MapReduce/Bigtable for Distributed Optimization
Venue
Neural Information Processing Systems Workshop on Leaning on Cores, Clusters, and Clouds (2010)
Publication Year
2010
Authors
Keith B. Hall, Scott Gilpin, Gideon Mann
BibTeX
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.
