DGSM: A GPU-Based Subgraph Isomorphism framework with DFS exploration

Subgraph Isomorphism is a fundamental problem in graph analytics and it has been applied to many domains. It is well known that subgraph isomorphism is an NP-complete problem. Thus, it generally becomes bottle-neck of the applications to which it is applied. There has been a lot of efforts devoted to this problem in the past two decades. However, GPU-based subgraph isomorphism systems are relatively rare since the GPU memory is not big enough to hold all the instances during the matching process. Most current GPU subgraph isomorphism frameworks suffer from the limited GPU main memory and redundant computation. These issues restrict them on smaller patterns and graphs and limit their performance. To overcome these issues, we design a new GPU-based sub-graph isomorphism system named DGSM. Our system also efficiently utilize special architecture features to improve data parallelism and memory bandwidth for matching. We validate our techniques by comparing with two state-of-the-art systems, CPU-based DAF and GPU-based GSI. Our experimental results show that our system achieve 2 orders of magnitude faster than DAF and GSI on both labeled and unlabeled graph.