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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 | Source: | 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 | Keywords: | Markov Chain Monte Carlo; high performance computing; affine invariant sampling | Document URI: | http://hdl.handle.net/1942/24917 | ISBN: | 9781538623268 | DOI: | 10.1109/CLUSTER.2017.68 | ISI #: | 000413691000053 | Rights: | (C) IEEE | Category: | C1 | Type: | Proceedings Paper | Validations: | ecoom 2018 |
Appears in Collections: | Research publications |
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