Abstract
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.
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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: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2009. Lecture Notes in Computer Science, vol 6067. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14390-8_40
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DOI: https://doi.org/10.1007/978-3-642-14390-8_40
Publisher Name: Springer, Berlin, Heidelberg
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