Skip to main content
SpringerLink
Log in

Agent-Based Modeling of Social Phenomena for High Performance Distributed Simulations

  • Conference paper
  • First Online:
Computational Science – ICCS 2021 (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12743))

Included in the following conference series:

Abstract

Detailed models of numerous groups of social beings, which find applications in broad range of applications, require efficient methods of parallel simulation. Detailed features of particular models strongly influence the complexity of the parallelization problem. In this paper we identify and analyze existing classes of models and possible approaches to their simulation parallelization. We propose a new method for efficient scalability of the most challenging class of models: stochastic, with beings mobility and mutual exclusion of actions. The method is based on a concept of two-stage application of plans, which ensures equivalence of parallel and sequential execution. The method is analyzed in terms of distribution transparency and scalability at HPC-grade hardware. Both weak and strong scalability tests show speedup close to linear with more than 3000 parallel workers.

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 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight 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.

    https://youtu.be/9W-zmyQo-K8.

References

  1. Bezbradica, M., Crane, M., Ruskin, H.J.: Parallelisation strategies for large scale cellular automata frameworks in pharmaceutical modelling. In: 2012 International Conference on High Performance Computing & Simulation (HPCS), pp. 223–230. IEEE (2012)

    Google Scholar 

  2. Bowzer, C., Phan, B., Cohen, K., Fukuda, M.: Collision-free agent migration in spatial simulation. In: Proceedings of 11th Joint Agent-Oriented Workshops in Synergy (JAWS 2017), Prague, Czech (2017)

    Google Scholar 

  3. Bujas, J., Dworak, D., Turek, W., Byrski, A.: High-performance computing framework with desynchronized information propagation for large-scale simulations. J. Comput. Sci. 32, 70–86 (2019)

    Article  Google Scholar 

  4. Giordano, A., De Rango, A., D’Ambrosio, D., Rongo, R., Spataro, W.: Strategies for parallel execution of cellular automata in distributed memory architectures. In: 2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp. 406–413 (2019)

    Google Scholar 

  5. Kłusek, A., Topa, P., Wąs, J., Lubaś, R.: An implementation of the social distances model using multi-GPU systems. Int. J. High Perform. Comput. Appl. 32(4), 482–495 (2018)

    Article  Google Scholar 

  6. Kruskal, W.H., Wallis, W.A.: Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47(260), 583–621 (1952)

    Article  Google Scholar 

  7. Paciorek, M., Bogacz, A., Turek, W.: Scalable signal-based simulation of autonomous beings in complex environments. In: Krzhizhanovskaya, V.V., et al. (eds.) ICCS 2020. LNCS, vol. 12139, pp. 144–157. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50420-5_11

    Chapter  Google Scholar 

  8. Paciorek, M., Bujas, J., Dworak, D., Turek, W., Byrski, A.: Validation of signal propagation modeling for highly scalable simulations. Concurr. Comput. Pract. Exp,. e5718 (2020). https://doi.org/10.1002/cpe.5718

  9. Railsback, S.F., Grimm, V.: Agent-Based and Individual-based Modeling: A Practical Introduction. Princeton University Press, Princeton (2019)

    MATH  Google Scholar 

  10. Turek, W.: Erlang-based desynchronized urban traffic simulation for high-performance computing systems. Futur. Gener. Comput. Syst. 79, 645–652 (2018)

    Article  Google Scholar 

  11. Xia, C., Wang, H., Zhang, A., Zhang, W.: A high-performance cellular automata model for urban simulation based on vectorization and parallel computing technology. Int. J. Geogr. Inf. Sci. 32(2), 399–424 (2018)

    Article  Google Scholar 

Download references

Acknowledgements

The research presented in this paper was partially supported by the funds of Polish Ministry of Science and Higher Education assigned to AGH University of Science and Technology. This research was supported in part by PLGrid Infrastructure.

Author information

Authors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Mateusz Paciorek & Wojciech Turek

Authors
  1. Mateusz Paciorek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wojciech Turek
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mateusz Paciorek .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M. A. Sloot

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Paciorek, M., Turek, W. (2021). Agent-Based Modeling of Social Phenomena for High Performance Distributed Simulations. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12743. Springer, Cham. https://doi.org/10.1007/978-3-030-77964-1_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-77964-1_32

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77963-4

  • Online ISBN: 978-3-030-77964-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation