利用线性八叉树进行有限元模拟的低常数并行算法

H. Sundar, R. Sampath, Santi S. Adavani, C. Davatzikos, G. Biros
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引用次数: 65

摘要

在本文中,我们提出了在线性八叉树上构造一致性有限元离散化的并行算法。现有的基于八叉树的离散化扩展到数十亿个元素,但复杂性常数可能很高。在我们的方法中,我们使用了几种技术来最小化开销:一种新颖的自下而上的树结构和2:1的平衡约束执行;通过将八叉树和元素连接表示为唯一可解码代码(UDC)来进行压缩的Golomb-Rice编码;重叠通信和计算;和字节对齐缓存效率。应用拉普拉斯函数的成本与使用具有相同元素数量的直接索引规则网格离散化的成本相当。我们的算法在匹兹堡超级计算中心的Cray XT3上的4096个处理器上扩展到了40亿元。与之前需要几分钟的实现相比,整个树的构建时间不到一分钟;变系数拉普拉斯算子的离散化计算只需要几秒钟。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Low-constant parallel algorithms for finite element simulations using linear octrees
In this article we propose parallel algorithms for the construction of conforming finite-element discretization on linear octrees. Existing octree-based discretizations scale to billions of elements, but the complexity constants can be high. In our approach we use several techniques to minimize overhead: a novel bottom-up tree-construction and 2:1 balance constraint enforcement; a Golomb-Rice encoding for compression by representing the octree and element connectivity as an Uniquely Decodable Code (UDC); overlapping communication and computation; and byte alignment for cache efficiency. The cost of applying the Laplacian is comparable to that of applying it using a direct indexing regular grid discretization with the same number of elements. Our algorithm has scaled up to four billion octants on 4096 processors on a Cray XT3 at the Pittsburgh Supercomputing Center. The overall tree construction time is under a minute in contrast to previous implementations that required several minutes; the evaluation of the discretization of a variable-coefficient Laplacian takes only a few seconds.
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