NDLI: Performance issues in parallelizing data-intensive applications on a multi-core cluster

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Performance issues in parallelizing data-intensive applications on a multi-core cluster

Content Provider	CiteSeerX
Author	Ravi, Vignesh T. Agrawal, Gagan
Description	The deluge of available data for analysis demands the need to scale the performance of data mining implementations. With the current architectural trends, one of the major challenges today is achieving programmability and performance for data mining applications on multi-core machines and cluster of multi-core machines. To address this problem, we have been developing a runtime framework, FREERIDE, that enables parallel execution of data mining and data analysis tasks. The contributions of this paper are two-fold: 1) This paper describes and evaluates various shared-memory parallelization techniques developed in our run-time system on a cluster of multi-cores, and 2) We report on a detailed performance study to understand why certain parallelization techniques outperform other techniques for a particular application. 1
File Format	PDF
Language	English
Publisher Institution	Proc. of Intl. Symposium on Cluster Computing and the Grid (CCGRID 2009), IEEE Computer Society
Access Restriction	Open
Subject Keyword	Multi-core Machine Major Challenge Today Data Mining Implementation Data-intensive Application Data Mining Available Data Particular Application Multi-core Cluster Run-time System Certain Parallelization Technique Parallel Execution Runtime Framework Data Mining Application Performance Issue Data Analysis Task Detailed Performance Study Various Shared-memory Parallelization Technique Current Architectural Trend
Content Type	Text
Resource Type	Article

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