Principal Investigator: Jun Zhang
Graduate Research Assistants: Eun-Joo Lee, and Shuting Xu
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 techniques and 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 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.
The project will train two female graduate students in high performance computational sciences. One student will work on testing sparse matrices using standard preconditioners. The other will build data representation of the sparse matrices and features. Preliminary research results will be presented at the 3rd International Conference on Preconditioning Techniques for Large Scale Industrial Applications, to be held in California in October 2003. A full scale research proposal will be submitted to NSF in July 2003.
Conference, Workshop, and Seminar Presentations:
This page is supported by the U.S. National Science Foundation. However, any opinions, findings, and conclusions or recommendations expressed in this documents are those of the author and do not necessarily reflect the views of the U.S. National Science Foundation.
Go back to Funded Research Projects page.