Name
Abstract | ||
|---|---|---|
Subspace iteration is a reliable and cost effective method for solving positive definite banded symmetric generalized eigenproblems, especially in the case of large scale problems. This paper discusses an algorithm that makes use of two parallel banded solvers in subspace iteration. A shift is introduced to decompose the banded linear systems into relatively independent subsystems and to accelerate the iterations. With this shift, an eigenproblem is mapped efficiently into the memories of a multiprocessor and a high speedup is obtained for parallel implementations. An optimal shift is a shift that balances total computation and communication costs. Under certain conditions, we show how to estimate an optimal shift analytically using the decay rate for the inverse of a banded matrix, and how to improve this estimate. Computational results on iPSC/2 and iPSC/860 multiprocessors are presented. |
Year | DOI | Venue |
|---|---|---|
1994 | 10.1016/0167-8191(94)90070-1 | Parallel Computing |
Keywords | Field | DocType |
linear algebra,generalized eigenvalue problem,banded linear system solver,timing results,linear system solvers,distributed memory multiprocessor,eigenvalues,distributed processing,cost effectiveness,decay rate,eigenvectors,computation,iterations,computer programming,parallel processing,scale,algorithms,positive definite,linear systems,linear equations,complexity,decomposition,parallel programming,linear system | Linear algebra,Linear system,Subspace topology,Computer science,Parallel algorithm,Parallel computing,Algorithm,Theoretical computer science,Eigendecomposition of a matrix,Band matrix,Eigenvalues and eigenvectors,Speedup | Journal |
Volume | Issue | ISSN |
20 | 8 | Parallel Computing |
Citations | PageRank | References |
3 | 0.56 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong Zhang | 1 | 297 | 48.54 |
| William F. Moss | 2 | 3 | 1.24 |