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High-Performance Data Mining with Skeleton-based Structured Parallel Programming

Coppola, Massimo and Vanneschi, Marco (2001) High-Performance Data Mining with Skeleton-based Structured Parallel Programming. Technical Report del Dipartimento di Informatica . Università di Pisa, Pisa, IT.

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    We show how to apply a Structured Parallel Programming methodology based on skeletons to Data Mining problems, reporting several results about three commonly used mining techniques, namely association rules, decision tree induction and spatial clustering. We analyze the structural patterns common to these applications, looking at application performance and software engineering efficiency. Our aim is to clearly state what features a Structured Parallel Programming Environment should have to be useful for parallel Data Mining. Within the skeleton-based PPE SkIE that we have developed, we study the different patterns of data access of parallel implementations of Apriori, C4.5 and DBSCAN. We need to address large partitions reads, frequent and sparse access to small blocks, as well as an irregular mix of small and large transfers, to allow efficient development of applications on huge databases. We examine the addition of an object/component interface to the skeleton structured model, to simplify the development of environment-integrated, parallel Data Mining applications.

    Item Type: Book
    Uncontrolled Keywords: High Performance Computing; Structured Parallel Programming;
    Subjects: Area01 - Scienze matematiche e informatiche > INF/01 - Informatica
    Divisions: Dipartimenti (until 2012) > DIPARTIMENTO DI INFORMATICA
    Depositing User: dott.ssa Sandra Faita
    Date Deposited: 23 Dec 2014 17:03
    Last Modified: 23 Dec 2014 17:03
    URI: http://eprints.adm.unipi.it/id/eprint/2051

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