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Publications of Torsten Hoefler
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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) )

Abstract

Betweenness 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.

Documents

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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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