Accelerating computation of eigenvectors in the nonsymmetric eigenvalue problem
Mark Gates and Azzam Haidar and Jack Dongarra
In the nonsymmetric eigenvalue problem, work has focused on the Hessenberg reduction and QR iteration, using efficient algorithms and fast, Level 3 BLAS routines. Comparatively, computation of eigenvectors performs poorly, limited to slow, Level 2 BLAS performance with little speedup on multi-core systems. It has thus become a dominant cost in the eigenvalue problem. To address this, we present improvements for the eigenvector computation to use Level 3 BLAS where applicable and parallelize the remaining triangular solves, achieving good parallel scaling and accelerating the overall eigenvalue problem more than three-fold.
Published 2014-03-03 05:00:00 as ut-eecs-14-726 (ID:584)