基于GPU的三维非结构化网格并行AMG求解器

K. Tej, N. Sivadasan, Vatsalya Sharma, R. Banerjee
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引用次数: 2

摘要

多年来,图形处理单元(gpu)已经从图形加速器演变为可扩展的协处理器。利用GPU实现了三维非结构化网格的代数多网格求解器。该求解器在计算流体动力学(CFD)中有着广泛的应用。结合顶点着色、优化的内存表示、多网格和改进的粗化技术,我们在并行实现中获得了相当大的加速。求解压力泊松方程是求解Navier-Stokes方程时最耗时的部分,该求解器为求解压力泊松方程提供了显著的加速。在我们的实验研究中,我们解出了盖驱动腔体的压力泊松方程和经过方形圆柱体的层流方程。与串行非多网格实现相比,我们的实现在大小为260万的网格上实现了915倍的盖子驱动腔问题速度,在大小为170万的网格上实现了1020倍的层流通过方形圆柱体问题速度。对于我们的实现,我们使用了NVIDIA的CUDA编程模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parallel AMG solver for three dimensional unstructured grids using GPU
Graphics Processing Units (GPUs) have evolved over the years from being graphics accelerator to scalable coprocessor. We implement an algebraic multigrid solver for three dimensional unstructured grids using GPU. Such a solver has extensive applications in Computational Fluid Dynamics (CFD). Using a combination of vertex coloring, optimized memory representations, multi-grid and improved coarsening techniques, we obtain considerable speedup in our parallel implementation. Our solver provides significant acceleration for solving pressure Poisson equations, which is the most time consuming part while solving Navier-Stokes equations. In our experimental study, we solve pressure Poisson equations for flow over lid driven cavity and for laminar flow past square cylinder. Our implementation achieves 915 times speed up for the lid driven cavity problem on a grid of size 2.6 million and a speed up of 1020 times for the laminar flow past square cylinder problem on a grid of size 1.7 million, compared to serial non-multigrid implementations. For our implementation, we used NVIDIA's CUDA programming model.
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