Pitfalls in the use of Parallel Inference for the Dirichlet Process
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Proceedings of the 31st International Conference on Machine Learning, PMLR 32(2):208216, 2014.
Abstract
Recent work done by Lovell, Adams, and Mansingka (2012) and Williamson, Dubey, and Xing (2013) has suggested an alternative parametrisation for the Dirichlet process in order to derive nonapproximate parallel MCMC inference for it  work which has been pickedup and implemented in several different fields. In this paper we show that the approach suggested is impractical due to an extremely unbalanced distribution of the data. We characterise the requirements of efficient parallel inference for the Dirichlet process and show that the proposed inference fails most of these requirements (while approximate approaches often satisfy most of them). We present both theoretical and experimental evidence, analysing the load balance for the inference and showing that it is independent of the size of the dataset and the number of nodes available in the parallel implementation. We end with suggestions of alternative paths of research for efficient nonapproximate parallel inference for the Dirichlet process.
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