An Evaluation of Double Divide and Conquer on a Multi-core

Abstract

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. This paper presents an evaluation of parallel double Divide and Conquer for singular value decomposition on a multi-core architecture.