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dc.contributor.author Suchard, MA
dc.contributor.author Wang, Q
dc.contributor.author Chan, C
dc.contributor.author Frelinger, J
dc.contributor.author Cron, A
dc.contributor.author West, M
dc.coverage.spatial United States
dc.date.accessioned 2011-06-21T17:30:32Z
dc.date.issued 2010-06-01
dc.identifier http://www.ncbi.nlm.nih.gov/pubmed/20877443
dc.identifier.citation J Comput Graph Stat, 2010, 19 (2), pp. 419 - 438
dc.identifier.issn 1061-8600
dc.identifier.uri http://hdl.handle.net/10161/4404
dc.description.abstract This article describes advances in statistical computation for large-scale data analysis in structured Bayesian mixture models via graphics processing unit (GPU) programming. The developments are partly motivated by computational challenges arising in fitting models of increasing heterogeneity to increasingly large datasets. An example context concerns common biological studies using high-throughput technologies generating many, very large datasets and requiring increasingly high-dimensional mixture models with large numbers of mixture components. We outline important strategies and processes for GPU computation in Bayesian simulation and optimization approaches, give examples of the benefits of GPU implementations in terms of processing speed and scale-up in ability to analyze large datasets, and provide a detailed, tutorial-style exposition that will benefit readers interested in developing GPU-based approaches in other statistical models. Novel, GPU-oriented approaches to modifying existing algorithms software design can lead to vast speed-up and, critically, enable statistical analyses that presently will not be performed due to compute time limitations in traditional computational environments. Supplemental materials are provided with all source code, example data, and details that will enable readers to implement and explore the GPU approach in this mixture modeling context.
dc.format.extent 419 - 438
dc.language ENG
dc.language.iso en_US en_US
dc.relation.ispartof J Comput Graph Stat
dc.relation.isversionof 10.1198/jcgs.2010.10016
dc.title Understanding GPU Programming for Statistical Computation: Studies in Massively Parallel Massive Mixtures.
dc.title.alternative en_US
dc.type Journal Article
dc.description.version Version of Record en_US
duke.date.pubdate 2010-6-0 en_US
duke.description.endpage 438 en_US
duke.description.issue 2 en_US
duke.description.startpage 419 en_US
duke.description.volume 19 en_US
dc.relation.journal Journal of Computational and Graphical Statistics en_US
pubs.author-url http://www.ncbi.nlm.nih.gov/pubmed/20877443
pubs.issue 2
pubs.organisational-group /Duke
pubs.organisational-group /Duke/School of Medicine
pubs.organisational-group /Duke/School of Medicine/Basic Science Departments
pubs.organisational-group /Duke/School of Medicine/Basic Science Departments/Biostatistics & Bioinformatics
pubs.organisational-group /Duke/School of Medicine/Institutes and Centers
pubs.organisational-group /Duke/School of Medicine/Institutes and Centers/Duke Cancer Institute
pubs.organisational-group /Duke/Student
pubs.organisational-group /Duke/Trinity College of Arts & Sciences
pubs.organisational-group /Duke/Trinity College of Arts & Sciences/Statistical Science
pubs.publication-status Published
pubs.volume 19

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