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dc.contributor.authorNakano, Junjien_US
dc.date.accessioned2009-01-29T14:54:20Z-
dc.date.available2009-01-29T14:54:20Z-
dc.date.issued2004en_US
dc.identifier.urihttp://hdl.handle.net/10419/22200-
dc.description.abstractParallel computing means to divide a job into several tasks and use more than one processor simultaneously to perform these tasks. Assume you have developed a new estimation method for the parameters of a complicated statistical model. After you prove the asymptotic characteristics of the method (for instance, asymptotic distribution of the estimator), you wish to perform many simulations to assure the goodness of the method for reasonable numbers of data values and for different values of parameters. You must generate simulated data, for example, 100 000 times for each length and parameter value. The total simulation work requires a huge number of random number generations and takes a long time on your PC. If you use 100 PCs in your institute to run these simulations simultaneously, you may expect that the total execution time will be 1/100. This is the simple idea of parallel computing.en_US
dc.language.isoengen_US
dc.publisheren_US
dc.relation.ispartofseriesPapers / Humboldt-Universität Berlin, Center for Applied Statistics and Economics (CASE) 2004,27en_US
dc.subject.ddc330en_US
dc.titleParallel computing techniquesen_US
dc.typeWorking Paperen_US
dc.identifier.ppn495308226en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
dc.identifier.repecRePEc:zbw:caseps:200427-
Appears in Collections:Papers, CASE - Center for Applied Statistics and Economics, HU Berlin

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