Parallel divide and conquer algorithm for the symmetric eigenvalue problem on distributed memory architectures

Françoise Tisseur, Jack Dongarra

Research output: Contribution to journalArticlepeer-review

64 Scopus citations

Abstract

We present a new parallel implementation of a divide and conquer algorithm for computing the spectral decomposition of a symmetric tridiagonal matrix on distributed memory architectures. The implementation we develop differs from other implementations in that we use a two-dimensional block cyclic distribution of the data, we use the Lowner theorem approach to compute orthogonal eigenvectors, and we introduce permutations before the back transformation of each rank-one update in order to make good use of deflation. This algorithm yields the first scalable, portable, and numerically stable parallel divide and conquer eigensolver. Numerical results confirm the effectiveness of our algorithm. We compare performance of the algorithm with that of the QR algorithm and of bisection followed by inverse iteration on an IBM SP2 and a cluster of Pentium PIIs.

Original languageEnglish
Pages (from-to)2223-2236
Number of pages14
JournalUnknown Journal
Volume20
Issue number6
DOIs
StatePublished - 1999

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