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Summary: Cluster I/O with River: Making the Fast Case Common
Remzi H. Arpaci-Dusseau, Eric Anderson, Noah Treuhaft,
David E. Culler, Joseph M. Hellerstein, David Patterson, Kathy Yelick
Computer Science Division
University of California, Berkeley
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We introduce River, a data-flow programming environment and I/O
substrate for clusters of computers. River is designed to provide max-
imum performance in the common case -- even in the face of non-
uniformities in hardware, software, and workload. River is based on
two simpledesignfeatures: ahigh-performancedistributedqueue,and
a storage redundancy mechanism called graduated declustering. We
have implemented a number of data-intensive applications on River,
which validate our design with near-ideal performance in a variety of
non-uniform performance scenarios.
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Scalable I/O systems form the basis for much of the high-
performance computing market. In recent years, manufactur-
ers have found that growth in customer appetite for I/O capacity
is outstripping Moore's law [40]. Cluster systems are a key
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