Exploiting Graphics Processing Units to Speed Up Subgraph Enumeration for Efficient Graph Pattern Mining GraphDuMato

Abstract

Subgraph selection involves searching an input graph for subgraphs with a certain attribute. Graph pattern mining (GPM) relies on this technique, despite its computing complexity and rapid growth. Problems with uncoalesced access to memory, separation, and strain unbalance make efficient subgraph identification on GPUs a huge problem, given GPUs' success in speeding up operations across multiple industries. It is surprising that these challenges have not received sufficient attention in earlier research. This work presents new methods for effectively building and running subgraph enumeration on GPUs. Optimization of computational resource usage is achieved by combining a warp-centric design with a Depth first search (DFS) style approach. Memory efficiency, execution divergence, and GPU activity parallelization could all be enhanced by integrating these two methods. An affordable load balancing level is also incorporated for the purpose of dispersing work among thread warps, which further decreases GPU idleness. The GraphDuMato system facilitates the utilization of GPM methods with its intuitive application programming interface (API). Testing proves that GraphDuMato can outperform state-of-the-art GPM algorithms on a regular basis and may mine subgraphs with up to twelve trees.