Implementing cloud-based parallel metaheuristics: an overview

  • Patricia González Computer Architecture Group, Universidade da Coruña, Spain
  • Xoán Carlos Pardo Martínez Computer Architecture Group, Universidade da Coruña, Spain
  • Ramón Doallo Computer Architecture Group, Universidade da Coruña, Spain
  • Julio Banga BioProcess Engineering Group, IIM-CSIC, Spain
Keywords: cloud computing, MapReduce, MPI, parallel metaheuristics, Spark

Abstract

Metaheuristics are among the most popular methods for solving hard global optimization problems in many areas of science and engineering. Their parallel im- plementation applying HPC techniques is a common approach for efficiently using available resources to re- duce the time needed to get a good enough solution to hard-to-solve problems. Paradigms like MPI or OMP are the usual choice when executing them in clusters or supercomputers. Moreover, the pervasive presence of cloud computing and the emergence of programming models like MapReduce or Spark have given rise to an increasing interest in porting HPC workloads to the cloud, as is the case with parallel metaheuristics. In this paper we give an overview of our experience with different alternatives for porting parallel metaheuris- tics to the cloud, providing some useful insights to the interested reader that we have acquired through extensive experimentation.

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Published
2018-12-12
How to Cite
González, P., Pardo Martínez, X., Doallo, R., & Banga, J. (2018). Implementing cloud-based parallel metaheuristics: an overview. Journal of Computer Science and Technology, 18(03), e26. https://doi.org/10.24215/16666038.18.e26
Section
Original Articles