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A parallel geometric multigrid method for finite elements on octree meshes applied to elastic image registration.

机译:八叉树网格上有限元的并行几何多重网格方法,应用于弹性图像配准。

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摘要

The first component of this thesis is a parallel algorithm for constructing octree meshes for finite element computations. Prior to octree meshing, the linear octree data structure must be constructed and a constraint known as "2:1 balancing" must be enforced parallel algorithms for these two subproblems are also presented. The second component of this thesis is a parallel geometric multigrid algorithm for solving elliptic partial differential equations (PDEs) using these octree meshes. The last component of this thesis is a parallel multiscale Gauss Newton optimization algorithm for solving the elastic image registration problem. The registration problem is discretized using finite elements on octree meshes and the parallel geometric multigrid algorithm is used as a preconditioner in the Conjugate Gradient (CG) algorithm to solve the linear system of equations formed in each Gauss Newton iteration.The parallel octree meshing and multigrid algorithms have several physical and computer science applications such as in solid/fluid mechanics, heat/mass transfer, electromagnetism, image processing and unstructured mesh generation. Potential applications for the image registration algorithm include automatic identification of abnormalities in medical images, motion reconstruction from temporal sequences of images and planning of surgeries.Several ideas were used to reduce the overhead for constructing the octree meshes. These include (a) a way to lower communication costs by reducing the number of synchronizations and reducing the communication message size, (b) a way to reduce the number of searches required to build element-to-vertex mappings, and (c) a compression scheme to reduce the memory footprint of the entire data structure. To our knowledge, the multigrid algorithm presented in this work is the only matrix-free multiplicative geometric multigrid implementation for solving finite element equations on octree meshes using thousands of processors. The overall scheme is second-order accurate, for sufficiently smooth right-hand sides and material properties and its complexity, for nearly uniform trees, is O&parl0Nnplog Nnp&parr0+O (np log np). Here, N is the number of octants and np is the number of processors. The proposed registration algorithm is also unique it is a combination of many different ideas: adaptivity, parallelism, fast optimization algorithms, and fast linear solvers.All the algorithms were implemented in C++ using the Message Passing Interface (MPI) standard and were built on top of the PETSc library from Argonne National Laboratory. The multigrid implementation has been released as an open source software: Dendro. Several numerical experiments were performed to test the performance of the algorithms. These experiments were performed on a variety of NSF TeraGrid platforms: on the Cray XT3 MPP system "Bigben" at the Pittsburgh Supercomputing Center (PSC), the Intel 64 Linux Cluster "Abe" at the National Center for Supercomputing Applications (NCSA), and the Sun Constellation Linux Cluster "Ranger" at the Texas Advanced Computing Center (TACC). Our largest run was a highly-nonuniform, 8-billion-unknown, elasticity calculation on 32,000 processors.
机译:本文的第一部分是一种并行算法,用于构造八叉树网格以进行有限元计算。在八叉树网格划分之前,必须先构建线性八叉树数据结构,并且还必须针对这两个子问题提出一种称为“ 2:1平衡”的约束并行算法。本文的第二部分是使用这些八叉树网格求解椭圆形偏微分方程(PDE)的并行几何多重网格算法。本文的最后一个组成部分是用于解决弹性图像配准问题的并行多尺度高斯牛顿优化算法。使用八叉树网格上的有限元离散化配准问题,并使用共轭梯度(CG)算法中的并行几何多重网格算法作为先决条件,以解决每次高斯牛顿迭代中形成的方程组的线性系统。该算法具有多种物理和计算机科学应用,例如在固体/流体力学,热/质量传递,电磁,图像处理和非结构化网格生成中。图像配准算法的潜在应用包括自动识别医学图像中的异常,根据图像的时间序列进行运动重建以及手术计划。几种想法被用来减少构建八叉树网格的开销。其中包括(a)通过减少同步次数和减小通信消息大小来降低通信成本的方法,(b)一种减少构建元素到顶点映射所需的搜索次数的方法,以及(c)a压缩方案可减少整个数据结构的内存占用量。据我们所知,这项工作中提出的多重网格算法是使用数千个处理器求解八叉树网格上的有限元方程组的唯一无矩阵乘法几何多重网格实现。总体方案是二阶准确的,对于右侧足够光滑的右侧和材料属性,对于几乎均匀的树木,其复杂度为O&parl0Nnplog Nnp&parr0 + O(np log np)。在这里,N是八分圆数,np是处理器数。所提出的注册算法也很独特,它是将自适应性,并行性,快速优化算法和快速线性求解器等许多不同思想结合在一起的。所有算法均使用C ++使用消息传递接口(MPI)标准实现,并建立在顶部来自Argonne国家实验室的PETSc库。多网格实现已作为开源软件Dendro发布。进行了几次数值实验以测试算法的性能。这些实验是在各种NSF TeraGrid平台上进行的:在匹兹堡超级计算中心(PSC)的Cray XT3 MPP系统“ Bigben”上,在国家超级计算应用中心(NCSA)上的英特尔64 Linux集群“ Abe”上,以及位于德克萨斯高级计算中心(TACC)的Sun Constellation Linux群集“ Ranger”。我们最大的一次尝试是在32,000个处理器上进行高度不均匀,80亿未知的弹性计算。

著录项

  • 作者

    Sampath, Rahul S.;

  • 作者单位

    Georgia Institute of Technology.;

  • 授予单位 Georgia Institute of Technology.;
  • 学科 Mathematics.Engineering Biomedical.Computer Science.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 182 p.
  • 总页数 182
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:37:56

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