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.

翻译：子图匹配是图分析中的核心操作，支持从社交网络分析到生物信息学的广泛应用。近期基于GPU的方法通过利用并行性加速子图匹配，但依赖于粗粒度执行模型，因内存开销高和线程利用率不足而面临可扩展性和效率问题。本文提出gMatch，一种GPU上的硬件高效子图匹配方法。gMatch引入细粒度执行模型，降低内存消耗并实现线程间灵活的任务调度。我们进一步设计warp级批量探索和轻量级负载均衡，以提升执行效率和可扩展性。在多样化负载和真实世界数据集上的实验表明，gMatch在性能和可扩展性上均优于最先进的子图匹配方法，包括STMatch、T-DFS和EGSM。我们还与用于小模式挖掘的最先进系统（如BEEP和G$^2$Miner）进行了对比。尽管这些系统在小数据集上表现更优，但gMatch可扩展到显著更大的查询和数据集，而现有方法在此类场景下性能下降或无法完成。