FlexGM: An Adaptive Runtime System to Accelerate Graph Matching Networks on GPUs

GMNs (Graph Matching Networks) exploit recently developed GNNs (Graph Neural Networks) to analyze the similarity between two graphs. They are increasingly deployed in many application domains due to their improved inference accuracy. A GMN consists of two stages, i.e., node-embedding and node-matching stages. The node-matching stage matches node features from two graphs for similarity, which accounts for over 90% of the total execution time. However, it is challenging to accelerate GMNs on GPUs due to their diverse computing patterns for different graph inputs. For large graphs, the overhead comes mainly from the high computation overhead, which increases quadratically to the size of the graphs; for small graphs, the overhead comes from the low parallelism and resource utilization. In this paper, we propose FlexGM, a flexible runtime, to adaptively accelerate GMNs on GPUs. For large graphs, we exploit the massive computation redundancy in GMNs and develop a low-overhead deduplication module to mitigate the high computation overhead. For small graphs, we develop a unified matching module to optimize GPU hardware resource usage. An adaptive module manager is then developed to judiciously select beneficial optimization strategies. Experimental results show that the FlexGM system achieves 2.5x (up to 7.6x) average speedup over existing methods.