Distributed Breadth-First Search with 2-D Partitioning

Many emerging large-scale data science applications require searching large graphs distributed across multiple memories and processors. This paper presents a scalable implementation of distributed breadth-first search (BFS) which has been applied to graphs with over one billion vertices. The main contribution of this paper is to compare a 2-D (edge) partitioning of the graph to the more common 1-D (vertex) partitioning. For Poisson random graphs which have low diameter like many realistic information network data, we determine when one type of partitioning is advantageous over the other. Also for Poisson random graphs, we show that memory use is scalable. The experimental tests use a level-synchronized BFS algorithm running on a large Linux cluster and BlueGene/L. On the latter machine, the timing is related to the number of synchronization steps in the algorithm.

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