FUTURES-AMR: Towards an Adaptive Mesh Refinement Framework for Geosimulations

Authors Ashwin Shashidharan, Ranga Raju Vatsavai, Derek B. Van Berkel, Ross K. Meentemeyer

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Author Details

Ashwin Shashidharan
  • Department of Computer Science, North Carolina State University, Raleigh, USA
Ranga Raju Vatsavai
  • Department of Computer Science, North Carolina State University, Raleigh, USA
Derek B. Van Berkel
  • Center for Geospatial Analytics, North Carolina State University, Raleigh, USA
Ross K. Meentemeyer
  • Center for Geospatial Analytics, North Carolina State University, Raleigh, USA

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Ashwin Shashidharan, Ranga Raju Vatsavai, Derek B. Van Berkel, and Ross K. Meentemeyer. FUTURES-AMR: Towards an Adaptive Mesh Refinement Framework for Geosimulations. In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 16:1-16:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


Adaptive Mesh Refinement (AMR) is a computational technique used to reduce the amount of computation and memory required in scientific simulations. Geosimulations are scientific simulations using geographic data, routinely used to predict outcomes of urbanization in urban studies. However, the lack of support for AMR techniques with geosimulations limits exploring prediction outcomes at multiple resolutions. In this paper, we propose an adaptive mesh refinement framework FUTURES-AMR, based on static user-defined policies to enable multi-resolution geosimulations. We develop a prototype for the cellular automaton based urban growth simulation FUTURES by exploiting static and dynamic mesh refinement techniques in conjunction with the Patch Growing Algorithm (PGA). While, the static refinement technique supports a statically defined fixed resolution mesh simulation at a location, the dynamic refinement technique supports dynamically refining the resolution based on simulation outcomes at runtime. Further, we develop two approaches - asynchronous AMR and synchronous AMR, suitable for parallel execution in a distributed computing environment with varying support for solution integration of the multi-resolution results. Finally, using the FUTURES-AMR framework with different policies in an urban study, we demonstrate reduced execution time, and low memory overhead for a multi-resolution simulation.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Distributed simulation
  • Computing methodologies → Multiscale systems
  • Applied computing → Environmental sciences
  • Adaptive mesh refinement
  • Geosimulation
  • Distributed system
  • Multi-resolution
  • Urban geography


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