使用内存处理的图形数据库的高效规则路径查询

IF 6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Weihan Kong;Shengan Zheng;Yifan Hua;Ruoyan Ma;Yuheng Wen;Guifeng Wang;Cong Zhou;Linpeng Huang
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引用次数: 0

摘要

图数据库中的常规路径查询(rpq)受到内存墙的限制。新兴的内存处理(PIM)技术提供了一种很有前途的解决方案,可以在PIM模块中并行地调度和执行路径匹配任务。我们提出了一个高效的基于pim的数据管理系统,为rpq和图表更新量身定制。我们的解决方案称为PimBeam,通过实现pim友好的动态图分区算法,促进了高效的批处理rpq和图更新。该算法有效地解决了图偏度问题,同时保持了图的局部性,并且处理rpq的开销很小。PimBeam通过在PIM端添加过滤模块并利用PIM的并行性来简化标签过滤查询。对于图更新,PimBeam通过将主机CPU的更新开销分摊给PIM模块来提高处理效率。PimBeam的评估结果表明,与最先进的传统图形数据库相比,rpq的平均加速速度为3.59倍,图更新的平均加速速度为29.33倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PimBeam: Efficient Regular Path Queries Over Graph Database Using Processing-in-Memory
Regular path queries (RPQs) in graph databases are bottlenecked by the memory wall. Emerging processing-in-memory (PIM) technologies offer a promising solution to dispatch and execute path matching tasks in parallel within PIM modules. We present an efficient PIM-based data management system tailored for RPQs and graph updates. Our solution, called PimBeam, facilitates efficient batch RPQs and graph updates by implementing a PIM-friendly dynamic graph partitioning algorithm. This algorithm effectively addresses graph skewness issues while maintaining graph locality with low overhead for handling RPQs. PimBeam streamlines label filtering queries by adding a filtering module on the PIM side and leveraging the parallelism of PIM. For the graph updates, PimBeam enhances processing efficiency by amortizing the host CPU's update overhead to PIM modules. Evaluation results of PimBeam indicate 3.59x speedup for RPQs and 29.33x speedup for graph update on average over the state-of-the-art traditional graph database.
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来源期刊
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems 工程技术-工程:电子与电气
CiteScore
11.00
自引率
9.40%
发文量
281
审稿时长
5.6 months
期刊介绍: IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.
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