JPL Technical Report Server

Climate Data Assimilation on a Massively Parallel Supercomputer

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dc.contributor.author Ding, Hong Q. en_US
dc.contributor.author Ferraro, Robert D. en_US
dc.date.accessioned 2004-09-30T23:10:31Z
dc.date.available 2004-09-30T23:10:31Z
dc.date.issued 1996-11 en_US
dc.identifier.citation not specified en_US
dc.identifier.clearanceno 96-1259 en_US
dc.identifier.uri http://hdl.handle.net/2014/26396
dc.description.abstract We have designed and implemented a set of highly efficient and highly scalable algorithms for an unstructured computational package, the PSAS data assimilation package, as demonstrated by detailed performance analysis of systematic runs on up to 512-nodes of an Intel Paragon. The preconditioned Conjugate Gradient solver achieves a sustained 18 Gflops performance. Consequently, we achieve an unprecedented 100-fold reduction in time to solution on the Intel Paragon over a single head of a Cray C90. This not only exceeds the daily performance requirement of the Data Assimilation Office at NASA's Goddard Space Flight Center, but also makes it possible to explore much larger and challenging data assimilation problems which are unthinkable on a traditional computer platform such as the Cray C90. en_US
dc.format.extent 575359 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.subject.other algorithms Intel Paragon PSAS data assimilation Cray C90 massively parallel supercomputer en_US
dc.title Climate Data Assimilation on a Massively Parallel Supercomputer en_US


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