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In this paper, we propose a novel method, GSM, to compute graph matching (subgraph isomorphism) on GPUs. Unlike previous formulations of graph matching, our approach is BFS-based and thus can be mapped onto GPUs in a massively parallel fashion. Our implementation uses the Gunrock program- ming model and we evaluate our implementation in runtime and memory consumption compared with previous state-of-the- art work. We sustain a peak traversed-edges-per-second (TEPS) rate of nearly 10 GTEPS. Our algorithm is the most scalable and parallel among all existing GPU implementations and also outperforms all existing CPU distributed implementations. This work specifically focuses on leveraging our implementation on the triangle counting problem for the Subgraph Isomorphism Graph Challenge, demonstrating a geometric mean speedup over the 2018 champion of 3.84x
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Scalable Breadth-First Search on a GPU Cluster
On a GPU cluster, the ratio of high computing power to communication bandwidth makes scaling breadth-first search (BFS) on a scale-free graph extremely challenging. By separating high and low out-degree vertices, we present an implementation with scalable computation and a model for scalable communication for BFS and direction-optimized BFS. Our communication model uses global reduction for high-degree vertices, and point-to-point transmission for low-degree vertices. Leveraging the characteristics of degree separation, we reduce the graph size to one third of the conventional edge list representation. With several other optimizations, we observe linear weak scaling as we increase the number of GPUs, and achieve 259.8 GTEPS on a scale-33 Graph500 RMAT graph with 124 GPUs on the latest CORAL early access system.Proceedings of the 31st IEEE International Parallel and Distributed Processing Symposium
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- PAR ID:
- 10066971
- Date Published:
- Journal Name:
- Proceedings of the 31st IEEE International Parallel and Distributed Processing Symposium
- Page Range / eLocation ID:
- 1090 to 1101
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IPDPS.2018.00118
