A Parallel QZ Algorithm For Distributed Memory HPC Systems

Appearing frequently in applications, generalized eigenvalue problems represent one of the core problems in numerical linear algebra. The QZ algorithm of Moler and Stewart is the most widely used algorithm for addressing such problems. Despite its importance, little attention has been paid to the parallelization of the QZ algorithm. The purpose of this work is to fill this gap. We propose a parallelization of the QZ algorithm that incorporates all modern ingredients of dense eigensolvers, such as multishift and aggressive early deflation techniques. To deal with (possibly many) infinite eigenvalues, a new parallel deflation strategy is developed. Numerical experiments for several random and application examples demonstrate the effectiveness of our algorithm on two different distributed memory HPC systems.


Publié dans:
SIAM Journal On Scientific Computing, 36, 5, C480-C503
Année
2014
Publisher:
Philadelphia, Siam Publications
Mots-clefs:
Laboratoires:




 Notice créée le 2015-02-20, modifiée le 2018-09-13


Évaluer ce document:

Rate this document:
1
2
3
 
(Pas encore évalué)