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Parallel GPU-based Plane-Sweep Algorithm for Construction of iCPI-Trees

机译:基于并行GPU的平面扫描算法构造iCPI树

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This article tackles the problem of efficient construction of iCPI trees, frequently used in co-location pattern discovery in spatial databases. It discusses the methods for parallelization of iCPI-tree construction and plane-sweep algorithms used in state-of-the-art algorithms for co-location pattern mining. The main contribution of this paper is threefold: (1) a general algorithm for parallel iCPI-tree construction is presented, (2) two variants of parallel plane-sweep algorithm (which can be used in conjunction with the aforementioned iCPI-tree construction algorithm) are introduced and (3) all three algorithms are implemented on CUDA GPU platform and their performance is tested against an efficient multithreaded parallel implementation of iCPI-tree construction on CPU. Experiments prove that our solutions allow for large speedups over CPU version of the algorithm. This paper is an extension of the conference paper (Andrzejewski & Boinski, 2014).
机译:本文解决了有效构建iCPI树的问题,该树经常在空间数据库中的共置模式发现中使用。它讨论了用于i-tree结构和平面扫描算法的并行化方法,该算法用于同位模式挖掘的最新算法中。本文的主要贡献在于三方面:(1)提出了一种用于并行iCPI-tree构建的通用算法,(2)并行平面扫描算法的两种变体(可与上述iCPI-tree构建算法结合使用) )进行了介绍)(3)所有这三种算法均在CUDA GPU平台上实现,并且针对iCPI树在CPU上的高效多线程并行实现对它们的性能进行了测试。实验证明,我们的解决方案可以比CPU版本的算法大幅度提高速度。本文是会议论文的扩展(Andrzejewski&Boinski,2014)。

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