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ABSTRACT
Large datasets, on the order of GB and TB, are increasingly common as abundant computational resources allow practitioners to collect, produce and store data at higher rates. As dataset sizes grow, it becomes more challenging to interactively manipulate and analyze these datasets due to the large amounts of data that need to be moved and processed. Application-independent caches, such as operating system page caches and database buffer caches, are present throughout the memory hierarchy to reduce data access times and alleviate transfer overheads. We claim that an application-aware cache with relatively modest memory requirements can effectively exploit dataset structure and application information to speed access to large datasets. We demonstrate this idea in the context of a system named the tree cache, to reduce query latency to large octree datasets by an order of magnitude.
REFERENCES
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CITED BY 4
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Stratos Papadomanolakis , Anastassia Ailamaki , Julio C. Lopez , Tiankai Tu , David R. O'Hallaron , Gerd Heber, Efficient query processing on unstructured tetrahedral meshes, Proceedings of the 2006 ACM SIGMOD international conference on Management of data, June 27-29, 2006, Chicago, IL, USA
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Steven W. Schlosser , Michael P. Ryan , Ricardo Taborda , Julio López , David R. O'Hallaron , Jacobo Bielak, Materialized community ground models for large-scale earthquake simulation, Proceedings of the 2008 ACM/IEEE conference on Supercomputing, November 15-21, 2008, Austin, Texas
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