PERFORMANCE EVALUATION OF DOMAIN DECOMPOSITION METHOD WITH SPARSE MATRIX STORAGE SCHEMES IN MODERN SUPERCOMPUTER

Autor:	Ryuji Shioya, Abul Mukid Mohammad Mukaddes, Masao Ogino
Rok vydání:	2014
Předmět:	Computational Mathematics Matrix-free methods Skyline matrix Computer science Computer Science (miscellaneous) Sparse matrix-vector multiplication Domain decomposition methods Parallel computing Sparse approximation Supercomputer Storage efficiency Sparse matrix
Zdroj:	International Journal of Computational Methods. 11:1344007
ISSN:	1793-6969 0219-8762
DOI:	10.1142/s0219876213440076
Popis:	The use of proper data structures with corresponding algorithms is critical to achieve good performance in scientific computing. The need of sparse matrix vector multiplication in each iteration of the iterative domain decomposition method has led to implementation of a variety of sparse matrix storage formats. Many storage formats have been presented to represent sparse matrix and integrated in the method. In this paper, the storage efficiency of those sparse matrix storage formats are evaluated and compared. The performance results of sparse matrix vector multiplication used in the domain decomposition method is considered. Based on our experiments in the FX10 supercomputer system, some useful conclusions that can serve as guidelines for the optimization of domain decomposition method are extracted.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::fc49df17611675f50aca9148c8f2d69f https://doi.org/10.1142/s0219876213440076 Zobrazit plný text záznamu