Optimizing disk storage to support statistical analysis operations

Parrish, Allen; Vrbsky, Susan V.; Dixon, Brandon; Ni, Weigang

doi:10.1016/j.dss.2003.09.005

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2005

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Cited by 4 publications

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“…However, CARE amortizes the performance costs: Users only pay for the data they use, as a detailed comparison between CARE's storage format and the typical row-major format commonly associated with other applications shows. 6 We designed CARE's data sets for distribution on stand-alone Windows platforms, making them application-independent of network or server support. The data's locality further enhances performance.…”

Section: Performancementioning

confidence: 99%

CARE: an automobile crash data analysis tool

et al. 2003

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Section: Performancementioning

confidence: 99%

CARE: an automobile crash data analysis tool

et al. 2003

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Utilizing commodity hardware and software to distribute a real-world application: maximizing reuse while improving performance

Davis

Smith

Dixon

et al. 2005

Softw: Pract. Exper.

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Commodity computing hardware continues to increase performance while decreasing price. This combination is driving a renewed interest in parallel and distributed computing. In this study, we examine the performance of an existing application in a ten‐node computing cluster using commodity off‐the‐shelf components. The application is a statistical analysis software package that processes categorical data used by state public safety programs. The study examines various network topologies and focuses on minimizing the software modifications required to distribute the application. We conclude that parallel computing using commodity components is an effective mechanism to increase the performance of real‐world applications especially when the underlying application architectures have the flexibility to support efficient reuse of the existing code. Copyright © 2005 John Wiley & Sons, Ltd.

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