CyberGIS-ABM: Synergizing High-Performance Computing and Network Science for Scalable Agent-Based Modeling of Human-Environment Interactions
Topics: Geographic Information Science and Systems
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Keywords: Agent-based modelling, CyberGIS, High-performance computing
Session Type: Virtual Paper Abstract
Day: Saturday
Session Start / End Time: 2/26/2022 11:20 AM (Eastern Time (US & Canada)) - 2/26/2022 12:40 PM (Eastern Time (US & Canada))
Room: Virtual 20
Authors:
Rebecca Catherine Vandewalle, University of Illinois Urbana Champaign
Shaowen Wang, University of Illinois Urbana Champaign
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Abstract
Recent and ongoing crises, such as the COVID-19 pandemic and western U.S. wildfires, underscore the complexity surrounding interrelationships between human actions and impacts such as disease spread and evacuation congestion. Growing recognition of the far reaching impact that individual actions can have on broader environments calls for approaches that can capture multi-scale processes that depend on small scale actions and interactions.
A powerful tool for modeling complex processes can be found in agent-based models (ABMs) through "bottom-up" reasoning. In an agent-based model, actions are encoded for individual actors, or agents. Agents have the capability to both interact with other agents and shape the environment. However, scaling up an agent-based model requires more significant computational resources, especially when the model is based on spatial network representations.
To address these scaling limitations, we have developed a CyberGIS-ABM framework for synergizing high-performance computing and network science for scalable agent-based modeling of human-environment interactions. This framework is designed to directly harness high-performance computing resources using Message Passing Interface (MPI), object-oriented programming, and parallel computing software design patterns. In parallel computing, load balancing is a key strategy intended to evenly distribute work between processors to improve computational performance. The CyberGIS-ABM framework exploits network characteristics to achieve dynamic load-balancing. Furthermore, the framework is optimized to handle spatial network data (e.g., OpenStreetMap street network data).
The paper will illustrate how to scale up an ABM to effectively utilize high-performance computing resources and extend the possibilities for informed analyses in the context of large-scale emergency evacuation modeling.
CyberGIS-ABM: Synergizing High-Performance Computing and Network Science for Scalable Agent-Based Modeling of Human-Environment Interactions
Category
Virtual Paper Abstract
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