  This article presents svds-C, an open-source and high-performance C program
for accurately and robustly computing truncated SVD, e.g. computing several
largest singular values and corresponding singular vectors. We have
re-implemented the algorithm of svds in Matlab in C based on MKL or OpenBLAS
and multi-thread computing to obtain the parallel program named svds-C. svds-C
running on shared-memory computer consumes less time and memory than svds
thanks to careful implementation of multi-thread parallelization and memory
management. Numerical experiments on different test cases which are
synthetically generated or directly from real world datasets show that, svds-C
runs remarkably faster than svds with averagely 4.7X and at most 12X speedup
for 16-thread parallel computing on a computer with Intel CPU, while preserving
same accuracy and consuming about half memory space. Experimental results also
demonstrate that svds-C has similar advantages over svds on the computer with
AMD CPU, and outperforms other state-of-the-art algorithms for truncated SVD on
computing time and robustness.
