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.
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