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When distributed computation is communication expensive

When distributed computation is communication expensive We consider a number of fundamental statistical and graph problems in the message-passing model, where we have $$k$$ k machines (sites), each holding a piece of data, and the machines want to jointly solve a problem defined on the union of the $$k$$ k data sets. The communication is point-to-point, and the goal is to minimize the total communication among the $$k$$ k machines. This model captures all point-to-point distributed computational models with respect to minimizing communication costs. Our analysis shows that exact computation of many statistical and graph problems in this distributed setting requires a prohibitively large amount of communication, and often one cannot improve upon the communication of the simple protocol in which all machines send their data to a centralized server. Thus, in order to obtain protocols that are communication-efficient, one has to allow approximation, or investigate the distribution or layout of the data sets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Distributed Computing Springer Journals

When distributed computation is communication expensive

Distributed Computing , Volume 30 (5) – May 5, 2014

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References (7)

Publisher
Springer Journals
Copyright
Copyright © 2014 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Computer Communication Networks; Computer Hardware; Computer Systems Organization and Communication Networks; Software Engineering/Programming and Operating Systems; Theory of Computation
ISSN
0178-2770
eISSN
1432-0452
DOI
10.1007/s00446-014-0218-3
Publisher site
See Article on Publisher Site

Abstract

We consider a number of fundamental statistical and graph problems in the message-passing model, where we have $$k$$ k machines (sites), each holding a piece of data, and the machines want to jointly solve a problem defined on the union of the $$k$$ k data sets. The communication is point-to-point, and the goal is to minimize the total communication among the $$k$$ k machines. This model captures all point-to-point distributed computational models with respect to minimizing communication costs. Our analysis shows that exact computation of many statistical and graph problems in this distributed setting requires a prohibitively large amount of communication, and often one cannot improve upon the communication of the simple protocol in which all machines send their data to a centralized server. Thus, in order to obtain protocols that are communication-efficient, one has to allow approximation, or investigate the distribution or layout of the data sets.

Journal

Distributed ComputingSpringer Journals

Published: May 5, 2014

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