Parallelization of a regionalization heuristic in distributed computing platforms – a case study of parallel-p-compact-regions problem

Jason Laura, WenWen Li, Sergio J. Rey, Luc Anselin

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

In this paper, we report efforts to develop a parallel implementation of the p-compact regionalization problem suitable for multi-core desktop and high-performance computing environments. Regionalization for data aggregation is a key component of many spatial analytical workflows that are known to be NP-Hard. We utilize a low communication cost parallel implementation technique that provides a benchmark for more complex implementations of this algorithm. Both the initialization phase, utilizing a Memory-based Randomized Greedy and Edge Reassignment (MERGE) algorithm, and the local search phase, utilizing Simulated Annealing, are distributed over available compute cores. Our results suggest that the proposed parallelization strategy is capable of solving the compactness-driven regionalization problem both efficiently and effectively. We expect this work to advance CyberGIS research by extending its application areas into the regionalization world and to make a contribution to the spatial analysis community by proposing this parallelization strategy to solve large regionalization problems efficiently.

Original languageEnglish (US)
Pages (from-to)536-555
Number of pages20
JournalInternational Journal of Geographical Information Science
Volume29
Issue number4
DOIs
StatePublished - Apr 3 2015

Keywords

  • CyberGIS
  • cyberinfrastructure
  • heuristic
  • performance
  • spatial optimization

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development
  • Library and Information Sciences

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