About Adam McLaughlin

Adam McLaughlin is a Ph.D. student in the High Performance Computing Lab under Professor David Bader at Georgia Tech as well as an intern for the Programming Systems and Applications team lead by Michael Garland at NVIDIA Research. His current research focuses on utilizing GPUs for fast parallel execution of algorithms that traverse unstructured network data sets such as crawls of the internet, road maps, or social networks. When he's not using grep he can likely be found listening (or better, karaoking) to post-hardcore acts (such as Dance Gavin Dance and Hail the Sun), reminiscing his days as a semi-professional poker player, or laughing at improv, sketch, and stand-up comedy.
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Accelerating Graph Betweenness Centrality with CUDA

Graph analysis is a fundamental tool for domains as diverse as social networks, computational biology, and machine learning. Real-world applications of graph algorithms involve tremendously large networks that cannot be inspected manually. Betweenness Centrality (BC) is a popular analytic that determines vertex influence in a graph. It has many practical use cases, including finding the best locations for stores within cities, power grid contingency analysis, and community detection. Unfortunately, the fastest known algorithm for computing betweenness centrality has O(mn) time complexity for graphs with n vertices and m edges, making the analysis of large networks challenging.

This post describes how we used CUDA and NVIDIA GPUs to accelerate the BC computation, and how choosing efficient parallelization strategies results in an average speedup of 2.7x, and more than 10x speedup for road networks and meshes versus a naïve edge-parallel strategy.

Example Betweenness Centrality scores for a small graph
Fig. 1. Example Betweenness Centrality scores for a small graph

Betweenness Centrality determines the importance of vertices in a network by measuring the ratio of shortest paths passing through a particular vertex to the total number of shortest paths between all pairs of vertices. Intuitively, this ratio determines how well a vertex connects pairs of vertices in the network. Formally, the Betweenness Centrality of a vertex v is defined as:

BC(v) = \sum_{s \neq t \neq v} \frac{\sigma_{st}(v)}{\sigma_{st}}

where \sigma_{st} is the number of shortest paths between vertices s and t and \sigma_{st}(v) is the number of those shortest paths that pass through v. Consider Figure 1 above. Vertex 4 is the only vertex that lies on paths from its left (vertices 5 through 9) to its right (vertices 1 through 3). Hence vertex 4 lies on all the shortest paths between these pairs of vertices and has a high BC score. In contrast, vertex 9 does not belong on a path between any pair of the remaining vertices and thus it has a BC score of 0. Continue reading