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In this paper, we propose a GPU-efficient subgraph isomorphism algorithmusing the Gunrock graph analytic framework, GSM (Gunrock Subgraph Matching), tocompute graph matching on GPUs. In contrast to previous approaches on the CPUwhich are based on depth-first traversal, GSM is BFS-based: possible matchesare explored simultaneously in a breadth-first strategy. The advantage of usingBFS-based traversal is that we can leverage the massively parallel processingcapabilities of the GPU. The disadvantage is the generation of moreintermediate results. We propose several optimization techniques to cope withthe problem. Our implementation follows a filtering-and-verification strategy.While most previous work on GPUs requires one-/two-step joining, we useone-step verification to decide the candidates in current frontier of nodes.Our implementation has a speedup up to 4x over previous GPU state-of-the-artimplementation.

Fast Gunrock Subgraph Matching (GSM) on GPUs