Efficient Parallel Ear Decomposition of Graphs with Application to Betweenness-Centrality

• Published in: 2016 IEEE 23rd International Conference on High Performance Computing (HiPC) pp. 301 - 310
• Main Authors: Pachorkar, Charudatt, Chaitanya, Meher, Kothapalli, Kishore, Bera, Debajyoti
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-12-2016
• Subjects: Algorithm design and analysis; betweenness-centrality; Ear; ear decomposition; GPGPU; graph algorithms; Graphics processing units; Information technology; Parallel algorithms; parallel computing; Partitioning algorithms; Vegetation
• DOI: 10.1109/HiPC.2016.043

Parallel graph algorithms continue to attract a lot of research attention given their applications to several fields of sciences and engineering. Efficient design and implementation of graph algorithms on modern manycore accelerators has to however contend with a host of challenges including not being able to reach full memory system throughput and irregularity. Of late, focusing on real-world graphs, researchers are addressing these challenges by using decomposition and preprocessing techniques guided by the structural properties of such graphs. In this direction, we present a new GPU algorithm for obtaining an ear decomposition of a graph. Our implementation of the proposed algorithm on an NVidia Tesla K40c improves the state-of-the-art by a factor of 2.3x on average on a collection of real-world and synthetic graphs. The improved performance of our algorithm is due to our proposed characterization that identifies edges of the graph as redundant for the purposes of an ear decomposition. We then study an application of the ear decomposition of a graph in computing the betweenness-centrality values of nodes in the graph. We use an ear decomposition of the input graph to systematically remove nodes of degree two. The actual computation of betweenness-centrality is done on the remaining nodes and the results are extended to nodes removed in the previous step. We show that this approach improves the state-of-the-art for computing betweenness-centrality on an NVidia K40c GPU by a factor of 1.9x on average over a collection of real-world graphs.