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A Divide-and-Conquer Algorithm for the Bidiagonal SVD
SIAM Journal on Matrix Analysis and Applications
Parallel programming with MPI
Applied numerical linear algebra
Applied numerical linear algebra
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On Computing an Eigenvector of a Tridiagonal Matrix. Part I: Basic Results
SIAM Journal on Matrix Analysis and Applications
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SIAM Journal on Matrix Analysis and Applications
A new singular value decomposition algorithm suited to parallelization and preliminary results
ACST'06 Proceedings of the 2nd IASTED international conference on Advances in computer science and technology
Parallelism of double divide and conquer algorithm for singular value decomposition
PDCN'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: parallel and distributed computing and networks
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This paper presents comprehensive evaluations of parallel double Divide and Conquer for singular value decomposition on a super computer, HPC2500. For bidiagonal SVD, double Divide and Conquer was proposed. It first computes singular values by a compact version of Divide and Conquer. The corresponding singular vectors are then computed by twisted factorization. The speed and accuracy of double Divide and Conquer are as good or even better than standard algorithms such as QR and the original Divide and Conquer. Moreover, it shows high scalability even on a PC cluster, distributed memory architecture. Parallel algorithm of dDC and numerical results in some architectural options, matrix sizes and types on HPC2500, SMP cluster is shown.