Data distributions for sparse matrix vector multiplication

Sparse matrix vector multiplication (SpMxV) is often one of the core components of many scientific applications. Many authors have proposed methods for its data distribution in distributed memory multiprocessors. We can classify these into four groups: Scatter, D-Way Strip, Recursive and Miscellaneo...

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Veröffentlicht in:	Parallel computing Jg. 21; H. 4; S. 583 - 605
Hauptverfasser:	Romero, L.F., Zapata, E.L.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 01.04.1995 Elsevier
Schlagworte:	Algorithmics. Computability. Computer arithmetics Applied sciences Computer science; control theory; systems Data distribution Distributed memory multiprocessor Exact sciences and technology Memory and file management (including protection and security) Memory organisation. Data processing Mesh topology Parallel algorithm Performance evaluation Software Software engineering Sparse computation Sparse matrix Theoretical computing Performance evaluation Parallel algorithm Data distribution Sparse matrix Mesh topology Distributed memory multiprocessor Sparse computation Multiple recursive decomposition Decomposition method Sparse matrix vector multiplication Matrix product Distributed system Topology Generalization Multiprocessor Matrix calculus Recursive method
ISSN:	0167-8191, 1872-7336
Online-Zugang:	Volltext
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