GPU-based parallel householder bidiagonalization

Liu, Fangbin; Seinstra, F.J.

doi:10.1145/1851476.1851512

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“…Alternatively, parallelization is possible on an algorithmic level. For example, it is possible to apply independent reflections simultaneously; the bidiagonalization has been mapped to graphical processing (GPU) units [26] and to a distributed cluster [25]. Load balancing is an issue for such parallel algorithms, however, because the number of off-diagonal columns (or rows) to eliminate get successively smaller.…”

Section: Introductionmentioning

confidence: 99%

A Distributed and Incremental SVD Algorithm for Agglomerative Data Analysis on Large Networks

Iwen¹,

Ong²

2016

SIAM J. Matrix Anal. & Appl.

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In this paper it is shown that the SVD of a matrix can be constructed efficiently in a hierarchical approach. The proposed algorithm is proven to recover the singular values and left singular vectors of the input matrix A if its rank is known. Further, the hierarchical algorithm can be used to recover the d largest singular values and left singular vectors with bounded error. It is also shown that the proposed method is stable with respect to roundoff errors or corruption of the original matrix entries. Numerical experiments validate the proposed algorithms and parallel cost analysis.

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Section: Introductionmentioning

confidence: 99%