svds-C: A Multi-Thread C Code for Computing Truncated Singular Value Decomposition
<|reference_start|>svds-C: A Multi-Thread C Code for Computing Truncated Singular Value Decomposition: 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.<|reference_end|>
@article{feng2024svds-c:,
    title={svds-C: A Multi-Thread C Code for Computing Truncated Singular Value
  Decomposition},
    author={Xu Feng, Wenjian Yu, Yuyang Xie},
    journal={arXiv preprint arXiv:2405.18966},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18966},
    primaryClass={cs.MS}
}