Improving Metaheuristics for Mapping Independent Tasks into Heterogeneous Memory-Constrained Systems

  • Javier Cuenca
  • Domingo Giménez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5101)


This paper shows different strategies for improving some metaheuristics for the solution of a task mapping problem. Independent tasks with different computational costs and memory requirements are scheduled in a heterogeneous system with computational heterogeneity and memory constraints. The tuned methods proposed in this work could be used for optimizing realistic systems, such as scheduling independent processes onto a processors farm.


processes mapping metaheuristics heterogeneous systems 


  1. 1.
    Wilkinson, B., Allen, M.: Parallel Programming: Techniques and Applications Using Networked Workstations and Parallel Computers, 2nd edn. Prentice-Hall, Englewood Cliffs (2005)Google Scholar
  2. 2.
    Grama, A., Gupta, A., Karypis, G., Kumar, V.: Introduction to Parallel Computing, 2nd edn. Addison-Wesley, Reading (2003)Google Scholar
  3. 3.
    Banino, C., Beaumont, O., Legrand, A., Robert, Y.: Sheduling strategies for master-slave tasking on heterogeneous processor grids. In: Fagerholm, J., Haataja, J., Järvinen, J., Lyly, M., Råback, P., Savolainen, V. (eds.) PARA 2002. LNCS, vol. 2367, pp. 423–432. Springer, Heidelberg (2002)Google Scholar
  4. 4.
    Pinau, J.F., Robert, Y., Vivien, F.: Off-line and on-line scheduling on heterogeneous master-slave platforms. In: 14th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP 2006), pp. 439–446 (2006)Google Scholar
  5. 5.
    Brucker, P.: Scheduling Algorithms, 1st edn. Springer, Heidelberg (2007)zbMATHGoogle Scholar
  6. 6.
    Lennerstad, H., Lundberg, L.: Optimal scheduling results for parallel computing. SIAM News, 16–18 (1994)Google Scholar
  7. 7.
    Cuenca, J., Giménez, D., López, J.J., Martínez-Gallary, J.P.: A proposal of metaheuristics to schedule independent tasks in heterogeneous memory-constrained systems. In: CLUSTER (2007)Google Scholar
  8. 8.
    Hromkovic, J.: Algorithmics for Hard Problems, 2nd edn. Springer, Heidelberg (2003)Google Scholar
  9. 9.
    Dréo, J., Pétrowski, A., Siarry, P., Taillard, E.: Metaheuristics for Hard Optimization. Springer (2005)Google Scholar
  10. 10.
    Raidl, G.R.: A unified view on hybrid metaheuristics. In: Almeida, F., Blesa Aguilera, M.J., Blum, C., Moreno Vega, J.M., Pérez Pérez, M., Roli, A., Sampels, M. (eds.) HM 2006. LNCS, vol. 4030, pp. 1–12. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Javier Cuenca
    • 1
  • Domingo Giménez
    • 2
  1. 1.Departamento de Ingeniería y Tecnología de ComputadoresUniversidad de MurciaMurciaSpain
  2. 2.Departamento de Informática y SistemasUniversidad de MurciaMurciaSpain

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