Parallel Algorithm for Landform Attributes Representation on Multicore and Multi-GPU Systems

  • Murilo Boratto
  • Pedro Alonso
  • Carla Ramiro
  • Marcos Barreto
  • Leandro Coelho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7333)


Mathematical models are often used to simplify landform representation. Its importance is due to the possibility of describing phenomena by means of mathematical models from a data sample. High processing power is needed to represent large areas with a satisfactory level of details. In order to accelerate the solution of complex problems, it is necessary to combine two basic components in heterogeneous systems formed by a multicore with one or more GPUs. In this paper, we present a methodology to represent landform attributes on multicore and multi-GPU systems using high performance computing techniques for efficient solution of two-dimensional polynomial regression model that allow to address large problem instances.


Mathematical Modeling Landform Representation Parallel Computing Performance Estimation Multicore Multi-GPU 


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  1. 1.
    Bajaj, C., Ihm, I., Warren, J.: Higher-order interpolation and least-squares approximation using implicit algebraic surfaces. ACM Transactions on Graphics 12, 327–347 (1993)zbMATHCrossRefGoogle Scholar
  2. 2.
    Ballard, G., Demmel, J., Gearhart, A.: Communication bounds for heterogeneous architectures. Tech. Rep. 239, LAPACK Working Note (February 2011)Google Scholar
  3. 3.
    Barnat, J., Bauch, P., Brim, L., Ceska, M.: Computing strongly connected components in parallel on CUDA. In: Proceedings of the 25th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2011), pp. 544–555. IEEE Computer Society (2011)Google Scholar
  4. 4.
    Chapman, B., Jost, G., van der Pas, R.: Using OpenMP: portable shared memory parallel programming (scientific and engineering computation). The MIT Press (2007)Google Scholar
  5. 5.
    Golub, G.H., Loan, C.F.V.: Matrix Computations, 2nd edn., Baltimore, MD, USA (1989)Google Scholar
  6. 6.
    Marr, D.T., Binns, F., Hill, D.L., Hinton, G., Koufaty, D.A., Miller, J.A., Upton, M.: Hyper-threading technology architecture and microarchitecture. Intel Technology Journal 6(1), 1–12 (2002)Google Scholar
  7. 7.
    Namikawa, L.M., Renschler, C.S.: Uncertainty in digital elevation data used for geophysical flow simulation. In: GeoInfo, pp. 91–108 (2004)Google Scholar
  8. 8.
    Nogueira, L., Abrantes, R.P., Leal, B.: A methodology of distributed processing using a mathematical model for landform attributes representation. In: Proceeding of the IADIS International Conference on Applied Computing (April 2008)Google Scholar
  9. 9.
    Nogueira, L., Abrantes, R.P., Leal, B., Goulart, C.: A model of landform attributes representation for application in distributed systems. In: Proceeding of the IADIS International Conference on Applied Computing (April 2008)Google Scholar
  10. 10.
    Rawlings, J.O., Pantula, S.G., Dickey, D.A.: Applied Regression Analysis: A Research Tool. Springer Texts in Statistics. Springer (April 1998)Google Scholar
  11. 11.
    Rufino, I., Galvão, C., Rego, J., Albuquerque, J.: Water resources and urban planning: the case of a coastal area in brazil. Journal of Urban and Environmental Engineering 3, 32–42 (2009)CrossRefGoogle Scholar
  12. 12.
    Rutzinger, M., Hofle, B., Vetter, M., Pfeifer, N.: Digital terrain models from airborne laser scanning for the automatic extraction of natural and anthropogenic linear structures. In: Geomorphological Mapping: a Professional Handbook of Techniques and Applications, pp. 475–488. Elsevier (2011)Google Scholar
  13. 13.
    Sengupta, S., Harris, M., Zhang, Y., Owens, J.D.: Scan primitives for GPU computing. In: Proceedings of the 22nd ACM SIGGRAPH/EUROGRAPHICS Symposium on Graphics Hardware, pp. 97–106. Eurographics Association, Aire-la-Ville (2007)Google Scholar
  14. 14.
    Song, F., Tomov, S., Dongarra, J.: Efficient support for matrix computations on heterogeneous multicore and multi-GPU architectures. Tech. Rep. 250, LAPACK Working Note (June 2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Murilo Boratto
    • 1
  • Pedro Alonso
    • 2
  • Carla Ramiro
    • 2
  • Marcos Barreto
    • 3
  • Leandro Coelho
    • 1
  1. 1.Núcleo de Arquitetura de Computadores e Sistemas Operacionais (ACSO)Universidade do Estado da Bahia (UNEB)SalvadorBrazil
  2. 2.Departamento de Sistemas Informaticos y Computación (DSIC)Universidad Politécnica de Valencia (UPV)ValenciaEspaña
  3. 3.Laboratório de Sistemas Distribuídos (LaSiD)Universidade Federal da Bahia (UFBA)SalvadorBrazil

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