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ALGORITHMS: New Concept and Parallel Algorithms for Robust Preconditioning in Large Scale Parallel Matrix Computation

算法：大规模并行矩阵计算中鲁棒预处理的新概念和并行算法

基本信息

批准号：
0202934
负责人：
Jun Zhang
金额：
$ 17.24万
依托单位：
University of Kentucky Research Foundation
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2002
资助国家：
美国
起止时间：
2002-06-01 至 2006-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=0202934&HistoricalAwards=false
关键词：
ALGORITHMS New Concept Parallel Algorithms

项目摘要

Large sparse unstructured matrices arising from various computer simulation and modeling are commonly solved by preconditioned iterative methods. This research project will study and design robust high performance preconditioners for parallel solution of large sparse linear systems, based on a class of multistep successive sparse approximate inverse preconditioning techniques.We will develop new concept and parallel algorithms of multistep successive preconditioning for enhancing the robustness of standard sparse approximate inverse preconditioning techniques, and generalize this concept to the context of other preconditioning techniques. Study will be conducted to show the advantages of such approach to enhance both preconditioning accuracy and factorization stability. We will build portable software packages to implement new preconditioning strategies for solving unstructured general sparse linear systems on high performance parallel computers.The general purpose high performance preconditioned iterative solvers from this research project are expected to make significant impact in the field of applied scientific computing. Our experience and existing strength will ensure that theproject be carried out fully as proposed. As U.S. industry is more and more relying on computer aided design and manufacturing, large scale computer simulation and modeling will be a vital component in new products research and development. The outcome of this research will benefit U.S. industry as well as scientific research community by providing more efficient kernel software forlarge scale computer simulations.

在各种计算机模拟和建模过程中产生的大型稀疏非结构化矩阵通常采用预条件迭代法求解。本研究计划将在一类多步逐次稀疏近似逆预处理技术的基础上，研究和设计大型稀疏线性方程组并行求解的鲁棒高性能预处理器，提出多步逐次预处理的新概念和并行算法，以增强标准稀疏近似逆预处理技术的鲁棒性，并将此概念推广到其他预处理技术的上下文中。研究将显示这种方法的优点，以提高预处理精度和分解稳定性。我们将建立可移植的软件包，在高性能并行计算机上实现新的预处理策略，用于求解非结构化一般稀疏线性方程组。本研究项目的通用高性能预处理迭代求解器有望在应用科学计算领域产生重大影响。我们的经验和现有实力将确保该项目按建议全面实施。随着美国工业越来越依赖计算机辅助设计和制造，大规模计算机仿真和建模将成为新产品研究和开发的重要组成部分。这项研究的成果将有利于美国工业界以及科学研究界提供更有效的内核软件的大规模计算机模拟。