Reduction to Condensed Forms for Symmetric Eigenvalue Problems on Multi-core Architectures
نویسندگان
چکیده
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. The target architecture is a current general purpose multi-core processor, where parallelism is extracted using a tuned multi-threaded implementation of BLAS. Also, in response to the advances of hardware accelerators, we modify the code in SBR to accelerate the computation by off-loading a significant part of the operations to a graphics processor (GPU). Our results on a system with two Intel QuadCore processors and a Tesla C1060 GPU illustrate the performance and scalability delivered by these architectures.
منابع مشابه
Condensed forms for the symmetric eigenvalue problem on multi-threaded 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 generalpurpose multi-core processors. In response to the advances of hardware accelerators, we also modify the code in the SBR toolbox to accelerate ...
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