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http://hdl.handle.net/1942/24917
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DC Field | Value | Language |
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dc.contributor.author | NEMETH, Balazs | - |
dc.contributor.author | HABER, Tom | - |
dc.contributor.author | LIESENBORGS, Jori | - |
dc.contributor.author | LAMOTTE, Wim | - |
dc.date.accessioned | 2017-10-04T09:05:53Z | - |
dc.date.available | 2017-10-04T09:05:53Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | 2017 IEEE International Conference on Cluster Computing (CLUSTER), IEEE, p. 520-524 | - |
dc.identifier.isbn | 9781538623268 | - |
dc.identifier.issn | 1552-5244 | - |
dc.identifier.uri | http://hdl.handle.net/1942/24917 | - |
dc.description.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. | - |
dc.description.sponsorship | Part of the work presented in this paper was funded by Johnson & Johnson. | - |
dc.language.iso | en | - |
dc.publisher | IEEE | - |
dc.relation.ispartofseries | IEEE International Conference on Cluster Computing | - |
dc.rights | (C) IEEE | - |
dc.subject.other | Markov Chain Monte Carlo; high performance computing; affine invariant sampling | - |
dc.title | Distributed Affine-Invariant MCMC Sampler | - |
dc.type | Proceedings Paper | - |
local.bibliographicCitation.conferencedate | 05-08/09/2017 | - |
local.bibliographicCitation.conferencename | 2017 IEEE: International Conference on Cluster Computing (Cluster 2017) | - |
local.bibliographicCitation.conferenceplace | Honolulu (Hawaii), USA | - |
dc.identifier.epage | 524 | - |
dc.identifier.spage | 520 | - |
local.bibliographicCitation.jcat | C1 | - |
dc.description.notes | Nemeth, B (reprint author), Hasselt Univ tUL Imec, Expertise Ctr Digital Media, Martelarenlaan 42, B-3500 Hasselt, Belgium | - |
local.publisher.place | New York (NY), USA | - |
local.type.refereed | Refereed | - |
local.type.specified | Proceedings Paper | - |
dc.identifier.doi | 10.1109/CLUSTER.2017.68 | - |
dc.identifier.isi | 000413691000053 | - |
local.bibliographicCitation.btitle | 2017 IEEE International Conference on Cluster Computing (CLUSTER) | - |
item.validation | ecoom 2018 | - |
item.contributor | NEMETH, Balazs | - |
item.contributor | HABER, Tom | - |
item.contributor | LIESENBORGS, Jori | - |
item.contributor | LAMOTTE, Wim | - |
item.accessRights | Restricted Access | - |
item.fullcitation | NEMETH, Balazs; HABER, Tom; LIESENBORGS, Jori & LAMOTTE, Wim (2017) Distributed Affine-Invariant MCMC Sampler. In: 2017 IEEE International Conference on Cluster Computing (CLUSTER), IEEE, p. 520-524. | - |
item.fulltext | With Fulltext | - |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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2326a520.pdf Restricted Access | Published version | 162.57 kB | Adobe PDF | View/Open Request a copy |
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