This paper describes the design of three parallel algorithms for merging the topic hierarchies generated by a probabilistic topic model. These algorithms have been implemented on a shared memory multi-processor workstation and are primarily suitable to parallelize a sequential topic-modeling algorithm, to be run on top of a multi-core machine. We perform empirical experiments using a set of documents crawled from Wikipedia. The outcome of these experiments asserts that the parallel algorithms yield a model with a good likelihood to the underlying document set being modeled and realize a sub-linear speed-up over the sequential topic-modeling variant.
['Karthick Seshadri', 'S. Mercy Shalinie']
Parallel algorithms for merging topic trees and their application in meta search engines