SGSI – A Scalable GPU-Friendly Subgraph Isomorphism Algorithm

Li Zeng, Lei Zou, M. Tamer Özsu

Abstract: Due to the inherent hardness of subgraph isomorphism, the performance is often a bottleneck in various real-world applications. We address this by designing an efficient subgraph isomorphism algorithm leveraging features of GPU architecture. Existing GPU-based solutions adopt two-step output scheme, performing the same join twice in order to write intermediate results concurrently. They also lack GPU architecture-aware optimizations that allow scaling to large graphs. In this article, we propose a Scalable GPU-friendly subgraph isomorphism algorithm, SGSI. SGSI incorporates a Prealloc-Combine strategy based on the vertex-oriented framework, which avoids joining-twice in existing solutions. It uses a GPU-friendly data structure (called PCSR) to represent an edge-labeled graph. We also study fine-grained load balance strategies and discuss how to handle enormous graphs that cannot be resident in GPU memory. A partition-based pipeline framework is proposed. Extensive experiments on both synthetic and real graphs show that SGSI outperforms the state-of-the-art algorithms by up to several orders of magnitude and has a good scalability with graph size scaling to billions of edges.

External IDs:doi:10.1109/tkde.2022.3230744