ALGORITHM: Collaborative Research: SEIDD--Scalable Domain Decomposition Algorithms for Solving Parabolic Problems

算法：合作研究：SEIDD——解决抛物型问题的可扩展域分解算法

• 批准号: 0305393
• 负责人: Yu Zhuang
• 金额: $ 4.54万
• 依托单位: Texas Tech University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2005-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0305393&HistoricalAwards=false
• 关键词: ALGORITHM; Collaborative; Research; SEIDD; Scalable

The Stabilized Explicit-Implicit Domain Decomposition (SEIDD) is aclass of globally non-iterative and non-overlapping Domain Decomposition (DD) algorithms for solving parabolic equations on parallel computers. These algorithms do not use large amounts of memory and are inherently parallel, which makes them useful for large scale parallel processing.The SEIDD approach aims to provide algorithmic solutions that can better utilize parallel/distributed computing resources, thereby improving the accessibility of high performance computing power for researchers and educators from a broad range of science and engineering disciplines.Through integration with education and curriculum development, this research project promises to provide better education opportunities in high performance computing for a wide range of students while offering immediate training for computer science students in parallel algorithm design and programming.

稳定显隐区域分解（SEIDD）是一类在并行计算机上求解抛物型方程的全局非迭代、非重叠区域分解（DD）算法。这些算法不使用大量的内存，并且本质上是并行的，这使得它们对大规模并行处理非常有用。SEIDD方法旨在提供能够更好地利用并行/分布式计算资源的算法解决方案，从而为来自广泛的科学和工程学科的研究人员和教育工作者提供高性能计算能力的可访问性。通过与教育和课程开发的整合，该研究项目承诺为广大学生提供更好的高性能计算教育机会，同时为计算机科学专业的学生提供并行算法设计和编程方面的即时培训。