Multiprocessor Jacobi algorithms for dense symmetric eigenvalue and singular value decompositions
Two parallel algorithms are presented based on Jacobi's method for real symmetric matrices to determine the complete eigensystem of a dense real symmetric matrix and the singular value decomposition of rectangular matrices on a multiprocessor. The intent is to study the advantages of using Jacobi and Jacobi-like schemes over new and existing EISPACK and LINPACK routines on an Alliant FX/8 computer system. For the dense symmetric eigenvalue problem, promising results are shown for small-order matrices. A ''one-sided'' Jacobi-like algorithm which produces the singular value decomposition of a rectangular matrix is shown to provide superior performance for rectangular matrices in which the number of rows is much larger than the number of columns. 17 refs., 9 figs., 5 tabs.
- Research Organization:
- Illinois Univ., Urbana (USA). Center for Supercomputing Research and Development
- DOE Contract Number:
- FG02-85ER25001
- OSTI ID:
- 7162999
- Report Number(s):
- DOE/ER/25001-12; CONF-860877-2; CSRD-546; ON: DE87002102
- Resource Relation:
- Conference: International conference on parallel processing, St. Charles, IL, USA, 19 Aug 1986; Other Information: Portions of this document are illegible in microfiche products
- Country of Publication:
- United States
- Language:
- English
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