Log Diameter Rounds MST Verification and Sensitivity in MPC

Sam Coy, Artur Czumaj, Gopinath Mishra, Anish Mukherjee
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Abstract

We consider two natural variants of the problem of minimum spanning tree (MST) of a graph in the parallel setting: MST verification (verifying if a given tree is an MST) and the sensitivity analysis of an MST (finding the lowest cost replacement edge for each edge of the MST). These two problems have been studied extensively for sequential algorithms and for parallel algorithms in the PRAM model of computation. In this paper, we extend the study to the standard model of Massive Parallel Computation (MPC). It is known that for graphs of diameter $D$, the connectivity problem can be solved in $O(\log D + \log\log n)$ rounds on an MPC with low local memory (each machine can store only $O(n^{\delta})$ words for an arbitrary constant $\delta > 0$) and with linear global memory, that is, with optimal utilization. However, for the related task of finding an MST, we need $\Omega(\log D_{\text{MST}})$ rounds, where $D_{\text{MST}}$ denotes the diameter of the minimum spanning tree. The state of the art upper bound for MST is $O(\log n)$ rounds; the result follows by simulating existing PRAM algorithms. While this bound may be optimal for general graphs, the benchmark of connectivity and lower bound for MST suggest the target bound of $O(\log D_{\text{MST}})$ rounds, or possibly $O(\log D_{\text{MST}} + \log\log n)$ rounds. As for now, we do not know if this bound is achievable for the MST problem on an MPC with low local memory and linear global memory. In this paper, we show that two natural variants of the MST problem: MST verification and sensitivity analysis of an MST, can be completed in $O(\log D_T)$ rounds on an MPC with low local memory and with linear global memory; here $D_T$ is the diameter of the input ``candidate MST'' $T$. The algorithms asymptotically match our lower bound, conditioned on the 1-vs-2-cycle conjecture.
对数直径轮 MST 验证和 MPC 中的灵敏度
我们考虑了并行环境下图的最小生成树(MST)问题的两个自然变体:MST 验证(验证给定的树是否是 MST)和 MST 的灵敏度分析(为 MST 的每条边找到成本最低的替换边)。在计算的 PRAM 模型中,这两个问题已经针对顺序算法和并行算法进行了广泛的研究。在本文中,我们将研究扩展到大规模并行计算 (MPC) 的标准模型。众所周知,对于直径为 $D$ 的图,连接性问题可以在具有低局部内存(对于任意常数 $\delta> 0$,每台机器只能存储 $O(n^{\delta})$ 个字)和线性全局内存(即最佳利用率)的 MPC 上以 $O(\log D +\log\log n)$ 轮解决。然而,对于查找 MST 的相关任务,我们需要 $\Omega(\logD_{text{MST}})$ 轮,其中 $D_{text{MST}}$ 表示最小生成树的直径。最先进的 MST 上限为 $O(\log n)$ 轮;这是通过模拟现有的 PRAM 算法得出的结果。虽然这个上限对于一般图来说可能是最优的,但是连通性基准和 MST 的下限表明目标上限是 $O(\log D_{text{MST}})$ rounds,或者可能是 $O(\log D_{text{MST}} + \log\log n)$ rounds。目前,我们还不知道在局部内存小、全局内存线性的 MPC 上,MST 问题是否可以实现这一约束。在本文中,我们证明了 MST 问题的两个自然变体:MST 验证和 MST 敏感性分析,可以在具有低局部内存和线性全局内存的 MPC 上以 $O(\log D_T)$ 轮完成;这里的 $D_T$ 是输入 "候选 MST" $T$ 的直径。这些算法渐近地符合我们的下限,条件是1-vs-2循环猜想。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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