MapReduce框架中的排序、搜索和模拟
Sorting, Searching, and Simulation in the MapReduce Framework
摘要：
在本文中，我们从算法的角度研究MapReduce框架，并通过设计和分析基本排序，搜索和模拟问题的高效MapReduce算法来证明我们的方法的有用性。本研究的目的是最终将MapReduce框架置于与著名的PRAM和BSP并行模型相同的理论基础上，这将有利于MapReduce算法的理论和实践。我们描述了高效的MapReduce算法排序，多搜索，并在BSP和CRCW PRAM模型中指定的并行算法的模拟。我们还提供了一些应用这些结果的问题并行计算几何的MapReduce框架，从而导致有效的MapReduce算法排序，2-和3-维凸包，和固定维线性规划。对于映射器和归约器的内存/消息I/O大小为$M=Θ(N^ε)$的情况，对于一个小的常量$ε>0$，我们为这些应用程序设计的所有MapReduce算法都以常量轮数运行。
英文摘要：
In this paper, we study the MapReduce framework from an algorithmic standpoint and demonstrate the usefulness of our approach by designing and analyzing efficient MapReduce algorithms for fundamental sorting, searching, and simulation problems. This study is motivated by a goal of ultimately putting the MapReduce framework on an equal theoretical footing with the well-known PRAM and BSP parallel models, which would benefit both the theory and practice of MapReduce algorithms. We describe efficient MapReduce algorithms for sorting, multi-searching, and simulations of parallel algorithms specified in the BSP and CRCW PRAM models. We also provide some applications of these results to problems in parallel computational geometry for the MapReduce framework, which result in efficient MapReduce algorithms for sorting, 2- and 3-dimensional convex hulls, and fixed-dimensional linear programming. For the case when mappers and reducers have a memory/message-I/O size of $M=Θ(N^ε)$, for a small constant $ε>0$, all of our MapReduce algorithms for these applications run in a constant number of rounds.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
