Abstract
Breadth-first search (BFS) is a basic algorithm for graph processing. It is a very important algorithm because a number of graph-processing algorithms use breadth-first search as a sub-routine. Recently, large-scale graphs have been used in various fields, and there is a growing need for an efficient approach by which to process large-scale graphs. In the present paper, we present a hybrid BFS implementation on a GPU for efficient traversal of a complex network, and we achieved a speedup of up to 29x, as compared to the previous GPU implementation. We also applied an implementation for GPUs on a distributed memory system. This implementation achieved a speed of 117.546 GigaTEPS on a 256-node HA-PACS cluster with 1,024 NVIDIA M2090 GPUs and was ranked 39th on the June 2013 Graph500 list.
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Hiragushi, T., Takahashi, D. (2013). Efficient Hybrid Breadth-First Search on GPUs. In: Aversa, R., Kołodziej, J., Zhang, J., Amato, F., Fortino, G. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2013. Lecture Notes in Computer Science, vol 8286. Springer, Cham. https://doi.org/10.1007/978-3-319-03889-6_5
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DOI: https://doi.org/10.1007/978-3-319-03889-6_5
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