Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Book cover Advances in Geocomputation

Part of the book series: Advances in Geographic Information Science ((AGIS))

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    CPU denotes central processing unit, and GPU denotes graphics processing unit.

References

  • Acar UA, Blelloch GE, Blumofe RD (2002) The data locality of work stealing. Theory Comput Syst 35:321–347

    Article  Google Scholar 

  • Agarwal N, Nellans D, O’Connor M, Keckler SW, Wenisch TF (2015) Unlocking bandwidth for GPUs in CC-NUMA systems. In: 2015 IEEE 21st international symposium on high performance computer architecture (HPCA), Burlingame, CA. IEEE, pp 354–365

    Google Scholar 

  • Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512

    Article  Google Scholar 

  • Bayer R, McCreight E (1970) Organization and maintenance of large ordered indices. In: Proceedings of the 1970 ACM SIGFIDET (now SIGMOD) workshop on data description, access and control—SIGFIDET ‘70, New York, NY. ACM Press, pp 107–141

    Google Scholar 

  • Bian L (2004) A conceptual framework for an individual-based spatially explicit epidemiological model. Environ Plan 31:381–395

    Article  Google Scholar 

  • Billari FC, Prskawetz A (2003) Agent-based computational demography. Physica-Verlag HD, Heidelberg

    Book  Google Scholar 

  • Brown DG, Riolo R, Robinson DT, North M, Rand W (2005) Spatial process and data models: toward integration of agent-based models and GIS. J Geogr Syst 7:25–47

    Article  Google Scholar 

  • Crooks A, Castle C, Batty M (2008) Key challenges in agent-based modelling for geo-spatial simulation. Comput Environ Urban Syst 32:417–430

    Article  Google Scholar 

  • Demaine E (2002) Cache-oblivious algorithms and data structures. In: Lecture notes from the EEF summer school on massive data sets. BRICS, University of Aarhus, Denmark

    Google Scholar 

  • Denning PJ (1970) Virtual memory. ACM Comput Surv 2:153–189

    Article  Google Scholar 

  • Denning PJ (1980) Working sets past and present. IEEE Trans Softw Eng SE-6, 64–84

    Google Scholar 

  • Denning PJ (2006) The locality principle. In: Barria JA (ed) Communication networks and computer systems. Imperial College Press, London, pp 43–67

    Chapter  Google Scholar 

  • Epstein JM (1999) Agent-based computational models and generative social science. Complexity 4:41–60

    Article  Google Scholar 

  • Epstein JM (2009) Modelling to contain pandemics. Nature 460:687

    Article  Google Scholar 

  • Frigo M, Leiserson CE, Prokop H, Ramachandran S (1999) Cache-oblivious algorithms. In: 40th annual symposium on foundations of computer science, New York, NY. IEEE, pp 285–297

    Google Scholar 

  • Gong Z, Tang W, Bennett DA, Thill J-C (2013) Parallel agent-based simulation of individual-level spatial interactions within a multicore computing environment. Int J Geogr Inf Sci 27:1152–1170

    Article  Google Scholar 

  • Grimm V, Berger U, Bastiansen F et al (2006) A standard protocol for describing individual-based and agent-based models. Ecol Model 198(1–2):115–126

    Article  Google Scholar 

  • Günther F, Mehl M, Pögl M et al (2006) A cache-aware algorithm for PDEs on hierarchical data structures based on space-filling curves. SIAM J Sci Comput 28:1634–1650

    Article  Google Scholar 

  • Hagberg AA, Schult DA, Swart PJ (2008) Exploring network structure, dynamics, and function using networkx. In: Varoquaux G, Vaught T, Millman J (eds) Proceedings of the 7th python in science conference (SciPy2008). Pasadena, CA, pp 11–15

    Google Scholar 

  • Irwin EG (2010) New directions for urban economic models of land use change: incorporating spatial dynamics and heterogeneity. J Reg Sci 50:65–91 (2008)

    Article  Google Scholar 

  • Jeannot E, Meneses E, Mercier G, Tessier F, Zheng G (2013) Communication and topology-aware load balancing in Charm++ with treematch. In: 2013 IEEE international conference on cluster computing (CLUSTER), Indianapolis, IN. IEEE, pp 1–8

    Google Scholar 

  • Karypis G, Kumar V (1998a) A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J Sci Comput 20:359–392

    Article  Google Scholar 

  • Karypis G, Kumar V (1998b) Multilevel k-way partitioning scheme for irregular graphs. J Parall Distrib Comput 48:96–129

    Article  Google Scholar 

  • Kennedy K, McKinley KS (1992) Optimizing for parallelism and data locality. In: Proceedings of the 6th international conference on supercomputing (ICS ‘92), New York, NY. ACM Press, pp 323–334

    Google Scholar 

  • Kowarschik M, Weiß C (2003) An overview of cache optimization techniques and cache-aware numerical algorithms. In: Meyer U, Sanders P, Sibeyn J (eds) Algorithms for memory hierarchies: advanced lectures, vol 2625. Springer, Berlin, pp 213–232

    Chapter  Google Scholar 

  • Lawder JK, King PJH (2001) Querying multi-dimensional data indexed using the Hilbert space-filling curve. ACM SIGMOD Rec 30:19–24

    Article  Google Scholar 

  • Li M, Subhraveti D, Butt AR, Khasymski A, Sarkar P (2012) CAM: a topology aware minimum cost flow based resource manager for MapReduce applications in the cloud. In: Proceedings of the 21st international symposium on high-performance parallel and distributed computing—HPDC ‘12, New York, NY. ACM Press, pp 211–222

    Google Scholar 

  • Matthews RB, Gilbert NG, Roach A, Polhill JG, Gotts NM (2007) Agent-based land-use models: a review of applications. Landscape Ecol 22:1447–1459

    Article  Google Scholar 

  • Parker DC, Manson SM, Ma Janssen, Hoffmann MJ, Deadman P (2003) Multi-agent systems for the simulation of land-use and land-cover change: a review. Ann Assoc Am Geogr 93:314–337

    Article  Google Scholar 

  • Pilla LL, Ribeiro CP, Coucheney P, Broquedis F, Gaujal B, Navaux POA, Méhaut J-F (2014) A topology-aware load balancing algorithm for clustered hierarchical multi-core machines. Future Gen Comput Syst 30:191–201

    Article  Google Scholar 

  • Schubert E, Zimek A, Kriegel H-P (2013) Geodetic distance queries on R-Trees for indexing geographic data. In: Nascimento MA, Sellis T, Cheng R et al (eds) Advances in spatial and temporal databases. Springer, Berlin Heidelberg, pp 146–164

    Chapter  Google Scholar 

  • Shaheen M, Strzodka R (2012) NUMA aware iterative stencil computations on many-core systems. In: 2012 IEEE 26th international parallel and distributed processing symposium. IEEE, pp 461–473

    Google Scholar 

  • Stanilov K (2012) Space in agent-based models. In: Heppenstall AJ, Crooks AT, See LM, Batty M (eds) Agent-based models of geographical systems. Springer, Netherlands, Dordrecht, pp 253–269

    Chapter  Google Scholar 

  • Tang W (2008) Simulating complex adaptive geographic systems: a geographically aware intelligent agent approach. Cartogr Geogr Inf Sci 35:239–263

    Article  Google Scholar 

  • Wang S, Armstrong MP (2009) A theoretical approach to the use of cyberinfrastructure in geographical analysis. Int J Geogr Inf Sci 23:169–193

    Article  Google Scholar 

  • Weisbuch G, Deffuant G, Amblard F et al (2002) Meet, discuss, and segregate! Complexity 7:55–63

    Article  Google Scholar 

  • Wilkinson B, Allen M (2004) Parallel programming: techniques and applications using networked workstations and parallel computers, 2nd edn. Prentice-Hall Inc, Upper Saddle River, NJ

    Google Scholar 

  • Wolf ME, Lam MS (1991) A data locality optimizing algorithm. ACM SIGPLAN Not 26:30–44

    Article  Google Scholar 

  • Wu B, Zhang EZ, Shen X (2011) Enhancing data locality for dynamic simulations through asynchronous data transformations and adaptive control. In: 2011 international conference on parallel architectures and compilation techniques, Galveston, TX. IEEE, pp 243–252

    Google Scholar 

  • Xu Z, Tang C, Zhang Z (2003) Building topology-aware overlays using global soft-state. In: 23rd international conference on distributed computing systems, Providence, RI. IEEE, pp 500–508

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK

    Zhaoya Gong

  2. Department of Geography and Earth Sciences, University of North Carolina, Charlotte, USA

    Wenwu Tang & Jean-Claude Thill

Authors
  1. Zhaoya Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenwu Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jean-Claude Thill
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhaoya Gong .

Editor information

Editors and Affiliations

  1. School of Economic, Political and Policy Sciences, University of Texas at Dallas, Richardson, Texas, USA

    Daniel A. Griffith

  2. School of Economic, Political and Policy Sciences, University of Texas at Dallas, Richardson, Texas, USA

    Yongwan Chun

  3. School of Economic, Political and Policy Sciences, University of Texas at Dallas, Richardson, Texas, USA

    Denis J. Dean

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing Switzerland

About this paper

Cite this paper

Gong, Z., Tang, W., Thill, JC. (2017). A Graph-Based Locality-Aware Approach to Scalable Parallel Agent-Based Models of Spatial Interaction. In: Griffith, D., Chun, Y., Dean, D. (eds) Advances in Geocomputation. Advances in Geographic Information Science. Springer, Cham. https://doi.org/10.1007/978-3-319-22786-3_36

Download citation

Publish with us

Policies and ethics

Search

Navigation