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 -