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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/24917

Title: Distributed Affine-Invariant MCMC Sampler
Authors: Nemeth, Balazs
Haber, Tom
Liesenborgs, Jori
Lamotte, Wim
Issue Date: 2017
Publisher: IEEE
Citation: 2017 IEEE International Conference on Cluster Computing (CLUSTER), IEEE, p. 520-524
Series/Report: IEEE International Conference on Cluster Computing
Abstract: Markov Chain Monte Carlo methods provide a tool for tackling high dimensional problems. With many-core systems readily available today, it is no surprise that leveraging parallelism in these samplers has been a subject of recent research. The focus has been on solutions for shared-memory architectures, however these perform poorly in a distributedmemory environment. This paper introduces a fully decentralized version of an affine invariant sampler. By observing that a pseudorandom number generator makes stochastic algorithms deterministic, communication is both minimized and hidden by computation. Two cases at opposite ends of the communication to-computation ratio spectrum are used during evaluation against the currently available master-slave solution, where a more than tenfold reduction in execution time is measured.
Notes: Nemeth, B (reprint author), Hasselt Univ tUL Imec, Expertise Ctr Digital Media, Martelarenlaan 42, B-3500 Hasselt, Belgium
URI: http://hdl.handle.net/1942/24917
DOI: 10.1109/CLUSTER.2017.68
ISI #: 000413691000053
ISBN: 9781538623268
ISSN: 1552-5244
Category: C1
Type: Proceedings Paper
Appears in Collections: Research publications

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