Breaking the linear-memory barrier in MPC: Fast MIS on trees with strongly sublinear memory

Sebastian Brandt, Manuela Fischer, Jara Uitto

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Recently, studying fundamental graph problems in the Massively Parallel Computation (MPC) framework, inspired by the MapReduce paradigm, has gained a lot of attention. An assumption common to a vast majority of approaches is to allow Ω˜(n) memory per machine, where n is the number of nodes in the graph and Ω˜ hides polylogarithmic factors. However, as pointed out by Karloff et al. [SODA'10] and Czumaj et al. [STOC'18], it might be unrealistic for a single machine to have linear or only slightly sublinear memory. In this paper, we thus study a more practical variant of the MPC model which only requires substantially sublinear or even subpolynomial memory per machine. In contrast to the linear-memory MPC model and also to streaming algorithms, in this low-memory MPC setting, a single machine will only see a small number of nodes in the graph. We introduce a new and strikingly simple technique to cope with this imposed locality. In particular, we show that the Maximal Independent Set (MIS) problem can be solved efficiently, that is, in O(log 3⁡log⁡n) rounds, when the input graph is a tree. This constitutes an almost exponential speed-up over the low-memory MPC algorithm in O˜(log⁡n) rounds in a concurrent work by Ghaffari and Uitto [SODA'19] and substantially reduces the local memory from Ω˜(n) required by the recent O(log⁡log⁡n)-round MIS algorithm of Ghaffari et al. [PODC'18] to n ε for any ε>0, without incurring a significant loss in the round complexity. Moreover, it demonstrates how to make use of the all-to-all communication in the MPC model to almost exponentially improve on the corresponding bound in the LOCAL and PRAM models by Lenzen and Wattenhofer [PODC'11].

Original languageEnglish
Pages (from-to)22-34
Number of pages13
JournalTheoretical Computer Science
Volume849
DOIs
Publication statusPublished - 6 Jan 2021
MoE publication typeA1 Journal article-refereed

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