Railsback, Steven
[VerfasserIn];
Ayllón, Daniel
[VerfasserIn];
Berger, Uta
[VerfasserIn];
Grimm, Volker
[VerfasserIn];
Lytinen, Steven
[VerfasserIn];
Sheppard, Colin
[VerfasserIn];
Thiele, Jan C.
[VerfasserIn]
Improving Execution Speed of Models Implemented in NetLogo
Sie können Bookmarks mittels Listen verwalten, loggen Sie sich dafür bitte in Ihr SLUB Benutzerkonto ein.
Medientyp:
E-Artikel
Titel:
Improving Execution Speed of Models Implemented in NetLogo
Beteiligte:
Railsback, Steven
[VerfasserIn];
Ayllón, Daniel
[VerfasserIn];
Berger, Uta
[VerfasserIn];
Grimm, Volker
[VerfasserIn];
Lytinen, Steven
[VerfasserIn];
Sheppard, Colin
[VerfasserIn];
Thiele, Jan C.
[VerfasserIn]
Anmerkungen:
Hinweis: Link zum Artikel, der zuerst in der Zeitschrift 'The journal of artificial societies and social simulation' erschienen ist. URL: http://dx.doi.org/10.18564/jasss.3282 DOI: 10.18564/jasss.3282 Quelle: The journal of artificial societies and social simulation (2017), 20(1), S. 3. ISSN: 1460-7425. DOI: 10.18564/jasss.3282.
Beschreibung:
NetLogo has become a standard platform for agent-based simulation, yet there appears to be widespread belief that it is not suitable for large and complex models due to slow execution. Our experience does not support that belief. NetLogo programs often do run very slowly when written to minimize code length and maximize clarity, but relatively simple and easily tested changes can almost always produce major increases in execution speed. We recommend a five-step process for quantifying execution speed, identifying slow parts of code, and writing faster code. Avoiding or improving agent filtering statements can often produce dramatic speed improvements. For models with extensive initialization methods, reorganizing the setup procedure can reduce the initialization effort in simulation experiments. Programming the same behavior in a different way can sometimes provide order-of-magnitude speed increases. For models in which most agents do nothing on most time steps, discrete event simulation—facilitated by the time extension to NetLogo—can dramatically increase speed. NetLogo’s BehaviorSpace tool makes it very easy to conduct multiple-model-run experiments in parallel on either desktop or high performance cluster computers, so even quite slow models can be executed thousands of times. NetLogo also is supported by efficient analysis tools, such as BehaviorSearch and RNetLogo, that can reduce the number of model runs and the effort to set them up for (e.g.) parameterization and sensitivity analysis.