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An Investigation into Concurrent Expectation Propagation

David Hall, Alex Kantchelian
EECS CS Division, University of California, Berkeley
University of California, CS252: Spring 2012 Final Projects, 2012

@article{hall2012investigation,

   title={An Investigation into Concurrent Expectation Propagation},

   author={Hall, D. and Kantchelian, A.},

   year={2012}

}

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As statistical machine learning becomes more and more prevalent and models become more complicated and fit to larger amounts of data, approximate inference mechanisms become more and more crucial to their success. Expectation propagation (EP) is one such algorithm for inference in probabilistic graphical models. In this work, we introduce a robustified version of EP which helps ensure convergence under a relaxed memory consistency model. The resulting algorithm can be efficiently implemented on a GPU in a straightforward way. Using a 2D Ising spin glass model, we evaluate both the original EP algorithm and our robustified version in terms of convergence, if any, and precision on a classic single core processor. We also compare the naive parallelized version of the original EP algorithm against the parallelized robustified EP on both a multicore CPU and a GPU.
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