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Sci. Comput."],"published-print":{"date-parts":[[2017,1]]},"abstract":"<jats:p>The increasing number of applications requiring the solution of large-scale singular value problems has rekindled an interest in iterative methods for the SVD. Some promising recent advances in large-scale iterative methods are still plagued by slow convergence and accuracy limitations for computing smallest singular triplets. Furthermore, their current implementations in MATLAB cannot address the required large problems. Recently, we presented a preconditioned, two-stage method to effectively and accurately compute a small number of extreme singular triplets. In this research, we present a high-performance library, PRIMME_SVDS, that implements our hybrid method based on the state-of-the-art eigensolver package PRIMME for both largest and smallest singular values. PRIMME_SVDS fills a gap in production level software for computing the partial SVD, especially with preconditioning. 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