Abstract
Multiprocessor architectures combining vector and parallel processing capabilities on a two-level shared memory structure have been implemented. This memory hierarchy structure requires that numerical algorithms possess good data locality in order to achieve high performance rates. The s-step Conjugate Gradient method (s-CG) is a generalization of the standard CG method with improved data locality and parallel properties. Here we show how to implement efficiently the Incomplete Cholesky and Polynomial Preconditioning with s-CG on multiprocessors with memory hierarchy.
Original language | English (US) |
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Pages (from-to) | 37-53 |
Number of pages | 17 |
Journal | Parallel Computing |
Volume | 11 |
Issue number | 1 |
DOIs | |
State | Published - Jul 1989 |
All Science Journal Classification (ASJC) codes
- Software
- Theoretical Computer Science
- Hardware and Architecture
- Computer Networks and Communications
- Computer Graphics and Computer-Aided Design
- Artificial Intelligence
Keywords
- ALLIANT FX/8
- Preconditioned conjugate gradient method
- implementation features
- model problem
- parallelisation of preconditioning