Dynamic Load Balancing for Distributed Finite Element Analysis

分布式有限元分析的动态负载平衡

• 批准号: 9460347
• 负责人: Manas Deb
• 金额: $ 7.5万
• 依托单位: Computational Mechanics Company, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-01-01 至 1996-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9460347&HistoricalAwards=false
• 关键词: Dynamic; Load; Balancing; Distributed; Finite

This Small Business Innovation Research Phase I project will investigate techniques for dynamic load balancing in parallel finite element analyses. The techniques will be particularly suitable for distributed analyses on heterogeneous networks. In the area of high performance computing, significant efforts are underway to effectively use the idle computing power of workstation networks. One of the key issues in designing algorithms for such systems is that of load balancing. The proposed project offers to devise techniques for dealing with this problem. The prevalent balancing methods aim to achieve a balanced state at discrete instants in time by rebalancing the whole system. This project will explore methods to maintain a continually (near) balanced state by exploiting balancing opportunities as and when they occur. Imbalances that occur in distributed analyses due to the dynamically varying capabilities of participating processors and nonuniform adaptations of meshes (as a result of using solution adaptive techniques of analysis) will be dealt with. A two-level approach is suggested to efficiently correct these imbalances with minimal communication overhead while maintaining the data locality that is so important for the efficiency of distributed implementations of finite element type discretizations.

这个小型企业创新研究第一阶段项目将研究并行有限元分析中的动态负载平衡技术。这些技术将特别适用于异质网络上的分布式分析。在高性能计算领域，正在做出重大努力，以有效利用工作站网络的空闲计算能力。为这类系统设计算法的关键问题之一是负载均衡问题。拟议的项目提供了解决这一问题的技术。目前流行的平衡方法旨在通过对整个系统进行再平衡，在离散时刻及时达到平衡状态。这个项目将探索通过在平衡机会发生时利用平衡机会来保持持续(接近)平衡状态的方法。将处理分布式分析中由于参与处理器的动态变化能力和网格的不一致适应(由于使用解决方案自适应分析技术)而出现的不平衡问题。提出了一种两级方法，以最小的通信开销有效地纠正这些不平衡，同时保持数据的局部性，而数据局部性对于有限元类型离散的分布式实现的效率非常重要。