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Scientific Data Management has become essential to the productivity of scientists using ever larger machines and running applications that produce ever more data. There are several specific issues when running on petascale (and beyond) machines. One is the need for massively parallel data output, which in part, depends on the data formats and semantics being used. Here, the inhibition of parallelism...
Most systems must evolve as their missions or roles change and/or as they adapt to new execution environments. When evolving large distributed applications, it is particularly difficult to make changes to the data formats that underlie their components' communications, because such 'format evolution' can affect all or many application components. Prior approaches to the problem of implementing changes...
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