Dendro: Parallel algorithms for multigrid and AMR methods on 2:1 balanced octrees

R. Sampath, Santi S. Adavani, H. Sundar, I. Lashuk, G. Biros
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引用次数: 55

Abstract

In this article, we present Dendro, a suite of parallel algorithms for the discretization and solution of partial differential equations (PDEs) involving second-order elliptic operators. Dendro uses trilinear finite element discretizations constructed using octrees. Dendro, comprises four main modules: a bottom-up octree generation and 2:1 balancing module, a meshing module, a geometric multiplicative multigrid module, and a module for adaptive mesh refinement (AMR). Here, we focus on the multigrid and AMR modules. The key features of Dendro are coarsening/refinement, inter-octree transfers of scalar and vector fields, and parallel partition of multilevel octree forests. We describe a bottom-up algorithm for constructing the coarser multigrid levels. The input is an arbitrary 2:1 balanced octree-based mesh, representing the fine level mesh. The output is a set of octrees and meshes that are used in the multigrid sweeps. Also, we describe matrix-free implementations for the discretized PDE operators and the intergrid transfer operations. We present results on up to 4096 CPUs on the Cray XT3 (ldquoBigBenrdquo), the Intel 64 system (ldquoAberdquo), and the Sun Constellation Linux cluster (ldquoRangerrdquo).
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多网格并行算法和2:1平衡八叉树上的AMR方法
在本文中,我们提出了Dendro,一套用于二阶椭圆算子偏微分方程离散化和求解的并行算法。Dendro使用使用八叉树构造的三线性有限元离散化。Dendro包括四个主要模块:自下而上的八叉树生成和2:1平衡模块、网格划分模块、几何乘法多网格模块和自适应网格细化模块(AMR)。在这里,我们主要关注多网格和AMR模块。Dendro的主要特征是粗化/精化、标量场和向量场在八叉树间的传递以及多层八叉树森林的并行划分。我们描述了一种自底向上的算法来构造较粗的多网格层。输入是一个任意2:1平衡的基于八叉树的网格,表示精细级网格。输出是一组用于多网格扫描的八叉树和网格。此外,我们还描述了离散PDE算子和网格间转移操作的无矩阵实现。我们在Cray XT3 (ldquoBigBenrdquo)、Intel 64系统(ldquoAberdquo)和Sun Constellation Linux集群(ldquoRangerrdquo)上展示了多达4096个cpu的结果。
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