Optimizing Sparse Linear Algebra for Large-Scale Graph Analytics.

Emerging data-intensive applications attempt to process and provide insight into vast amounts of online data. A new class of linear algebra algorithms can efficiently execute sparse matrix-matrix and matrix-vector multiplications on large-scale, shared memory multiprocessor systems, enabling analyst...

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Detalles Bibliográficos
Publicado en:	Computer (00189162) Vol. 48; no. 8; pp. 26 - 35
Autores principales:	Buono, Daniele, Gunnels, John A., Que, Xinyu, Checconi, Fabio, Petrini, Fabrizio, Tuan, Tai-Ching, Long, Chris
Formato:	Artículo
Publicado:	IEEE Aug2015
Materias:	Online data processing Linear algebra Computer storage devices Multiprocessors Social networks
Acceso en línea:	Ver este registro en EBSCOhost

Descripción
Sumario:	Emerging data-intensive applications attempt to process and provide insight into vast amounts of online data. A new class of linear algebra algorithms can efficiently execute sparse matrix-matrix and matrix-vector multiplications on large-scale, shared memory multiprocessor systems, enabling analysts to more easily discern meaningful data relationships, such as those in social networks.