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

TL;DR

cuRPQ is a GPU-accelerated framework that significantly speeds up the processing of regular and conjunctive regular path queries in graph analytics, overcoming computational challenges.

Contribution

It introduces a novel GPU traversal algorithm and strategies for efficient visited-set management and concurrent exploration, enabling high-performance query processing.

Findings

cuRPQ outperforms existing methods by orders of magnitude.

It handles large graphs without out-of-memory errors.

The framework is effective for complex CRPQ evaluations.

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.