SOFTWARE: A Software Environment for High Performance Scientific Computing Applications

软件：高性能科学计算应用程序的软件环境

• 批准号: 0234270
• 负责人: Jun Zhang
• 金额: $ 6.21万
• 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: 2003
• 资助国家: 美国
• 起止时间: 2003-03-15 至 2004-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0234270&HistoricalAwards=false
• 关键词: SOFTWARE; Software; Environment; Performance; Scientific

Efficient numerical algorithms play an increasingly important role in computational sciences to make large scale computer simulations tractable. Solution of very large sparse matrices has been one of the most time-consuming parts of many large scale high performance computer simulation problems. One of the important tasks in high performance scientific computing is to identify which solver is suitable for what class of applications (sparse matrices), and which sparse matrix can be solved by what solver. We will use the techniquesand ideas in knowledge discovery and data mining to extract useful information and special features from unstructured sparse matrices and to design appropriate strategies to match sparse matrices and solvers.The outcome of this exploratory study is some important preliminary data and database to demonstrate the feasibility of building a software environment for high performance scientific computing applications based on mining sparse matrices and extracting features.

高效的数值算法在计算科学中发挥着越来越重要的作用，使大规模计算机模拟变得易于处理。超大稀疏矩阵的求解一直是许多大规模高性能计算机模拟问题中最耗时的部分之一。高性能科学计算中的重要任务之一是确定哪种求解器适合哪类应用（稀疏矩阵），以及哪种求解器可以求解哪种稀疏矩阵。我们将利用知识发现和数据挖掘中的技术和思想，从非结构化稀疏矩阵中提取有用的信息和特殊特征，并设计适当的策略来匹配稀疏矩阵和求解器。这项探索性研究的结果是一些重要的初步数据和数据库来证明基于挖掘稀疏矩阵和提取特征的高性能科学计算应用软件环境的可行性。