Symmetric eigenvalue problem on a multiprocessor
Technical Report
·
OSTI ID:7008107
This paper is a survey of optimal methods for solving the symmetric eigenvalue problem on a multiprocessor. Although the inherent parallelism of the Jacobi method is well known, the convergence is not insured and the total number of operations is larger than that of the methods which transform the full matrix into a tridiagonal matrix. In this paper we are concerned with the methods which deal with the tridiagonalized eigenvalue systems. We review the sequential methods for solving symmetric eigenvalue problems by tridiagonal reduction using the appropriate routines from EISPACK. We also discuss the different ways of exploiting parallelism, examples of linear recurrence and orthogonalization are presented. Section 4 and Section 5 deal with the two algorithms, SESUPD, a parallel version of TQL2 in EISPACK, and TREPS, a parallel version of BISECT + TINVIT, respectively.
- Research Organization:
- Illinois Univ., Urbana (USA). Center for Supercomputing Research and Development; Institut National de Recherche d'Informatique et d'Automatique (INRIA), 35 - Rennes (France)
- DOE Contract Number:
- FG02-85ER25001
- OSTI ID:
- 7008107
- Report Number(s):
- DOE/ER/25001-13; ON: DE87002103
- Country of Publication:
- United States
- Language:
- English
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