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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Vydané v:	International Conference for High Performance Computing, Networking, Storage and Analysis (Online) s. 1 - 14
Hlavní autori:	Solomonik, Edgar, Besta, Maciej, Vella, Flavio, Hoefler, Torsten
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	New York, NY, USA ACM 12.11.2017
Edícia:	ACM Conferences
Predmet:	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
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