A Graph-Based Locality-Aware Approach to Scalable Parallel Agent-Based Models of Spatial Interaction

Zhaoya Gong; Wenwu Tang; Jean-Claude Thill

doi:10.1007/978-3-319-22786-3_36

A Graph-Based Locality-Aware Approach to Scalable Parallel Agent-Based Models of Spatial Interaction

Zhaoya Gong, Wenwu Tang, Jean-Claude Thill

Geography, Earth and Environmental Sciences

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

Abstract

A great potential exists for mitigating the computational costs of spatially
explicit agent-based models (SE-ABMs) by taking advantage of parallel and
high-performance computing. However, spatial dependency and heterogeneity of
interactions between agents pose challenges for parallel SE-ABMs to achieve good scalability. This chapter summarizes an application of the principle of data locality to tackle these challenges by extending a theoretical approach to the representation of the spatial computational domain. We propose and formalize a graph-based locality-aware approach to scalable parallelization of SE-ABMs. To demonstrate the applicability of this approach, two sets of experimentation are laid out and a locality-aware algorithm is designed to facilitate the study of model scalability. The results of simulation experiments illustrate the advantage of our approach to scalable parallel agent-based models of spatial interaction.

Original language	English
Title of host publication	Advances in Geocomputation:
Subtitle of host publication	Geocomputation 2015--The 13th International Conference
Editors	Daniel A. Griffith, Yongwan Chun, Denis J. Dean
Publisher	Springer
Pages	405-423
ISBN (Electronic)	9783319227863
ISBN (Print)	9783319227856
DOIs	https://doi.org/10.1007/978-3-319-22786-3_36
Publication status	Published - 5 Jan 2017
Event	GeoComputation 2015: The 13th International Conference - University of Texas at Dallas, Dallas, Texas, United States Duration: 20 May 2015 → 23 May 2015 http://www.utdallas.edu/geocomputation/

Publication series

Name	Advances in Geographic Information Science
Publisher	Springer
ISSN (Print)	1867-2434
ISSN (Electronic)	1867-2442

Conference

Conference	GeoComputation 2015
Country/Territory	United States
City	Dallas, Texas
Period	20/05/15 → 23/05/15
Internet address	http://www.utdallas.edu/geocomputation/

Keywords

Locality awareness
Parallel agent-based models
Spatial interaction

Access to Document

10.1007/978-3-319-22786-3_36Licence: None: All rights reserved

https://link.springer.com/chapter/10.1007%2F978-3-319-22786-3_36Licence: None: All rights reserved

Cite this

Gong, Z., Tang, W., & Thill, J-C. (2017). A Graph-Based Locality-Aware Approach to Scalable Parallel Agent-Based Models of Spatial Interaction. In D. A. Griffith, Y. Chun, & D. J. Dean (Eds.), Advances in Geocomputation: Geocomputation 2015--The 13th International Conference (pp. 405-423). (Advances in Geographic Information Science). Springer. https://doi.org/10.1007/978-3-319-22786-3_36

Gong, Zhaoya ; Tang, Wenwu ; Thill, Jean-Claude. / A Graph-Based Locality-Aware Approach to Scalable Parallel Agent-Based Models of Spatial Interaction. Advances in Geocomputation: Geocomputation 2015--The 13th International Conference. editor / Daniel A. Griffith ; Yongwan Chun ; Denis J. Dean. Springer, 2017. pp. 405-423 (Advances in Geographic Information Science).

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title = "A Graph-Based Locality-Aware Approach to Scalable Parallel Agent-Based Models of Spatial Interaction",

abstract = "A great potential exists for mitigating the computational costs of spatiallyexplicit agent-based models (SE-ABMs) by taking advantage of parallel andhigh-performance computing. However, spatial dependency and heterogeneity ofinteractions between agents pose challenges for parallel SE-ABMs to achieve good scalability. This chapter summarizes an application of the principle of data locality to tackle these challenges by extending a theoretical approach to the representation of the spatial computational domain. We propose and formalize a graph-based locality-aware approach to scalable parallelization of SE-ABMs. To demonstrate the applicability of this approach, two sets of experimentation are laid out and a locality-aware algorithm is designed to facilitate the study of model scalability. The results of simulation experiments illustrate the advantage of our approach to scalable parallel agent-based models of spatial interaction.",

keywords = "Locality awareness, Parallel agent-based models, Spatial interaction",

author = "Zhaoya Gong and Wenwu Tang and Jean-Claude Thill",

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booktitle = "Advances in Geocomputation:",

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Gong, Z, Tang, W & Thill, J-C 2017, A Graph-Based Locality-Aware Approach to Scalable Parallel Agent-Based Models of Spatial Interaction. in DA Griffith, Y Chun & DJ Dean (eds), Advances in Geocomputation: Geocomputation 2015--The 13th International Conference. Advances in Geographic Information Science, Springer, pp. 405-423, GeoComputation 2015, Dallas, Texas, United States, 20/05/15. https://doi.org/10.1007/978-3-319-22786-3_36

A Graph-Based Locality-Aware Approach to Scalable Parallel Agent-Based Models of Spatial Interaction. / Gong, Zhaoya; Tang, Wenwu; Thill, Jean-Claude.
Advances in Geocomputation: Geocomputation 2015--The 13th International Conference. ed. / Daniel A. Griffith; Yongwan Chun; Denis J. Dean. Springer, 2017. p. 405-423 (Advances in Geographic Information Science).

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

TY - CHAP

T1 - A Graph-Based Locality-Aware Approach to Scalable Parallel Agent-Based Models of Spatial Interaction

AU - Gong, Zhaoya

AU - Tang, Wenwu

AU - Thill, Jean-Claude

PY - 2017/1/5

Y1 - 2017/1/5

N2 - A great potential exists for mitigating the computational costs of spatiallyexplicit agent-based models (SE-ABMs) by taking advantage of parallel andhigh-performance computing. However, spatial dependency and heterogeneity ofinteractions between agents pose challenges for parallel SE-ABMs to achieve good scalability. This chapter summarizes an application of the principle of data locality to tackle these challenges by extending a theoretical approach to the representation of the spatial computational domain. We propose and formalize a graph-based locality-aware approach to scalable parallelization of SE-ABMs. To demonstrate the applicability of this approach, two sets of experimentation are laid out and a locality-aware algorithm is designed to facilitate the study of model scalability. The results of simulation experiments illustrate the advantage of our approach to scalable parallel agent-based models of spatial interaction.

AB - A great potential exists for mitigating the computational costs of spatiallyexplicit agent-based models (SE-ABMs) by taking advantage of parallel andhigh-performance computing. However, spatial dependency and heterogeneity ofinteractions between agents pose challenges for parallel SE-ABMs to achieve good scalability. This chapter summarizes an application of the principle of data locality to tackle these challenges by extending a theoretical approach to the representation of the spatial computational domain. We propose and formalize a graph-based locality-aware approach to scalable parallelization of SE-ABMs. To demonstrate the applicability of this approach, two sets of experimentation are laid out and a locality-aware algorithm is designed to facilitate the study of model scalability. The results of simulation experiments illustrate the advantage of our approach to scalable parallel agent-based models of spatial interaction.

KW - Locality awareness

KW - Parallel agent-based models

KW - Spatial interaction

U2 - 10.1007/978-3-319-22786-3_36

DO - 10.1007/978-3-319-22786-3_36

M3 - Chapter (peer-reviewed)

SN - 9783319227856

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SP - 405

EP - 423

BT - Advances in Geocomputation:

A2 - Griffith, Daniel A.

A2 - Chun, Yongwan

A2 - Dean, Denis J.

PB - Springer

T2 - GeoComputation 2015

Y2 - 20 May 2015 through 23 May 2015

ER -

A Graph-Based Locality-Aware Approach to Scalable Parallel Agent-Based Models of Spatial Interaction

Abstract

Publication series

Conference

Keywords

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Cite this