Monte Carlo Simulation (SC '08 Education Program's Workshop on Parallel & Cluster Computing)

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dc.contributor.authorNeeman, Henry
dc.date.accessioned2015-08-16T21:17:59Z
dc.date.available2015-08-16T21:17:59Z
dc.date.issued2008-08-10
dc.descriptionDr. Henry Neeman, Director OU Supercomputing Center for Education & Research University of Oklahoma SC08 Education Program’s Workshop on Parallel & Cluster computing August 10-16 2008en_US
dc.description.abstractAn application is known as embarrassingly parallel if its parallel implementation: can straightforwardly be broken up into roughly equal amounts of work per processor, AND has minimal parallel overhead (e.g., communication among processors). We love embarrassingly parallel applications, because they get near-perfect parallel speedup, sometimes with modest programming effort. Embarrassingly parallel applications are also known as loosely coupled. Monte Carlo is a European city where people gamble; that is, they play games of chance, which involve randomness. Monte Carlo methods are ways of simulating (or otherwise calculating) physical phenomena based on randomness. Monte Carlo simulations typically are embarrassingly parallelen_US
dc.description.peerreviewNoen_US
dc.description.sponsorshipUniversity of Oklahoma OU Supercomputing Center for Education & Research (OSCER) SC08 Education Program’s Workshop on Parallel & Cluster computingen_US
dc.identifier.urihttp://hdl.handle.net/11244/15509
dc.languageen_USen_US
dc.relation.ispartofseriesSC '08 Education Program's Workshop on Parallel & Cluster Computing
dc.subjectComputer Scienceen_US
dc.titleMonte Carlo Simulation (SC '08 Education Program's Workshop on Parallel & Cluster Computing)en_US
dc.typePresentationen_US
ou.groupOklahoma Supercomputing::General::2008en_US
shareok.orcid0000-0002-4528-5391

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