通过并行I/O实现多维数据集的高效检索

Sunil Prabhakar, K. Abdel-Ghaffar, D. Agrawal, A. E. Abbadi
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引用次数: 27

摘要

许多科学和工程应用处理大型多维数据集。这些应用程序的一个重要访问模式是检索与多维值范围相对应的数据。由于磁盘的高延迟,性能受到磁盘的限制。在多个磁盘上平铺和分布数据是一种通过并行I/O提高性能的有效技术。磁片在磁盘上的分布是实现增益的一个重要因素。为了提高范围查询的性能,文献中已经提出了几种多维数据的聚类方案。我们将先前为二维数据开发的循环格式扩展到多维数据。建立了循环格式的重要性质,在此基础上减少了在循环格式类中确定好的聚类格式的搜索空间。通过实验评估,我们建立了循环方案优于其他退聚方案,包括最先进的,无论是在并行度和鲁棒性方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient retrieval of multidimensional datasets through parallel I/O
Many scientific and engineering applications process large multidimensional datasets. An important access pattern for these applications is the retrieval of data corresponding to ranges of values in multiple dimensions. Performance is limited by disk largely due to high disk latencies. Tiling and distributing the data across multiple disks is an effective technique for improving performance through parallel I/O. The distribution of tiles across the disks is an important factor in achieving gains. Several schemes for declustering multidimensional data to improve the performance of range queries have been proposed in the literature. We extend the class of cyclic schemes which have been developed earlier for two-dimensional data to multiple dimensions. We establish important properties of cyclic schemes, based upon which we reduce the search space for determining good declustering schemes within the class of cyclic schemes. Through experimental evaluation, we establish that the cyclic schemes are superior to other declustering schemes, including the state-of-the-art, both in terms of the degree of parallelism and robustness.
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