A communication-aware framework for parallel spatially explicit agent-based models

Shook, Eric; Wang, Shaowen; Tang, Wenwu

doi:10.1080/13658816.2013.771740

Cited by 51 publications

(34 citation statements)

References 63 publications

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“…agent-based modelling) is important for understanding and making decisions within these highly complex, interwoven and distributed environments (Shook, Wang, and Tang 2013).…”

Section: Discussionmentioning

confidence: 99%

Computational and data sciences for health-GIS

Shi

Wang

2015

Annals of GIS

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Computational and data sciences are transforming the entire science enterprise. In the arena of GIS, this is represented by the emergence of cyberGIS. We provide an overview of applying the cyberGIS approach to spatial analysis for health studies. We emphasize that cyberGIS is not just a service to traditional spatial analyses, but itself is an alternative approach to problem solving. Some fundamental and profound distinctions of cyberGIS approaches in health-GIS include the following: (1) they may greatly reduce the reliance on models or assumptions, and instead seek actual empirical evidence through mining a large amount of data or virtual empirical evidence generated through computation; (2) they tend to be non-parametric and tend to generate local solution; (3) they are scalable to high-resolution and less aggregated data; (4) they tend to be stochastic rather than deterministic; and (5) with these approaches, the large amount of data may not be only from input data-sets, but also from analytical workflows. We described the kernel ratio estimation for local intensity estimation, the restricted and controlled Monte Carlo for data disaggregation, and unrestricted and controlled Monte Carlo for statistical significance evaluation as examples of the cyberGIS approaches in health-GIS.

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“…agent-based modelling) is important for understanding and making decisions within these highly complex, interwoven and distributed environments (Shook, Wang, and Tang 2013).…”

Section: Discussionmentioning

confidence: 99%

Computational and data sciences for health-GIS

Shi

Wang

2015

Annals of GIS

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“…Even though a number of domain decomposition methods have been proposed and applied in the last few years (e.g., Quinn et al 2003, Linard et al 2009, Parker and Epstein 2011, Shook et al 2013, to achieve load-balance for parallel spatial computing, the computational intensity must be effectively estimated and spatially represented before domain decomposition, which is one of the main focuses of this study. The computational intensity surface (CIS), proposed by Wang and Armstrong (2009), provides a means to represent the spatial heterogeneity of workload for a wide range of geospatial algorithms.…”

Section: Parallel Vector Map Visualizationmentioning

confidence: 99%

A spatially adaptive decomposition approach for parallel vector data visualization of polylines and polygons

Guo

Guan

Xie

et al. 2015

International Journal of Geographical Information Science

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With the wide adoption of big spatial data and the emergence of CyberGIS, the nontrivial computational intensity introduced by massive amount of data poses great challenges to the performance of vector map visualization. The parallel computing technologies provide promising solutions to such problems. Evenly decomposing the visualization task into multiple subtasks is one of the key issues in parallel visualization of vector data. This study focuses on the decomposition of polyline and polygon data for parallel visualization. Two key factors impacting the computational intensity were identified: the number of features and the number of vertices of each feature. The computational intensity transform functions (CITFs) were constructed based on the linear relationships between the factors and the computing time. The computational intensity grid (CIG) can then be constructed using the CITFs to represent the spatial distribution of computational intensity. A noninterlaced continuous space-filling curve is used to group the lattices of CIG into multiple sub-domains such that each subdomain entails the same amount of computational intensity as others. The experiments demonstrated that the approach proposed in this paper was able to effectively estimate and spatially represent the computational intensity of visualizing polylines and polygons. Compared with the regular domain decomposition methods, the new approach generated much more balanced decomposition of computational intensity for parallel visualization and achieved near-linear speedups, especially when the data is greatly heterogeneously distributed in space.

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“…Parallel ABM decomposes the original sequential model into multiple sub-problems that can be distributed to different computing units and solved simultaneously. This approach requires researchers to design new algorithms and implementations of ABMs tailored for the underlying parallel platforms [2,3]. Parallel ABM techniques have been applied to different research problems, including spatial interaction, vegetation dynamics, urban growth, and disease diffusion.…”

Section: Parallel Abm Simulationmentioning

confidence: 99%

“…Agent-based modeling (ABM) is an important and efficient approach to understand dynamic geospatial phenomena in a bottom-up manner [1][2][3][4]. It enables researchers to study emergent system-level patterns and outcomes from generalized interactions at the individual agent level [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

4D-SAS: A Distributed Dynamic-Data Driven Simulation and Analysis System for Massive Spatial Agent-Based Modeling

Guan

et al. 2016

IJGI

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Significant computation challenges are emerging as agent-based modeling becomes more complicated and dynamically data-driven. In this context, parallel simulation is an attractive solution when dealing with massive data and computation requirements. Nearly all the available distributed simulation systems, however, do not support geospatial phenomena modeling, dynamic data injection, and real-time visualization. To tackle these problems, we propose a distributed dynamic-data driven simulation and analysis system (4D-SAS) specifically for massive spatial agent-based modeling to support real-time representation and analysis of geospatial phenomena. To accomplish large-scale geospatial problem-solving, the 4D-SAS system was spatially enabled to support geospatial model development and employs high-performance computing to improve simulation performance. It can automatically decompose simulation tasks and distribute them among computing nodes following two common schemes: order division or spatial decomposition. Moreover, it provides streaming channels and a storage database to incorporate dynamic data into simulation models; updating agent context in real-time. A new online visualization module was developed based on a GIS mapping library, SharpMap, for an animated display of model execution to help clients understand the model outputs efficiently. To evaluate the system's efficiency and scalability, two different spatially explicitly agent-based models, an en-route choice model, and a forest fire propagation model, were created on 4D-SAS. Simulation results illustrate that 4D-SAS provides an efficient platform for dynamic data-driven geospatial modeling, e.g., both discrete multi-agent simulation and grid-based cellular automata, demonstrating efficient support for massive parallel simulation. The parallel efficiency of the two models is above 0.7 and remains nearly stable in our experiments.

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A communication-aware framework for parallel spatially explicit agent-based models

Cited by 51 publications

References 63 publications

Computational and data sciences for health-GIS

Computational and data sciences for health-GIS

A spatially adaptive decomposition approach for parallel vector data visualization of polylines and polygons

4D-SAS: A Distributed Dynamic-Data Driven Simulation and Analysis System for Massive Spatial Agent-Based Modeling

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