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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Bibliographic Details
Published in:	Computer (Long Beach, Calif.) Vol. 48; no. 8; pp. 26 - 34
Main Authors:	Buono, Daniele, Gunnels, John A., Xinyu Que, Checconi, Fabio, Petrini, Fabrizio, Tai-Ching Tuan, Long, Chris
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.08.2015 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algebra Algorithms Computer memory Concurrent programming Data analysis Data-intensive applications graph analytics irregular applications Memory management Multiprocessing Multiprocessors shared memory multiprocessor systems Social networks software Software engineering sparse linear algebra Sparse matrices Sparsity system performance
ISSN:	0018-9162, 1558-0814
Online Access:	Get full text
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