The Community for Technology Leaders
Green Image
Issue No. 08 - Aug. (2015 vol. 48)
ISSN: 0018-9162
pp: 26-34
Daniele Buono , IBM T.J. Watson Research Center
John A. Gunnels , IBM T.J. Watson Research Center
Xinyu Que , IBM T.J. Watson Research Center
Fabio Checconi , IBM T.J. Watson Research Center
Fabrizio Petrini , IBM T.J. Watson Research Center
Tai-Ching Tuan , University of Maryland
Chris Long , US Department of Defense
ABSTRACT
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.
INDEX TERMS
Data-intensive applications, Memory management, Concurrent programming, Software engineering, Data analysis, Multiprocessors, Sparse matrices
CITATION
Daniele Buono, John A. Gunnels, Xinyu Que, Fabio Checconi, Fabrizio Petrini, Tai-Ching Tuan, Chris Long, "Optimizing Sparse Linear Algebra for Large-Scale Graph Analytics", Computer, vol. 48, no. , pp. 26-34, Aug. 2015, doi:10.1109/MC.2015.228
82 ms
(Ver 3.3 (11022016))