{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-new-high-performance-and-scalable-svd","title":"A New High Performance and Scalable SVD algorithm on Distributed Memory Systems","arxiv_id":"1806.06204","date":"2018-06-16","proceeding":null,"authors":["Shengguo Li","Jie Liu","Yunfei Du"],"abstract":"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.","url_abs":"http://arxiv.org/abs/1806.06204v1","url_pdf":"http://arxiv.org/pdf/1806.06204v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"a-new-high-performance-and-scalable-svd","repo_url":"https://github.com/shengguolsg/Zolo-SVD","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}