Preprint Technical Note Version 2 Preserved in Portico This version is not peer-reviewed

DAPT: A Package Enabling Distributed Automated Parameter Testing

Version 1 : Received: 1 March 2021 / Approved: 2 March 2021 / Online: 2 March 2021 (22:21:47 CET)
Version 2 : Received: 3 May 2021 / Approved: 10 May 2021 / Online: 10 May 2021 (09:47:54 CEST)

A peer-reviewed article of this Preprint also exists.

Journal reference: Gigabyte 2021
DOI: 10.46471/gigabyte.22

Abstract

Modern agent-based models (ABM) and other simulation models require evaluation and testing of many different parameters. Managing that testing for large scale parameter sweeps (grid searches) as well as storing simulation data requires multiple, potentially customizable steps that may vary across simulations. Furthermore, parameter testing, processing, and analysis are slowed if simulation and processing jobs cannot be shared across teammates or computational resources. While high-performance computing (HPC) has become increasingly available, models can often be tested faster through the use of multiple computers and HPC resources. To address these issues, we created the Distributed Automated Parameter Testing (DAPT) Python package. By hosting parameters in an online (and often free) "database", multiple individuals can run parameter sets simultaneously in a distributed fashion, enabling ad hoc crowdsourcing of computational power. Combining this with a flexible, scriptable tool set, teams can evaluate models and assess their underlying hypotheses quickly. Here we describe DAPT and provide an example demonstrating its use.

Subject Areas

DAPT; workflow; agent-based modeling; model exploration; crowdsourcing

Comments (2)

Comment 1
Received: 10 May 2021
Commenter: Paul Macklin
Commenter's Conflict of Interests: Author
Comment: Updated after peer review and other feedback. 
+ Respond to this comment
Comment 2
Received: 8 June 2021
Commenter: Paul Macklin
Commenter's Conflict of Interests: I am an author of this preprint.
Comment: Now published at https://dx.doi.org/10.46471/gigabyte.22
+ Respond to this comment

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 2
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.