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Breadth-First Search (BFS) is a building block used in a wide array of graphanalytics and is used in various network analysis domains: social, road,transportation, communication, and much more. Over the last two decades,network sizes have continued to grow. The popularity of BFS has brought with ita need for significantly faster traversals. Thus, BFS algorithms have beendesigned to exploit shared-memory and shared-nothing systems -- this includesalgorithms for accelerators such as the GPU. GPUs offer extremely fasttraversals at the cost of processing smaller graphs due to their limited memorysize. In contrast, CPU shared-memory systems can scale to graphs with severalbillion edges but do not have enough compute resources needed for fasttraversals. This paper introduces ButterFly BFS, a multi-GPU traversalalgorithm that allows analyzing significantly larger networks at high rates.ButterFly BFS scales to the similar-sized graphs processed by shared-memorysystems while improving performance by more than 10X compared to CPUs. Weevaluate our new algorithm on an NVIDIA DGX-2 server with 16 V100 GPUS and showthat our algorithm scales with an increase in the number of GPUS. We show thatwe can achieve a roughly $70\%$ performance linear speedup, which isnon-trivial for BFS. For a scale 29 Kronecker graph and edge factor of 8, ournew algorithm traverses the graph at a rate of over 300 GTEP/s. That is a hightraversal rate for a single server.

ButterFly BFS -- An Efficient Communication Pattern for Multi Node Traversals