cuRPQ: A High-Performance GPU-Based Framework for Processing Regular and Conjunctive Regular Path Queries

2026-02-24

AI Summary

This work addresses the high computational cost of regular path queries (RPQs) and their more expressive variants, such as conjunctive regular path queries (CRPQs), in graph analysis, for which efficient GPU acceleration has been lacking. We present cuRPQ, the first framework to harness GPU acceleration for RPQ/CRPQ evaluation, introducing a novel parallel traversal mechanism tailored to GPU architectures, an efficient access set management scheme, and a concurrent exploration-materialization strategy. Experimental results demonstrate that cuRPQ achieves performance improvements of several orders of magnitude over the current state-of-the-art methods while effectively mitigating out-of-memory issues.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Regular path queries (RPQs) are fundamental for path-constrained reachability analysis, and more complex variants such as conjunctive regular path queries (CRPQs) are increasingly used in graph analytics. Evaluating these queries is computationally expensive, but to the best of our knowledge, no prior work has explored GPU acceleration. In this paper, we propose cuRPQ, a high-performance GPU-optimized framework for processing RPQs and CRPQs. cuRPQ addresses the key GPU challenges through a novel traversal algorithm, an efficient visited-set management scheme, and a concurrent exploration-materialization strategy. Extensive experiments show that cuRPQ outperforms state-of-the-art methods by orders of magnitude, without out-of-memory errors.

Problem

• Regular Path Queries
• Conjunctive Regular Path Queries
• GPU Acceleration
• Graph Analytics
• Path-Constrained Reachability

Innovation

• GPU acceleration
• regular path queries
• conjunctive regular path queries
• graph traversal
• visited-set management