The Parameter Server

author: Alexander J. Smola, Machine Learning Department, Carnegie Mellon University
published: Jan. 16, 2013,   recorded: December 2012,   views: 1136
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Description

In this talk I will discuss a number of vignettes on scaling optimization and inference. Despite arising from very different contexts (graphical models inference, convex optimization, neural networks), they all share a common design pattern - a synchronization mechanism in the form of a parameter server. It formalizes the notion of decomposing optimization problems into subsets and reconciling partial solutions. I will discuss some of the systems and distribution issues involved in building such a system.

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