Efficient GPU-Accelerated Subgraph Matching

Abstract

Subgraph matching is a basic operation in graph analytics, finding all occurrences of a query graph Q in a data graph G. A common approach is to first filter out non-candidate vertices in G, and then order the vertices in Q to enumerate results. Recent work has started to utilize the GPU to accelerate subgraph matching. However, the effectiveness of current GPU-based filtering and ordering methods is limited, and the result enumeration often runs out of memory quickly. To address these problems, we propose EGSM, an efficient approach to GPU-based subgraph matching. Specifically, we design a data structure Cuckoo trie to support dynamic maintenance of candidates for filtering, and order query vertices based on estimated numbers of candidate vertices on the fly. Furthermore, we perform a hybrid breadth-first and depth-first search with memory management for result enumeration. Consequently, EGSM significantly outperforms the state-of-the-art GPU-accelerated algorithms, including GSI and CuTS.