  The increasing number of applications requiring the solution of large scale
singular value problems have rekindled interest in iterative methods for the
SVD. Some promising recent ad- vances 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 software, 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. The numerical
experiments demonstrate its superior performance compared to other
state-of-the-art software and its good parallel performance under strong and
weak scaling.
