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Closed Cube Computation on Multi-core CPUs

机译:多核CPU上的封闭立方体计算

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Closed cube computation is a popular method to solve the huge output problem of data cubing and has attracted great interests among researchers. A lot of efficient algorithms such as QC-DFS, C-Cubing and CC_ALG have been proposed. However, due to significant changes in computer hardware architecture in recent years, these algorithms need to be revisited to achieve good performance. In this paper, we present a parallel algorithm for closed cube computation on multi-core CPUs to fully utilize the processor resources. In our algorithm, a cost-based load balance strategy is designed to deal with data skews and partitions based on each individual dimension are simultaneously computed to generate closed cells. An extensive performance study is conducted and the results show that good speedups can be gained for the parallel algorithm.
机译:封闭的多维数据集计算是一种解决数据巨大的巨大输出问题的流行方法,并引起了研究人员的巨大兴趣。已经提出了许多有效的算法,例如QC-DFS,C-CUBING和CC_ALG。然而,由于近年来计算机硬件架构的显着变化,需要重新审视这些算法以实现良好的性能。在本文中,我们在多核CPU上呈现了一种用于闭合多维数据集计算来充分利用处理器资源的并行算法。在我们的算法中,设计了一种基于成本的负载平衡策略来处理基于每个单独的维度的数据偏差和分区,同时计算以生成闭合单元。进行了广泛的性能研究,结果表明并行算法可以获得良好的加速。

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