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Partitioning of Complex Discrete Models for Highly Scalable Simulations Cover

Partitioning of Complex Discrete Models for Highly Scalable Simulations

Open Access
|Dec 2025

References

  1. S. T. Barnard and H. D. Simon, “Fast multilevel implementation of recursive spectral bisection for partitioning unstructured problems”, Concurrency: Practice and Experience, vol. 6, no. 2, 1994, pp. 101-117.
  2. M. Berger and S. Bokhari, “A partitioning strategy for nonuniform problems on multiprocessors”, IEEE Transactions on Computers, vol. C-36, 1987.
  3. T. Chan, J. R. Gilbert, and S.-H. Teng, Geometric spectral partitioning, Citeseer, 1994.
  4. C. M. Fiduccia and R. M. Mattheyses, “A lineartime heuristic for improving network partitions”. In: Proceedings of the 19th Design Automation Conference, 1982, pp. 175-181.
  5. G. Karypis and V. Kumar, “A fast and high quality multilevel scheme for partitioning irregular graphs”, SIAM Journal on scientific Computing, vol. 20, no. 1, 1998, pp. 359-392.
  6. G. L. Miller, S. Teng, W. Thurston, and S. A. Vavasis. “Automatic mesh partitioning”. In: A. George, J. Gilbert, and J. Liu, eds., Graphs Theory and Sparse Matrix Computation, The IMA Volumes in Mathematics and its Application, pp. 57-84. Springer-Verlag, 1993. Vol 56.
  7. B. Monien and S. Schamberger, “Graph partitioning with the party library: Helpful-sets in practice”. In: 16th Symposium on Computer Architecture and High Performance Computing (SBAC-PAD 2004), 2004, pp. 198-205.
  8. K. Nagel, “Cellular automata models for transportation applications”. In: S. Bandini, B. Chopard, and M. Tomassini, eds., Cellular Automata, Berlin, Heidelberg, 2002, pp. 20-31.
  9. M. Paciorek, A. Bogacz, and W. Turek, “Scalable signal-based simulation of autonomous beings in complex environments”. In: International Conference on Computational Science (ICCSA 2020), 2020, pp. 144-157.
  10. M. Paciorek and W. Turek, “Agent-based modeling of social phenomena for high performance distributed simulations”. In: International Conference on Computational Science (ICCSA 2021), 2021, pp. 412-425.
  11. A. Pothen, H. D. Simon, and K.-P. Liou, “Partitioning sparse matrices with eigenvectors of graphs”, SIAM Journal on Matrix Analysis and Applications, vol. 11, no. 3, 1990, pp. 430-452.
  12. A. Pothen, H. D. Simon, L. Wang, and S. T. Barnard, “Towards a fast implementation of spectral nested dissection”. In: Supercomputing”92: Proceedings of the 1992 ACM/IEEE Conference on Supercomputing, 1992, pp. 42-51.
  13. R. Preis, “Linear time 1/2-approximation algorithm for maximum weighted matching in general graphs”. In: Annual Symposium on Theoretical Aspects of Computer Science, 1999, pp. 259-269.
  14. S. F. Railsback and V. Grimm, Agent-based and individual-based modeling: a practical introduction, Princeton University Press, 2019.
  15. S. Schamberger, “Improvements to the helpfulset algorithm and a new evaluation scheme for graph-partitioners”. In: V. Kumar, M. L. Gavrilova, C. J. K. Tan, and P. L”Ecuyer, eds., Computational Science and Its Applications (ICCSA 2003), Berlin, Heidelberg, 2003, pp. 49-53.
  16. W. Turek, L. Siwik, and A. Byrski, “Leveraging rapid simulation and analysis of large urban road systems on hpc”, Transportation Research PartC: Emerging Technologies, vol. 87, 2018, pp. 46-57.
  17. C. Walshaw and M. Cross, “Mesh partitioning: A multilevel balancing and refinement algorithm”, SIAM Journal on Scientific Computing, vol. 22, 2004.
  18. J. Ziarko. “Grid partitioning source code”, https://github.com/WKD622/grid-partitioning/tree/master. [Online; accessed 01-20-2024].
DOI: https://doi.org/10.14313/jamris-2025-033 | Journal eISSN: 2080-2145 | Journal ISSN: 1897-8649
Language: English
Page range: 26 - 34
Submitted on: May 9, 2024
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Accepted on: Feb 10, 2013
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Published on: Dec 24, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Jakub Ziarko, Mateusz Najdek, Wojciech Turek, published by Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.