Computational Model Library

PPHPC - Predator-Prey for High-Performance Computing (1.4.0)

PPHPC is a conceptual model which captures important characteristics of spatial agent-based models (SABMs), such as agent movement and local agent interactions. It was designed with several goals in mind:

  • Provide a basis for a tutorial on complete model specification and thorough simulation output analysis.
  • Investigate statistical comparison strategies for model replication.
  • Compare different implementations from a performance point of view, using different frameworks, programming languages, hardware and/or parallelization strategies, while maintaining statistical equivalence among implementations.
  • Test the influence of different pseudo-random number generators (PRNGs) on the statistical accuracy of simulation output.

The model can be implemented using substantially different approaches that ensure statistically equivalent qualitative results. Implementations may differ in aspects such as the selected system architecture, choice of programming language and/or agent-based modeling framework, parallelization strategy, random number generator, and so forth. By comparing distinct PPHPC implementations, valuable insights can be obtained on the computational and algorithmical design of SABMs in general.

netlogo.png

Release Notes

Update article, final published version.

Associated Publications

Fachada N, Lopes VV, Martins RC, Rosa AC. (2015) Towards a standard model for research in agent-based modeling and simulation. PeerJ Computer Science 1:e36 http://dx.doi.org/10.7717/peerj-cs.36

PPHPC - Predator-Prey for High-Performance Computing 1.4.0

PPHPC is a conceptual model which captures important characteristics of spatial agent-based models (SABMs), such as agent movement and local agent interactions. It was designed with several goals in mind:

  • Provide a basis for a tutorial on complete model specification and thorough simulation output analysis.
  • Investigate statistical comparison strategies for model replication.
  • Compare different implementations from a performance point of view, using different frameworks, programming languages, hardware and/or parallelization strategies, while maintaining statistical equivalence among implementations.
  • Test the influence of different pseudo-random number generators (PRNGs) on the statistical accuracy of simulation output.

The model can be implemented using substantially different approaches that ensure statistically equivalent qualitative results. Implementations may differ in aspects such as the selected system architecture, choice of programming language and/or agent-based modeling framework, parallelization strategy, random number generator, and so forth. By comparing distinct PPHPC implementations, valuable insights can be obtained on the computational and algorithmical design of SABMs in general.

Release Notes

Update article, final published version.

Version Submitter First published Last modified Status
1.4.0 Nuno Fachada Wed Nov 25 17:23:09 2015 Sun Feb 18 06:11:17 2018 Published
1.3.0 Nuno Fachada Fri Nov 20 10:37:57 2015 Tue Feb 20 18:42:22 2018 Published
1.2.0 Nuno Fachada Sat Oct 31 14:06:56 2015 Tue Feb 20 18:42:14 2018 Published
1.1.0 Nuno Fachada Sat Aug 8 16:56:07 2015 Tue Feb 20 18:42:35 2018 Published
1.0.0 Nuno Fachada Sat Aug 8 16:27:27 2015 Tue Feb 20 18:42:46 2018 Published Peer Reviewed

Discussion

This website uses cookies and Google Analytics to help us track user engagement and improve our site. If you'd like to know more information about what data we collect and why, please see our data privacy policy. If you continue to use this site, you consent to our use of cookies.
Accept