) to analyze large‐scale models. On the basis of two conventional CPU codes, three GPU codes are programmed in search of higher computing performance. These GPU codes are designed as follows: In GPU code 1, direct and far fields in the FMM are simultaneously calculated on the GPU and the CPU, respectively; in GPU code 2, both fields are calculated on the GPU with a rotation‐coaxial translation–rotation decomposition algorithm; and in GPU code 3, both fields are calculated on the GPU with a diagonal translation scheme. The electric fields in human models are generated by applying a 50‐Hz magnetic field or by injecting direct‐current (DC) current through two electrodes and they were calculated successfully using a personal computer with three GPUs and six CPU cores. An analysis with 3.9 million surface elements took 89.4 s to solve its governing linear system with double‐precision floating‐point arithmetic. GPU codes 1, 2, and 3 demonstrated the least memory usage, the greatest speed‐up ratio, and the fastest calculation time, respectively. These results show an example of the trade‐off relationships of computation performances on a heterogeneous CPU–GPU system. Copyright © 2013 John Wiley & Sons, Ltd.