Scaling betweenness centrality using communication-efficient sparse matrix multiplication

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 Cen-trality (MFBC): a succinct BC algorithm based on novel sparse matrix multiplication routines that performs a factor of p1/3 less communication on p processors than the best known alternatives, for graphs withn vertices and average degreek = n/p2/3. Weformulate, 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 implementation outperforms the well-known CombBLAS library by up to 8x and shows more robust performance. Our design methodology is readily extensibl...

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 Cen-trality (MFBC): a succinct BC algorithm based on novel sparse matrix multiplication routines that performs a factor of p1/3 less communication on p processors than the best known alternatives, for graphs withn vertices and average degreek = n/p2/3. Weformulate, 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 implementation 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.