  This paper introduces a high performance implementation of \texttt{Zolo-SVD}
algorithm on distributed memory systems, which is based on the polar
decomposition (PD) algorithm via the Zolotarev's function (\texttt{Zolo-PD}),
originally proposed by Nakatsukasa and Freund [SIAM Review, 2016]. Our
implementation highly relies on the routines of ScaLAPACK and therefore it is
portable. Compared with the other PD algorithms such as the QR-based
dynamically weighted Halley method (\texttt{QDWH-PD}), \texttt{Zolo-PD} is
naturally parallelizable and has better scalability though performs more
floating-point operations. When using many processes, \texttt{Zolo-PD} is
usually 1.20 times faster than \texttt{QDWH-PD} algorithm, and
\texttt{Zolo-SVD} can be about two times faster than the ScaLAPACK routine
\texttt{\texttt{PDGESVD}}. These numerical experiments are performed on
Tianhe-2 supercomputer, one of the fastest supercomputers in the world, and the
tested matrices include some sparse matrices from particular applications and
some randomly generated dense matrices with different dimensions. Our
\texttt{QDWH-SVD} and \texttt{Zolo-SVD} implementations are freely available at
https://github.com/shengguolsg/Zolo-SVD.
