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arXiv · 1209.1076

Communication/Computation Tradeoffs in Consensus-Based Distributed Optimization

Konstantinos I. Tsianos·Sean Lawlor·Michael G. Rabbat
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Abstract

We study the scalability of consensus-based distributed optimization algorithms by considering two questions: How many processors should we use for a given problem, and how often should they communicate when communication is not free? Central to our analysis is a problem-specific value $r$ which quantifies the communication/computation tradeoff. We show that organizing the communication among nodes as a $k$-regular expander graph (Reingold, Vadhan, and Wigderson, 2002) yields speedups, while when all pairs of nodes communicate (as in a complete graph), there is an optimal number of processors that depends on $r$. Surprisingly, a speedup can be obtained, in terms of the time to reach a fixed level of accuracy, by communicating less and less frequently as the computation progresses. Experiments on a real cluster solving metric learning and non-smooth convex minimization tasks demonstrate strong agreement between theory and practice.

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BibTeXRIS

Konstantinos I. Tsianos, Sean Lawlor, Michael G. Rabbat. 2012-09-05. Communication/Computation Tradeoffs in Consensus-Based Distributed Optimization. https://arxiv.org/abs/1209.1076

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