Extreme-Scale Dynamic Exploration of a Distributed Agent-Based Model With the EMEWS Framework
Abstract
Here, agent-based models (ABMs) integrate multiple scales of behavior and data to produce higher-order dynamic phenomena and are increasingly used in the study of important social complex systems in biomedicine, socio-economics and ecology/resource management. However, the development, validation and use of ABMs is hampered by the need to execute very large numbers of simulations in order to identify their behavioral properties, a challenge accentuated by the computational cost of running realistic, large-scale, potentially distributed ABM simulations. In this paper we describe the Extreme-scale Model Exploration with Swift (EMEWS) framework, which is capable of efficiently composing and executing large ensembles of simulations and other “black box” scientific applications while integrating model exploration (ME) algorithms developed with the use of widely available 3rd-party libraries written in popular languages such as R and Python. EMEWS combines novel stateful tasks with traditional run-to-completion many task computing (MTC) and solves many problems relevant to highperformance workflows, including scaling to very large numbers (millions) of tasks, maintaining state and locality information, and enabling effective multiple-language problem solving. We present the high-level programming model of the EMEWS framework and demonstrate how it is used to integrate an active learning ME algorithm to dynamically and efficiently characterize themore »
- Publication Date:
- Research Org.:
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1839452
- Alternate Identifier(s):
- OSTI ID: 1491833; OSTI ID: 1861099
- Grant/Contract Number:
- AC02-06CH11357
- Resource Type:
- Published Article
- Journal Name:
- IEEE Transactions on Computational Social Systems
- Additional Journal Information:
- Journal Name: IEEE Transactions on Computational Social Systems Journal Volume: 5 Journal Issue: 3; Journal ID: ISSN 2373-7476
- Publisher:
- Institute of Electrical and Electronics Engineers
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; Agent-based modeling; high-performance computing; machine learning; metamodeling; parallel processing
Citation Formats
Ozik, Jonathan, Collier, Nicholson T., Wozniak, Justin M., Macal, Charles M., and An, Gary. Extreme-Scale Dynamic Exploration of a Distributed Agent-Based Model With the EMEWS Framework. United States: N. p., 2018.
Web. doi:10.1109/TCSS.2018.2859189.
Ozik, Jonathan, Collier, Nicholson T., Wozniak, Justin M., Macal, Charles M., & An, Gary. Extreme-Scale Dynamic Exploration of a Distributed Agent-Based Model With the EMEWS Framework. United States. https://doi.org/10.1109/TCSS.2018.2859189
Ozik, Jonathan, Collier, Nicholson T., Wozniak, Justin M., Macal, Charles M., and An, Gary. Sat .
"Extreme-Scale Dynamic Exploration of a Distributed Agent-Based Model With the EMEWS Framework". United States. https://doi.org/10.1109/TCSS.2018.2859189.
@article{osti_1839452,
title = {Extreme-Scale Dynamic Exploration of a Distributed Agent-Based Model With the EMEWS Framework},
author = {Ozik, Jonathan and Collier, Nicholson T. and Wozniak, Justin M. and Macal, Charles M. and An, Gary},
abstractNote = {Here, agent-based models (ABMs) integrate multiple scales of behavior and data to produce higher-order dynamic phenomena and are increasingly used in the study of important social complex systems in biomedicine, socio-economics and ecology/resource management. However, the development, validation and use of ABMs is hampered by the need to execute very large numbers of simulations in order to identify their behavioral properties, a challenge accentuated by the computational cost of running realistic, large-scale, potentially distributed ABM simulations. In this paper we describe the Extreme-scale Model Exploration with Swift (EMEWS) framework, which is capable of efficiently composing and executing large ensembles of simulations and other “black box” scientific applications while integrating model exploration (ME) algorithms developed with the use of widely available 3rd-party libraries written in popular languages such as R and Python. EMEWS combines novel stateful tasks with traditional run-to-completion many task computing (MTC) and solves many problems relevant to highperformance workflows, including scaling to very large numbers (millions) of tasks, maintaining state and locality information, and enabling effective multiple-language problem solving. We present the high-level programming model of the EMEWS framework and demonstrate how it is used to integrate an active learning ME algorithm to dynamically and efficiently characterize the parameter space of a large and complex, distributed Message Passing Interface (MPI) agent-based infectious disease model.},
doi = {10.1109/TCSS.2018.2859189},
journal = {IEEE Transactions on Computational Social Systems},
number = 3,
volume = 5,
place = {United States},
year = {Sat Sep 01 00:00:00 EDT 2018},
month = {Sat Sep 01 00:00:00 EDT 2018}
}
https://doi.org/10.1109/TCSS.2018.2859189
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