Brief Announcement: Semi-MapReduce Meets Congested Clique
Brief Announcement: Semi-MapReduce Meets Congested Clique
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简短公告：Semi-MapReduce 遇到拥堵派系
DOI：
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
M. Hajiaghayi
中科院分区：
文献类型：
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作者：
Soheil Behnezhad;Mahsa Derakhshan;M. Hajiaghayi
当涉及到MapReduce时，图形问题是很麻烦的。通常，为了能够设计利用MapReduce优势的算法，除了模型强加的假设之外，还需要假设，例如输入图的{\em密度}。
在最近的一次转变中，一个简单而健壮的MapReduce图问题模型引起了相当大的关注，其中每台机器的空间被设置为$O(|V|)$。我们称这个模型为{em Semi-MapReduce}，或简称为Semi-MPC，重点关注它的计算能力。
在这篇短文中，我们通过一组模拟方法表明，也许令人惊讶的是，Semi-MPC几乎等同于分布式计算的拥塞集团模型。然而，半预测预测除了圆形复杂性外，还包含了另一个需要优化的实际重要维度：机器数量。此外，我们还证明了其他分布式计算模型中的算法，例如拥塞，可以被模拟为运行在相同轮次的半预测预测中，同时也使用了最优机器数量。我们稍后通过使用最近开发的算法获得这些模型的改进算法来展示这些模拟方法的含义。
Graph problems are troublesome when it comes to MapReduce. Typically, to be able to design algorithms that make use of the advantages of MapReduce, assumptions beyond what the model imposes, such as the {\em density} of the input graph, are required.
In a recent shift, a simple and robust model of MapReduce for graph problems, where the space per machine is set to be $O(|V|)$ has attracted considerable attention. We term this model {\em semi-MapReduce}, or in short, semi-MPC, and focus on its computational power.
In this short note, we show through a set of simulation methods that semi-MPC is, perhaps surprisingly, almost equivalent to the congested clique model of distributed computing. However, semi-MPC, in addition to round complexity, incorporates another practically important dimension to optimize: the number of machines. Furthermore, we show that algorithms in other distributed computing models, such as CONGEST, can be simulated to run in the same number of rounds of semiMPC while also using an optimal number of machines. We later show the implications of these simulation methods by obtaining improved algorithms for these models using the recent algorithms that have been developed.
核心集满足 EDCS：海量图上的匹配和顶点覆盖算法
DOI：
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发表时间：
2019
期刊：
SODA 2019
影响因子：
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作者：
Assadi, S.
Batenai
通讯作者：
Assadi, S.
Batenai