Abstract
Sparse approximate inverse (SAI) techniques have recently emerged as a new class of parallel preconditioning techniques for solving large sparse linear systems on high performance computers. The choice of the sparsity pattern of the SAI matrix is probably the most important step in constructing an SAI preconditioner. Both dynamic and static sparsity pattern selection approaches have been proposed by researchers. Through a few numerical experiments, we conduct a comparable study on the properties and performance of the SAI preconditioners using the different sparsity patterns for solving some sparse linear systems.
| Original language | English |
|---|---|
| Pages (from-to) | 203-215 |
| Number of pages | 13 |
| Journal | Journal of Mathematical Modelling and Algorithms |
| Volume | 2 |
| Issue number | 3 |
| DOIs | |
| State | Published - 2003 |
Bibliographical note
Funding Information:★ This research was supported in part by the U.S. National Science Foundation under grants CCR-9902022, CCR-9988165, CCR-0092532, and ACI-0202934, in part by the Japan Research Organization for Information Science & Technology, and in part by the University of Kentucky Research Committee.
Funding
\u2605 This research was supported in part by the U.S. National Science Foundation under grants CCR-9902022, CCR-9988165, CCR-0092532, and ACI-0202934, in part by the Japan Research Organization for Information Science & Technology, and in part by the University of Kentucky Research Committee.
| Funders | Funder number |
|---|---|
| Japan Research Organization for Information Science and Technology | |
| University of Kentucky Research Committee | |
| U.S. Department of Energy Chinese Academy of Sciences Guangzhou Municipal Science and Technology Project Oak Ridge National Laboratory Extreme Science and Engineering Discovery Environment National Science Foundation National Energy Research Scientific Computing Center National Natural Science Foundation of China | CCR-0092532, CCR-9902022, ACI-0202934, 0202934, CCR-9988165 |
Keywords
- Dynamic and static sparsity pattern
- Parallel preconditioning
- Sparse approximate inverse
- Sparse matrices
ASJC Scopus subject areas
- Modeling and Simulation
- Applied Mathematics