  In this paper, we propose a novel method to compute triangle counting on
GPUs. Unlike previous formulations of graph matching, our approach is BFS-based
by traversing the graph in an all-source-BFS manner and thus can be mapped onto
GPUs in a massively parallel fashion. Our implementation uses the Gunrock
programming 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 2019, demonstrating a
geometric mean speedup over the 2018 champion of 3.84x.
