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arXiv · 1710.09001

A Sequential Approximation Framework for Coded Distributed Optimization

Jingge Zhu·Ye Pu·Vipul Gupta·Claire Tomlin·Kannan Ramchandran
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Abstract

Building on the previous work of Lee et al. and Ferdinand et al. on coded computation, we propose a sequential approximation framework for solving optimization problems in a distributed manner. In a distributed computation system, latency caused by individual processors ("stragglers") usually causes a significant delay in the overall process. The proposed method is powered by a sequential computation scheme, which is designed specifically for systems with stragglers. This scheme has the desirable property that the user is guaranteed to receive useful (approximate) computation results whenever a processor finishes its subtask, even in the presence of uncertain latency. In this paper, we give a coding theorem for sequentially computing matrix-vector multiplications, and the optimality of this coding scheme is also established. As an application of the results, we demonstrate solving optimization problems using a sequential approximation approach, which accelerates the algorithm in a distributed system with stragglers.

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Jingge Zhu, Ye Pu, Vipul Gupta, Claire Tomlin, Kannan Ramchandran. 2017-10-24. A Sequential Approximation Framework for Coded Distributed Optimization. https://arxiv.org/abs/1710.09001

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