MapReduce/Bigtable for Distributed Optimization

author: Slav Petrov, Research at Google, Google, Inc.
published: Jan. 13, 2011,   recorded: December 2010,   views: 6648


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

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Reviews and comments:

Comment1 basanti kumari, August 10, 2013 at 7:42 p.m.:

good job please speak in hindi i can't understand.

Comment2 basanti kumari, August 10, 2013 at 7:45 p.m.:

plz attach me a video of google big table on this id.
thanks for ur workshop.

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