gMatch: Fine-Grained and Hardware-Efficient Subgraph Matching on GPUs

TL;DR

gMatch introduces a fine-grained, hardware-efficient GPU-based approach for subgraph matching, significantly improving scalability and performance over existing methods on large datasets.

Contribution

It proposes a novel fine-grained execution model and load balancing techniques that enhance GPU-based subgraph matching efficiency and scalability.

Findings

gMatch outperforms state-of-the-art methods like STMatch, T-DFS, and EGSM in performance and scalability.

gMatch scales to larger queries and datasets where existing approaches struggle.

Experiments on diverse workloads and real-world datasets validate the effectiveness of gMatch.

Abstract

Subgraph matching is a core operation in graph analytics, supporting a broad spectrum of applications from social network analysis to bioinformatics. Recent GPU-based approaches accelerate subgraph matching by leveraging parallelism but rely on a coarse-grained execution model that suffers from scalability and efficiency issues due to high memory overhead and thread underutilization. In this paper, we propose gMatch, a hardware-efficient subgraph matching approach on GPUs. gMatch introduces a fine-grained execution model that reduces memory consumption and enables flexible task scheduling among threads. We further design warp-level batch exploration and lightweight load balancing to improve execution efficiency and scalability. Experiments on diverse workloads and real-world datasets show that gMatch outperforms state-of-the-art subgraph matching methods, including STMatch, T-DFS, and EGSM, in both performance and scalability. We also compare against state-of-the-art systems for mining small patterns, such as BEEP and G$^2$Miner. While these systems achieve better performance on small datasets, gMatch scales to substantially larger queries and datasets, where existing approaches degrade or fail to complete.