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Big Wins with Small Application-Aware Caches
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Source Conference on High Performance Networking and Computing archive
Proceedings of the 2004 ACM/IEEE conference on Supercomputing table of contents
Page: 20  
Year of Publication: 2004
ISBN:0-7695-2153-3
Authors
Julio C. Lopez  Carnegie Mellon University
David R. O'Hallaron  Carnegie Mellon University
Tiankai Tu  Carnegie Mellon University
Sponsor
SIGARCH: ACM Special Interest Group on Computer Architecture
Publisher
IEEE Computer Society  Washington, DC, USA
Bibliometrics
Downloads (6 Weeks): 1,   Downloads (12 Months): 17,   Citation Count: 4
Additional Information:

abstract   references   cited by   collaborative colleagues  

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DOI Bookmark: 10.1109/SC.2004.14

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

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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[3] M. Beynon, R. Ferreira, T. M. Kurc, A. Sussman, and J. H. Saltz. Datacutter: Middleware for filtering very large scientific datasets on archival storage systems. In Symp. on Mass Storage Systems, pages 119-134. IEEE, 2000.
 
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[16] J. Lopez, T. Tu, and D. O'Hallaron. CVMs: Community Velocity Model service. http://cvm.cs.cmu.edu, 2002.
 
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[17] H. Magistrale, R. Graves, and R. Clayton. A standard three-dimensional seismic velocity model for southern California: version 1. EOS Transactions AGU, 79:F605, 1998. www.scecdc.scec.org/3Dvelocity/ 3Dvelocity.html.
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[25] Southern California Earthquake Center. Community velocity model (SCEC/CME). www.scec.org/cme.
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[28] T. Tu, J. Lopez, and D. O'Hallaron. The Etree library: A system for manipulating large octrees on disk. Technical Report CMU-CS-03-174, Carnegie Mellon School of Computer Science, Pittsburgh, PA, July 2003.
 
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[29] T. Tu, D. O'Hallaron, and J. Lopez. Etree - a database-oriented method for generating large octree meshes. In Proceedings of the Eleventh International Meshing Roundtable, pages 127-138, Ithaca, NY, Sep 2002.
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Collaborative Colleagues:
Julio C. Lopez: colleagues
David R. O'Hallaron: colleagues
Tiankai Tu: colleagues