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svds-C: A Multi-Thread C Code for Computing Truncated Singular Value Decomposition

Authors :
Feng, Xu
Yu, Wenjian
Xie, Yuyang
Publication Year :
2024

Abstract

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.<br />Comment: 20 pages, accepted by SoftwareX

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2405.18966
Document Type :
Working Paper