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MapReduce/Bigtable for Distributed Optimization

Keith B. Hall
Scott Gilpin
Gideon Mann
Neural Information Processing Systems Workshop on Leaning on Cores, Clusters, and Clouds (2010)
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Abstract

For large data it can be very time consuming to run gradient based optimizat ion,for example to minimize the log-likelihood for maximum entropy models.Distributed methods are therefore appealing and a number of distributed gradientoptimization strategies have been proposed including: distributed gradient, asynchronousupdates, and iterative parameter mixtures. In this paper, we evaluatethese various strategies with regards to their accuracy and speed over MapReduce/Bigtable and discuss the techniques needed for high performance.

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