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For parallel breadth first search (BFS) algorithm on large-scale distributedmemory systems, communication often costs significantly more than arithmeticand limits the scalability of the algorithm. In this paper we sufficientlyreduce the communication cost in distributed BFS by compressing and sieving themessages. First, we leverage a bitmap compression algorithm to reduce the sizeof messages before communication. Second, we propose a novel distributeddirectory algorithm, cross directory, to sieve the redundant data in messages.Experiments on a 6,144-core SMP cluster show our algorithm outperforms thebaseline implementation in Graph500 by 2.2 times, reduces its communicationtime by 79.0%, and achieves a performance rate of 12.1 GTEPS (billion edgevisits per second)

Compression and Sieve: Reducing Communication in Parallel Breadth First Search on Distributed Memory Systems