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cuRPQ:一种高性能的基于GPU的处理规则路径查询和联合规则路径查询的框架

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

Korea Advanced Institute of Science and Technology(韩国科学技术院)

Sungwoo Park, Seohyeon Kim, Min-Soo Kim

AI总结:

cuRPQ是一种基于GPU的高性能框架,通过创新算法和管理策略高效处理规则路径查询和联合规则路径查询,显著提升性能并避免内存错误。

摘要:

规则路径查询(RPQ)是路径约束可达性分析的基本要素,更复杂的变种如联合规则路径查询(CRPQ)越来越多地用于图分析。评估这些查询计算成本很高,但到目前为止,没有先前的工作探索了GPU加速。在本文中,我们提出了cuRPQ,一种高性能的GPU优化框架,用于处理RPQ和CRPQ。cuRPQ通过一种新颖的遍历算法、高效的已访问集合管理方案和并发探索-材料化策略来解决关键的GPU挑战。广泛的实验表明,cuRPQ在性能上比最先进的方法高出多个数量级,且没有内存溢出错误。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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