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

2026-04-12

AI Summary

Existing GPU-based subgraph matching approaches are hindered by coarse-grained execution models, resulting in high memory overhead, low thread utilization, and poor scalability. This work proposes a fine-grained GPU subgraph matching method that introduces warp-level batch exploration, lightweight dynamic load balancing, and fine-grained task scheduling to significantly improve hardware utilization and reduce memory consumption. Experimental results demonstrate that the proposed approach consistently outperforms state-of-the-art methods—including STMatch, T-DFS, and EGSM—across diverse real-world datasets and query patterns, while exhibiting superior capability in handling large-scale queries and data volumes.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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.

Problem

• subgraph matching
• GPU
• scalability
• memory overhead
• thread underutilization

Innovation

• fine-grained execution
• GPU acceleration
• subgraph matching
• warp-level batch exploration
• load balancing