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A fundamental tradeoff between computation and communication in distributed computing

Li, S ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/TIT.2017.2756959
  3. Abstract:
  4. How can we optimally trade extra computing power to reduce the communication load in distributed computing? We answer this question by characterizing a fundamental tradeoff between computation and communication in distributed computing, i.e., the two are inversely proportional to each other. More specifically, a general distributed computing framework, motivated by commonly used structures like MapReduce, is considered, where the overall computation is decomposed into computing a set of “Map” and “Reduce” functions distributedly across multiple computing nodes. A coded scheme, named “Coded Distributed Computing” (CDC), is proposed to demonstrate that increasing the computation load of the Map functions by a factor of r (i.e., evaluating each function at r carefully chosen nodes) can create novel coding opportunities that reduce the communication load by the same factor. An information-theoretic lower bound on the communication load is also provided, which matches the communication load achieved by the CDC scheme. As a result, the optimal computation-communication tradeoff in distributed computing is exactly characterized. Finally, the coding techniques of CDC is applied to the Hadoop TeraSort benchmark to develop a novel CodedTeraSort algorithm, which is empirically demonstrated to speed up the overall job execution by 1.97× − 3.39×, for typical settings of interest. IEEE
  5. Keywords:
  6. Coded multicasting ; Computation-communication tradeoff ; Distributed computing ; Distributed databases ; Electrical engineering ; Encoding ; Electrical engineering ; Electronic mail ; Encoding (symbols) ; Information theory ; Multicasting ; Multiprocessing systems ; Benchmark testing ; Coded terasort ; Communication load ; Distributed computing frameworks ; Distributed database ; Information-theoretic lower bounds ; Map-reduce ; Optimal computation ; Distributed computer systems
  7. Source: IEEE Transactions on Information Theory ; 2017 ; 00189448 (ISSN)
  8. URL: https://ieeexplore.ieee.org/document/8051074