We investigate the performance of the routines in LAPACK and the Successive Band Reduction (SBR) toolbox for the reduction of a dense matrix to tridiagonal form, a crucial preprocessing stage in the solution of the symmetric eigenvalue problem, on general-purpose multi-core processors. In response to the advances of hardware accelerators, we also modify the code in SBR to accelerate the computation by off-loading a significant part of the operations to a graphics processor (GPU). Performance results illustrate the parallelism and scalability of these algorithms on current high-performance multi-core architectures. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Bientinesi, P., Igual, F. D., Kressner, D., & Quintana-Ortí, E. S. (2010). Reduction to condensed forms for symmetric eigenvalue problems on multi-core architectures. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6067 LNCS, pp. 387–395). https://doi.org/10.1007/978-3-642-14390-8_40
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