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Title: Fast BFS-Based Triangle Counting on GPUs
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  more » « less
Award ID(s):
1740333 1629657
NSF-PAR ID:
10171721
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Proceedings of the IEEE High Performance Extreme Computing Conference
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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