Implementation of the moving particle semi-implicit method on GPU
Implementation of the moving particle semi-implicit method on GPU
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DOI：
10.1007/s11433-010-4241-5
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发表时间：
2011-01
期刊：
影响因子：
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通讯作者：
Xiaosong Zhu;Liang Cheng;Lin Lu;B. Teng
中科院分区：
文献类型：
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作者：
Xiaosong Zhu;Liang Cheng;Lin Lu;B. Teng
移动粒子半隐式（MPS）方法在模拟剧烈的自由表面流动方面表现出良好的性能，因而成为流体流动模拟领域的一个热门方法。然而，搜索相邻粒子和求解大型稀疏矩阵方程（泊松型方程）的实现是非常耗时的。为了充分利用图形处理器（GPU）强大的并行计算能力，本研究在NVIDIA GTX 280上开发了一个基于GPU的MPS模型，该模型采用统一计算设备架构（CUDA）。通过间接方法进行有效的邻域粒子搜索，并通过双共轭梯度（BiCG）方法求解泊松型压力方程。四个不同的优化水平，目前通用的并行GPU为基础的MPS模型进行了论证。此外，还对GPU代码的优化进行了详细的讨论。利用该程序对溃坝水流基准问题进行了数值模拟。比较结果表明，基于GPU的MPS模型的性能是传统CPU模型的26倍。
The Moving Particle Semi-implicit (MPS) method performs well in simulating violent free surface flow and hence becomes popular in the area of fluid flow simulation. However, the implementations of searching neighbouring particles and solving the large sparse matrix equations (Poisson-type equation) are very time-consuming. In order to utilize the tremendous power of parallel computation of Graphics Processing Units (GPU), this study has developed a GPU-based MPS model employing the Compute Unified Device Architecture (CUDA) on NVIDIA GTX 280. The efficient neighbourhood particle searching is done through an indirect method and the Poisson-type pressure equation is solved by the Bi-Conjugate Gradient (BiCG) method. Four different optimization levels for the present general parallel GPU-based MPS model are demonstrated. In addition, the elaborate optimization of GPU code is also discussed. A benchmark problem of dam-breaking flow is simulated using both codes of the present GPU-based MPS and the original CPU-based MPS. The comparisons between them show that the GPU-based MPS model outperforms 26 times the traditional CPU model.