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The Communication Analysis of Implementation in Breadth First Search Algorithm

机译:广度优先搜索算法实现的通信分析

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Breadth first search (BFS), specially deals with large graph involving millions of vertices and edges, is a key component in processing bio-informatics, social networks etc. Since the scale of data stored and queried increasing, the performance of BFS algorithm is not satisfied in large amounts of data processing. For efficiently utilizing the processing power of modern processors, researchers optimized the algorithm and proposed four major types of implementation: 1D top-down BFS, 1D bottom-up BFS, 2D top-down BFS, 2D bottom-up BFS. These four types of implementation apply in different parallel computing system according specific conditions. In this paper we construct a MPI communication delay model to analysis these four types of BFS algorithm implementation. Our MPI communication model is based on MPI primitives. We break the four types of BFS algorithm implementation into MPI group communications and non-blocking communications. We extrapolate the latency of non-blocking communication by function of discrete data fitting and applying with network calculus. We take the MPI non-blocking communication as a basic unit to derive MPI group communication latency by analysing MPI primitives. We adopt logic communication and physical communication to discuss the MPI primitives' latency. We seek to obtain the optimal communication buffer in these four types of BFS algorithm implementation. And we try to provide a quantitative solution to choose a suitable topology in communication. Finally we use our experimental platform (a cluster computing system) to validate the proposed model.algorithm implementation. And we try to provide a quantitative solution to choose a suitable topology in communication. Finally we use our experimental platform (a cluster computing system) to validate the proposed model.
机译:广度优先搜索(BFS)是专门处理涉及数百万个顶点和边缘的大图的重要部分,是处理生物信息学,社交网络等的关键组成部分。由于存储和查询的数据规模不断增加,因此BFS算法的性能并不高满足大量数据处理。为了有效利用现代处理器的处理能力,研究人员优化了算法,并提出了四种主要实现方式:一维自上而下的BFS,一维自下而上的BFS,二维自上而下的BFS,二维自下而上的BFS。这四种类型的实现可根据特定条件应用于不同的并行计算系统。在本文中,我们构建了一个MPI通信延迟模型来分析这四种类型的BFS算法实现。我们的MPI通信模型基于MPI原语。我们将BFS算法实现的四种类型分为MPI组通信和非阻塞通信。我们通过离散数据拟合的功能并与网络演算一起推断无阻塞通信的延迟。我们以MPI无阻塞通信为基本单位,通过分析MPI基元来得出MPI组通信延迟。我们采用逻辑通信和物理通信来讨论MPI原语的延迟。我们试图在这四种类型的BFS算法实现中获得最佳的通信缓冲区。并且我们尝试提供一种定量解决方案,以选择合适的通信拓扑。最后,我们使用实验平台(集群计算系统)来验证所提出的model.algorithm实现。并且我们尝试提供一种定量解决方案,以选择合适的通信拓扑。最后,我们使用实验平台(集群计算系统)来验证所提出的模型。

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