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Many emerging large-scale data science applications require searching large graphs distributed across multiple memories and processors. This paper presents a distributed breadthfirst search (BFS) scheme that scales for random graphs with up to three billion vertices and 30 billion edges. Scalability was tested on IBM BlueGene/L with 32,768 nodes at the Lawrence Livermore National Laboratory. Scalability was obtained through a series of optimizations, in particular, those that ensure scalabledoi:10.1109/sc.2005.4 dblp:conf/sc/YooCHMHC05 fatcat:pnzpqjnprrg6vkqmlntgb4ohki