TeGraph+:实现灵活边缘修改的可扩展时态图处理技术

IF 5.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Chengying Huan;Yongchao Liu;Heng Zhang;Hang Liu;Shiyang Chen;Shuaiwen Leon Song;Yanjun Wu
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引用次数: 0

摘要

时态图被广泛应用于时间关键型应用中,这些应用可以提取具有时间特征的图结构信息,但静态图计算系统无法有效支持这些信息。然而,目前针对时态图问题的最先进解决方案不仅是临时和次优的,而且还表现出很差的可扩展性,特别是无法扩展到具有灵活边修改(包括插入和删除)和不同执行环境的不断演化的图。在本文中,我们将提出两个重要观点。首先,时空路径问题可被描述为拓扑最优问题,可使用通用的单扫描执行模型高效解决。其次,可以通过合并多余的顶点来减少转换时序图中的数据冗余。在这些基本见解的基础上,我们提出了 TeGraph+,这是一个多功能时态图计算引擎,具有以下贡献:(1) 时序图问题的统一优化策略和执行模型;(2) 带有图冗余减少策略的新型图转换模型;(3) 基于生成树分解(STD)的分布式执行模型,该模型使用高效的转换图分解策略将转换图划分为不同的生成树以进行分布式执行;(4) 高效的混合命令式和懒惰式图更新策略,支持灵活修改边的演化图;(5) 具有用户友好 API 的通用系统框架,支持各种执行环境,包括内存、外核和分布式执行环境。我们的广泛评估显示,TeGraph+ 与最先进的同行相比,可实现高达 241 美元/次的提速。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TeGraph+: Scalable Temporal Graph Processing Enabling Flexible Edge Modifications
Temporal graphs are widely used for time-critical applications, which enable the extraction of graph structural information with temporal features but cannot be efficiently supported by static graph computing systems. However, the current state-of-the-art solutions for temporal graph problems are not only ad-hoc and suboptimal, but they also exhibit poor scalability, particularly in terms of their inability to scale to evolving graphs with flexible edge modifications (including insertions and deletions) and diverse execution environments. In this article, we present two key observations. First, temporal path problems can be characterized as topological-optimum problems, which can be efficiently resolved using a universal single-scan execution model. Second, data redundancy in transformed temporal graphs can be mitigated by merging superfluous vertices. Building upon these fundamental insights, we propose TeGraph+, a versatile temporal graph computing engine that makes the following contributions: (1) a unified optimization strategy and execution model for temporal graph problems; (2) a novel graph transformation model with graph redundancy reduction strategy; (3) a spanning tree decomposition (STD) based distributed execution model which uses an efficient transformed graph decomposition strategy to partition the transformed graph into different spanning trees for distributed execution; (4) an efficient mixed imperative and lazy graph update strategy that offers support for evolving graphs with flexible edge modifications; (5) a general system framework with user-friendly APIs and the support of various execution environments, including in-memory, out-of-core, and distributed execution environments. Our extensive evaluation reveals that TeGraph+ can achieve up to $241\times$ speedups over the state-of-the-art counterparts.
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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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