TY - GEN

T1 - A faster parallel algorithm and efficient multithreaded implementations for evaluating betweenness centrality on massive datasets

AU - Madduri, Kamesh

AU - Ediger, David

AU - Jiang, Karl

AU - Bader, David A.

AU - Chavarría-Miranda, Daniel

PY - 2009

Y1 - 2009

N2 - We present a new lock-free parallel algorithm for computing betweenness centrality of massive complex networks that achieves better spatial locality compared with previous approaches. Betweenness centrality is a key kernel in analyzing the importance of vertices (or edges) in applications ranging from social networks, to power grids, to the influence of jazz musicians, and is also incorporated into the DARPA HPCS SSCA#2, a benchmark extensively used to evaluate the performance of emerging high-performance computing architectures for graph analytics. We design an optimized implementation of betweenness centrality for the massively multithreaded Cray XMT system with the Threadstorm processor. For a small-world network of 268 million vertices and 2.147 billion edges, the 16-processor XMT system achieves a TEPS rate (an algorithmic performance count for the number of edges traversed per second) of 160 million per second, which corresponds to more than a 2× performance improvement over the previous parallel implementation. We demonstrate the applicability of our implementation to analyze massive real-world datasets by computing approximate betweenness centrality for the large IMDb movie-actor network.

AB - We present a new lock-free parallel algorithm for computing betweenness centrality of massive complex networks that achieves better spatial locality compared with previous approaches. Betweenness centrality is a key kernel in analyzing the importance of vertices (or edges) in applications ranging from social networks, to power grids, to the influence of jazz musicians, and is also incorporated into the DARPA HPCS SSCA#2, a benchmark extensively used to evaluate the performance of emerging high-performance computing architectures for graph analytics. We design an optimized implementation of betweenness centrality for the massively multithreaded Cray XMT system with the Threadstorm processor. For a small-world network of 268 million vertices and 2.147 billion edges, the 16-processor XMT system achieves a TEPS rate (an algorithmic performance count for the number of edges traversed per second) of 160 million per second, which corresponds to more than a 2× performance improvement over the previous parallel implementation. We demonstrate the applicability of our implementation to analyze massive real-world datasets by computing approximate betweenness centrality for the large IMDb movie-actor network.

UR - http://www.scopus.com/inward/record.url?scp=70449792770&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70449792770&partnerID=8YFLogxK

U2 - 10.1109/IPDPS.2009.5161100

DO - 10.1109/IPDPS.2009.5161100

M3 - Conference contribution

AN - SCOPUS:70449792770

SN - 9781424437504

T3 - IPDPS 2009 - Proceedings of the 2009 IEEE International Parallel and Distributed Processing Symposium

BT - IPDPS 2009 - Proceedings of the 2009 IEEE International Parallel and Distributed Processing Symposium

T2 - 23rd IEEE International Parallel and Distributed Processing Symposium, IPDPS 2009

Y2 - 23 May 2009 through 29 May 2009

ER -