A Fundamental Tradeoff between Computation and Communication in Distributed Computing

TL;DR

This paper characterizes a fundamental inverse relationship between computation and communication in distributed computing, proposing a coding scheme that optimally balances the two and demonstrating practical speedups in a benchmark application.

Contribution

It introduces Coded Distributed Computing (CDC), an optimal coding scheme that leverages increased computation to significantly reduce communication load in distributed systems.

Findings

The CDC scheme achieves the theoretical lower bound on communication load.

Applying CDC to Hadoop TeraSort yields nearly 2 to 3.4 times speedup.

The paper precisely characterizes the computation-communication tradeoff in distributed computing.

Abstract

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 $1.97\times$ - $3.39\times$, for typical settings of interest.