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.

翻译：正则路径查询（RPQ）是路径约束可达性分析的基础，而更复杂的变体如合取正则路径查询（CRPQ）在图分析中的应用日益广泛。评估这些查询的计算开销巨大，但据我们所知，此前尚无工作探索GPU加速。本文提出cuRPQ，一种针对RPQ与CRPQ处理的高性能GPU优化框架。cuRPQ通过新颖的遍历算法、高效的已访问集管理方案以及并发探索-物化策略，解决了GPU实现中的关键挑战。大量实验表明，cuRPQ在避免内存溢出错误的同时，其性能超越现有最优方法数个数量级。