### Abstract

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.

Original language | English (US) |
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Title of host publication | IPDPS 2009 - Proceedings of the 2009 IEEE International Parallel and Distributed Processing Symposium |

DOIs | |

State | Published - Nov 25 2009 |

Event | 23rd IEEE International Parallel and Distributed Processing Symposium, IPDPS 2009 - Rome, Italy Duration: May 23 2009 → May 29 2009 |

### Publication series

Name | IPDPS 2009 - Proceedings of the 2009 IEEE International Parallel and Distributed Processing Symposium |
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### Other

Other | 23rd IEEE International Parallel and Distributed Processing Symposium, IPDPS 2009 |
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Country | Italy |

City | Rome |

Period | 5/23/09 → 5/29/09 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Computational Theory and Mathematics
- Hardware and Architecture
- Software

### Cite this

*IPDPS 2009 - Proceedings of the 2009 IEEE International Parallel and Distributed Processing Symposium*[5161100] (IPDPS 2009 - Proceedings of the 2009 IEEE International Parallel and Distributed Processing Symposium). https://doi.org/10.1109/IPDPS.2009.5161100

}

*IPDPS 2009 - Proceedings of the 2009 IEEE International Parallel and Distributed Processing Symposium.*, 5161100, IPDPS 2009 - Proceedings of the 2009 IEEE International Parallel and Distributed Processing Symposium, 23rd IEEE International Parallel and Distributed Processing Symposium, IPDPS 2009, Rome, Italy, 5/23/09. https://doi.org/10.1109/IPDPS.2009.5161100

**A faster parallel algorithm and efficient multithreaded implementations for evaluating betweenness centrality on massive datasets.** / Madduri, Kamesh; Ediger, David; Jiang, Karl; Bader, David A.; Chavarría-Miranda, Daniel.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 - 2009/11/25

Y1 - 2009/11/25

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

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

ER -