GPU-accelerated relaxed graph pattern matching algorithms

• Published in: The Journal of supercomputing Vol. 80; no. 15; pp. 21811 - 21836
• Main Authors: Benachour, Amira, Yahiaoui, Saïd, Bouhenni, Sarra, Kheddouci, Hamamache, Nouali-Taboudjemat, Nadia
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2024; Springer Nature B.V
• Subjects: Algorithms; Communication; Compilers; Computer Science; Graph matching; Graph theory; Graphics processing units; Interpreters; Isomorphism; Network analysis; Pattern matching; Processor Architectures; Programming Languages; Semantic web; Simulation; Social networks; Workers; Parallel processing; Graph simulation; GPU; Graph pattern matching
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-024-06283-7

Graph pattern matching is widely used in real-world applications, such as social network analysis. Since the traditional subgraph isomorphism is NP-complete and often too restrictive to catch sensible matches, relaxed graph pattern matching models are used. However, existing algorithms suffer from limited linear scalability and restricted degrees of parallelism. In this paper, we propose fast parallel algorithms, GPGS and GPDS, for graph simulation and dual simulation, respectively. They make most use of the GPU performance by adopting the edge-centric processing model. We perform parallel computations on the data graph edges to evaluate the matching constraints for each vertex allowing for fast and scalable algorithms. To the best of our knowledge, we present the first GPU-based algorithms for graph simulation and dual simulation. Extensive experiments on synthetic and real-world data graphs demonstrate that our algorithms significantly outperform existing methods, achieving up to 74.8 × acceleration for GPGS and up to 114.2 × acceleration for GPDS.