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E. Solomonik, M. Besta, F. Vella, T. Hoefler:
| | Scaling Betweenness Centrality using Communication-Efficient Sparse Matrix Multiplication
(Nov. 2017, Accepted at The International Conference for High Performance Computing, Networking, Storage and Analysis (SC'17) )
AbstractBetweenness centrality (BC) is a crucial graph problem that measures the significance of a vertex by the number of shortest paths
leading through it. We propose Maximal Frontier Betweenness Centrality (MFBC): a succinct BC algorithm based on novel sparse matrix multiplication routines that performs a factor of p 1/3 less communication on p processors than the best known alternatives, for
graphs with n vertices and average degree k = n/p 2/3 . We formulate,
implement, and prove the correctness of MFBC for weighted graphs
by leveraging monoids instead of semirings, which enables a surprisingly succinct formulation. MFBC scales well for both extremely
sparse and relatively dense graphs. It automatically searches a space
of distributed data decompositions and sparse matrix multiplication
algorithms for the most advantageous configuration. The MFBC im-
plementation outperforms the well-known CombBLAS library by
up to 8x and shows more robust performance. Our design methodology is readily extensible to other graph problems.
Documentsdownload article:  | | BibTeX | @inproceedings{, author={E. Solomonik and M. Besta and F. Vella and T. Hoefler}, title={{Scaling Betweenness Centrality using Communication-Efficient Sparse Matrix Multiplication}}, year={2017}, month={Nov.}, note={Accepted at The International Conference for High Performance Computing, Networking, Storage and Analysis (SC'17)}, source={http://www.unixer.de/~htor/publications/}, } |
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