Reduction to Condensed Forms for Symmetric Eigenvalue Problems on Multi-core Architectures

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 multicore 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.


Published in:
Parallel Processing And Applied Mathematics, 6067, 387-395
Presented at:
8th International Conference on Parallel Processing and Applied Mathematics, Wroclaw, POLAND, Sep 13-16, 2009
Year:
2010
Publisher:
Springer, New York
Laboratories:




 Record created 2011-05-05, last modified 2018-03-17

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