Analyzing Fuzzy Surface Modeling Using Load-Balanced Computation (Articolo in rivista)

Type
Label
  • Analyzing Fuzzy Surface Modeling Using Load-Balanced Computation (Articolo in rivista) (literal)
Anno
  • 2001-01-01T00:00:00+01:00 (literal)
Alternative label
  • Clematis A. (1), Spagnuolo M. (1) (2001)
    Analyzing Fuzzy Surface Modeling Using Load-Balanced Computation
    in Computing in science & engineering (Print)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Clematis A. (1), Spagnuolo M. (1) (literal)
Pagina inizio
  • 74 (literal)
Pagina fine
  • 81 (literal)
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  • Quest’articolo presenta i risultati di una ricerca per l’utilizzazione di metodi computazionali avanzati, quali l’aritmetica fuzzy ed il calcolo ad alte prestazioni, in settori applicativi come i sistemi informativi geografici. La ricerca è stata promossa da ricercatori CNR, e svolta in collaborazione con centri di prestigio internazionale quali l’Edinburgh Parallel Computing Centre ed il Department of Geography – University of Edinburgh. La rivista sulla quale l’articolo è apparso è edita dall’IEEE ed è rivolta a promuovere la conoscenza di metodi computazionali avanzati in un ambiente multidisciplinare. Impact Factor rivista: 1.175 (literal)
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  • 3 (literal)
Rivista
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  • In applications such as digital terrain modelling it is desirable to provide an explicit visualization of the model accuracy, for supporting users in spatial analysis activities. Coupling fuzzy arithmetic with traditional geometric models is an interesting possibility, but it requires the solution of computational problems related to the use of fuzzy-based models. In this context, we present a load balanced algorithm for the efficient use of parallel computing for typical queries of fuzzy models in Geographic Information Systems. We shortly introduce fuzzy arithmetic and its applications to terrain modelling. Then, the algorithm, which represents the computational kernel of querying fuzzy models, is described and its cost is evaluated. A parallelisation strategy for this algorithm is outlined. To obtain an efficient parallel code a great attention has to be paid to load balancing. We adopt an approach to load balancing, which introduces a low communication and computational overhead, it is flexible, portable, and it yields to good performances on different computing architectures. Experimental results obtained on a massively parallel machine, and on a network of heterogeneous workstations are discussed. Despite we refer to Geographic Information Systems as the present application field, the presented methodology could be applied to any scientific area in which surface representation and data uncertainity are important issues. (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • (1) IMATI-CNR (literal)
Titolo
  • Analyzing Fuzzy Surface Modeling Using Load-Balanced Computation (literal)
Abstract
  • In applications such as digital terrain modelling it is desirable to provide an explicit visualization of the model accuracy, for supporting users in spatial analysis activities. Coupling fuzzy arithmetic with traditional geometric models is an interesting possibility, but it requires the solution of computational problems related to the use of fuzzy-based models. In this context, we present a load balanced algorithm for the efficient use of parallel computing for typical queries of fuzzy models in Geographic Information Systems. We shortly introduce fuzzy arithmetic and its applications to terrain modelling. Then, the algorithm, which represents the computational kernel of querying fuzzy models, is described and its cost is evaluated. A parallelisation strategy for this algorithm is outlined. To obtain an efficient parallel code a great attention has to be paid to load balancing. We adopt an approach to load balancing, which introduces a low communication and computational overhead, it is flexible, portable, and it yields to good performances on different computing architectures. Experimental results obtained on a massively parallel machine, and on a network of heterogeneous workstations are discussed. Despite we refer to Geographic Information Systems as the present application field, the presented methodology could be applied to any scientific area in which surface representation and data uncertainity are important issues. (literal)
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