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 Centrality (MFBC): a succinct BC algorithm based on novel sparse matrix multiplication routines that performs...

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Veröffentlicht in:	International Conference for High Performance Computing, Networking, Storage and Analysis (Online) S. 1 - 14
Hauptverfasser:	Solomonik, Edgar, Besta, Maciej, Vella, Flavio, Hoefler, Torsten
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	New York, NY, USA ACM 12.11.2017
Schriftenreihe:	ACM Conferences
Schlagworte:	Betweenness centrality communication cost Computing methodologies > Parallel computing methodologies > Parallel algorithms > Massively parallel algorithms Computing methodologies > Symbolic and algebraic manipulation > Symbolic and algebraic algorithms > Algebraic algorithms Design methodology Distributed databases High performance computing Mathematics of computing > Mathematical software > Mathematical software performance parallel algorithm Program processors Software algorithms Software performance sparse matrix multiplication Tensors Theory of computation > Design and analysis of algorithms > Parallel algorithms > Massively parallel algorithms communication cost sparse matrix multiplication betweenness centrality parallel algorithm
ISBN:	9781450351140, 145035114X
ISSN:	2167-4337
Online-Zugang:	Volltext
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