### Abstract

This chapter proposes an improved edge-parallel approach for computing centrality metrics that can also accelerate breadth-first search and all-pairs shortest path. For some graph algorithms such as computing centrality, breadth-first search, and even all-pairs shortest path, an edge-parallel approach improves graphical processing unit (GPU) throughput with better load balancing and less thread divergence on scale-free networks. The edge-parallel approach is less appropriate for grids, meshes, and other graphs with low-degree variance, and it performs less well on dense graphs with many edges. The edge-parallel approach requires more GPU memory than node-parallel, which could limit its application to larger graphs than the considered, for which one could investigate efficient inter-block synchronization techniques. It has been believed the edge-parallel approach would benefit most scale-free network applications and should be investigated on further graph algorithms such as max flow.

Original language | English (US) |
---|---|

Title of host publication | GPU Computing Gems Jade Edition |

Publisher | Elsevier Inc. |

Pages | 15-28 |

Number of pages | 14 |

ISBN (Print) | 9780123859631 |

DOIs | |

State | Published - Dec 1 2012 |

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### ASJC Scopus subject areas

- Computer Science(all)

### Cite this

*GPU Computing Gems Jade Edition*(pp. 15-28). Elsevier Inc.. https://doi.org/10.1016/B978-0-12-385963-1.00002-2

**Edge v. Node Parallelism for Graph Centrality Metrics.** / Jia, Yuntao; Lu, Victor; Hoberock, Jared; Garland, Michael; Hart, John C.

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

*GPU Computing Gems Jade Edition.*Elsevier Inc., pp. 15-28. https://doi.org/10.1016/B978-0-12-385963-1.00002-2

}

TY - CHAP

T1 - Edge v. Node Parallelism for Graph Centrality Metrics

AU - Jia, Yuntao

AU - Lu, Victor

AU - Hoberock, Jared

AU - Garland, Michael

AU - Hart, John C

PY - 2012/12/1

Y1 - 2012/12/1

N2 - This chapter proposes an improved edge-parallel approach for computing centrality metrics that can also accelerate breadth-first search and all-pairs shortest path. For some graph algorithms such as computing centrality, breadth-first search, and even all-pairs shortest path, an edge-parallel approach improves graphical processing unit (GPU) throughput with better load balancing and less thread divergence on scale-free networks. The edge-parallel approach is less appropriate for grids, meshes, and other graphs with low-degree variance, and it performs less well on dense graphs with many edges. The edge-parallel approach requires more GPU memory than node-parallel, which could limit its application to larger graphs than the considered, for which one could investigate efficient inter-block synchronization techniques. It has been believed the edge-parallel approach would benefit most scale-free network applications and should be investigated on further graph algorithms such as max flow.

AB - This chapter proposes an improved edge-parallel approach for computing centrality metrics that can also accelerate breadth-first search and all-pairs shortest path. For some graph algorithms such as computing centrality, breadth-first search, and even all-pairs shortest path, an edge-parallel approach improves graphical processing unit (GPU) throughput with better load balancing and less thread divergence on scale-free networks. The edge-parallel approach is less appropriate for grids, meshes, and other graphs with low-degree variance, and it performs less well on dense graphs with many edges. The edge-parallel approach requires more GPU memory than node-parallel, which could limit its application to larger graphs than the considered, for which one could investigate efficient inter-block synchronization techniques. It has been believed the edge-parallel approach would benefit most scale-free network applications and should be investigated on further graph algorithms such as max flow.

UR - http://www.scopus.com/inward/record.url?scp=84882551380&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84882551380&partnerID=8YFLogxK

U2 - 10.1016/B978-0-12-385963-1.00002-2

DO - 10.1016/B978-0-12-385963-1.00002-2

M3 - Chapter

AN - SCOPUS:84882551380

SN - 9780123859631

SP - 15

EP - 28

BT - GPU Computing Gems Jade Edition

PB - Elsevier Inc.

ER -