HPC-ICTM: The Interval Categorizer Tessellation-Based Model for High Performance Computing

  • Marilton S. de Aguiar
  • Graçaliz P. Dimuro
  • Fábia A. Costa
  • Rafael K. S. Silva
  • César A. F. De Rose
  • Antônio C. R. Costa
  • Vladik Kreinovich
Conference paper

DOI: 10.1007/11558958_10

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3732)
Cite this paper as:
de Aguiar M.S. et al. (2006) HPC-ICTM: The Interval Categorizer Tessellation-Based Model for High Performance Computing. In: Dongarra J., Madsen K., Waśniewski J. (eds) Applied Parallel Computing. State of the Art in Scientific Computing. PARA 2004. Lecture Notes in Computer Science, vol 3732. Springer, Berlin, Heidelberg

Abstract

This paper presents the Interval Categorizer Tessellation-based Model (ICTM) for the simultaneous categorization of geographic regions considering several characteristics (e.g., relief, vegetation, land use etc.). Interval techniques are used for the modelling of uncertain data and the control of discretization errors. HPC-ICTM is an implementation of the model for clusters. We analyze the performance of the HPC-ICTM and present results concerning its application to the relief/land-use categorization of the region surrounding the lagoon Lagoa Pequena (RS, Brazil), which is extremely important from an ecological point of view.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marilton S. de Aguiar
    • 1
  • Graçaliz P. Dimuro
    • 1
  • Fábia A. Costa
    • 1
  • Rafael K. S. Silva
    • 2
  • César A. F. De Rose
    • 2
  • Antônio C. R. Costa
    • 1
    • 3
  • Vladik Kreinovich
    • 4
  1. 1.Escola de InformáticaUniversidade Católica de PelotasPelotasBrazil
  2. 2.PPGCCPontifícia Universidade Católica do Rio Grande do SulPorto AlegreBrazil
  3. 3.PPGCUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
  4. 4.Department of Computer ScienceUniversity of Texas at El PasoEl PasoUSA

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