Abstract
Breadth-first search (BFS) is widely used in web link and social network analysis as well as other fields. The Graphics Processing Unit (GPU) has been demonstrated to have great potential in accelerating graph algorithms through parallel processing. However, BFS is difficult to parallelize efficiently due to the irregular workload distribution, leading to load imbalance between threads. Previous work has proposed several strategies to alleviate the load imbalance but none of them solves this issue in general.
This paper presents a new GPU BFS algorithm that focuses on full load balance. Each BFS iteration is decoupled into two phases: work redistribution and neighbor gathering. Work redistribution phase reorganizes the irregular workloads in order for the neighbor gathering phase to visit the vertices in a load-balanced way. The evaluation results show that the proposed approach achieves speedups of up to 39x and 1.42x over CPU sequential implementation and state-of-the-art GPU implementation respectively.
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Zhu, Z., Li, J., Li, G. (2014). Load-Balanced Breadth-First Search on GPUs. In: Li, F., Li, G., Hwang, Sw., Yao, B., Zhang, Z. (eds) Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science, vol 8485. Springer, Cham. https://doi.org/10.1007/978-3-319-08010-9_46
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DOI: https://doi.org/10.1007/978-3-319-08010-9_46
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