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Conference object . 2023
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https://dx.doi.org/10.48550/ar...
Article . 2022
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Computing Power of Hybrid Models in Synchronous Networks

Authors: Fraigniaud, Pierre; Montealegre, Pedro; Paredes, Pablo; Rapaport, Ivan; Ríos-Wilson, Martín; Todinca, Ioan;

Computing Power of Hybrid Models in Synchronous Networks

Abstract

During the last two decades, a small set of distributed computing models for networks have emerged, among which LOCAL, CONGEST, and Broadcast Congested Clique (BCC) play a prominent role. We consider hybrid models resulting from combining these three models. That is, we analyze the computing power of models allowing to, say, perform a constant number of rounds of CONGEST, then a constant number of rounds of LOCAL, then a constant number of rounds of BCC, possibly repeating this figure a constant number of times. We specifically focus on 2-round models, and we establish the complete picture of the relative powers of these models. That is, for every pair of such models, we determine whether one is (strictly) stronger than the other, or whether the two models are incomparable. The separation results are obtained by approaching communication complexity through an original angle, which may be of independent interest. The two players are not bounded to compute the value of a binary function, but the combined outputs of the two players are constrained by this value. In particular, we introduce the XOR-Index problem, in which Alice is given a binary vector $x\in\{0,1\}^n$ together with an index $i\in[n]$, Bob is given a binary vector $y\in\{0,1\}^n$ together with an index $j\in[n]$, and, after a single round of 2-way communication, Alice must output a boolean $\textrm{out}_A$, and Bob must output a boolean $\textrm{out}_B$, such that $\mbox{out}_A\land\mbox{out}_B = x_j\oplus y_i$. We show that the communication complexity of XOR-Index is $Ω(n)$ bits.

Keywords

LOCAL, FOS: Computer and information sciences, hybrid model, [INFO] Computer Science [cs], synchronous networks, 004, Computer Science - Distributed, Parallel, and Cluster Computing, CONGEST, Broadcast Congested Clique, Distributed, Parallel, and Cluster Computing (cs.DC), Theory of computation → Distributed algorithms, ddc: ddc:004

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
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