Name
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 Zolotarevu0027s 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 this https URL |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Distributed, Parallel, and Cluster Computing | Parallelizable manifold,Singular value decomposition,Supercomputer,Matrix (mathematics),Computer science,Algorithm,Polar decomposition,ScaLAPACK,Sparse matrix,Scalability |
DocType | Volume | Citations |
Journal | abs/1806.06204 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shengguo Li | 1 | 0 | 0.34 |
| Jie Liu | 2 | 56 | 5.62 |
| Yunfei Du | 3 | 72 | 14.62 |