Fast Parallel Computation of Longest Common Prefixes

Julian Shun
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引用次数: 21

Abstract

Suffix arrays and the corresponding longest common prefix (LCP) array have wide applications in bioinformatics, information retrieval and data compression. In this work, we propose and theoretically analyze new parallel algorithms for computing the LCP array given the suffix array as input. Most of our algorithms have a work and depth (parallel time) complexity related to the LCP values of the input. We also present a slight variation of Kärkkäinen and Sanders' skew algorithm that requires linear work and poly-logarithmic depth in the worst case. We present a comprehensive experimental study of our parallel algorithms along with existing parallel and sequential LCP algorithms. On a variety of real-world and artificial strings, we show that on a 40-core shared-memory machine our fastest algorithm is up to 2.3 times faster than the fastest existing parallel algorithm, and up to 21.8 times faster than the fastest sequential LCP algorithm.
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最长公共前缀的快速并行计算
后缀阵列及其对应的最长公共前缀阵列在生物信息学、信息检索和数据压缩等领域有着广泛的应用。在这项工作中,我们提出并从理论上分析了以后缀数组为输入的LCP数组计算的新并行算法。我们的大多数算法都具有与输入LCP值相关的工作和深度(并行时间)复杂性。我们还提出了Kärkkäinen和Sanders的倾斜算法的轻微变化,在最坏的情况下需要线性工作和多对数深度。我们对我们的并行算法以及现有的并行和顺序LCP算法进行了全面的实验研究。在各种现实世界和人工字符串上,我们展示了在40核共享内存机器上,我们最快的算法比现有最快的并行算法快2.3倍,比最快的顺序LCP算法快21.8倍。
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