SOFTWARE: A Software Environment for High Performance Scientific Computing Applications

高效的数值算法在计算科学中发挥着越来越重要的作用，使大规模的计算机模拟易于处理。超大稀疏矩阵的求解一直是许多大规模高性能计算机仿真问题中最耗时的部分之一。高性能科学计算的重要任务之一是确定哪个求解器适用于哪种类型的应用（稀疏矩阵），以及哪个稀疏矩阵可以由哪个求解器求解。我们将使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。本探索性研究的成果为构建基于稀疏矩阵挖掘和特征提取的高性能科学计算应用软件环境的可行性提供了一些重要的初步数据和数据库。

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.