【24h】

EVALUATION OF PARALLEL AGGREGATE CREATION ORDERS: SMOOTHED AGGREGATION ALGEBRAIC MULTIGRID METHOD

机译:并行聚集创建顺序的评估:平滑聚集代数多重网格法

获取原文
获取原文并翻译 | 示例

摘要

The Algebraic MultiGrid method (AMG) has been studied intensively as an ideal solver for large scale Poisson problems. The Smoothed Aggregation Algebraic MultiGrid (SA-AMG) method is one of the most efficient of these methods. The aggregation procedure is the most important part of the method and is the main area of interest of several researchers. Here we investigate aggregate creation orders in the aggregation procedure. Five types of aggregation procedure are tested for isotropic, anisotropic and simple elastic problems. As a result, it is important that aggregates are created around one aggregate in each domain for isotropic problems. For anisotropic problems, aggregates around domain borders influence the convergence much. The best strategy for both anisotropic and isotropic problems in our numerical test is the ag- High Performance Computational Science and Engineering gregate creation method which creates aggregates on borders first then creates aggregates around one aggregate in the internal domain. In our test, the SA-AMG preconditioned Conjugate Gradient (CG) method is compared to the Localized ILU preconditioned CG method. In the experiments, Poisson problems up to 1.6 x 10~7 DOF are solved on 125PEs.
机译:作为大型泊松问题的理想求解器,代数多重网格方法(AMG)进行了深入研究。平滑聚合代数多重网格(SA-AMG)方法是这些方法中最有效的方法之一。聚集过程是该方法最重要的部分,并且是一些研究人员感兴趣的主要领域。在这里,我们调查汇总过程中的汇总创建顺序。针对各向同性,各向异性和简单弹性问题测试了五种类型的聚集过程。因此,对于各向同性问题,在每个域中围绕一个聚集体创建聚集体非常重要。对于各向异性问题,域边界周围的聚集体会极大地影响收敛。在我们的数值测试中,解决各向异性和各向同性问题的最佳策略是敏捷高性能计算科学与工程集合创建方法,该方法首先在边界上创建集合,然后在内部域中围绕一个集合创建集合。在我们的测试中,将SA-AMG预处理共轭梯度(CG)方法与本地化ILU预处理CG方法进行了比较。在实验中,在125PE上解决了高达1.6 x 10〜7 DOF的泊松问题。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号