ArticleIEEE transactions on computational social systems2018
Extreme-scale Dynamic Exploration of a Distributed Agent-based Model with the EMEWS Framework.
Article in IEEE transactions on computational social systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Start from the end: Policy exploration to inform effective and consistent interventions applied to COVID-19 in St. Louis.PNAS nexus · 2026Article
- Hybrid modelling using simulation and machine learning in healthcare.Computers & operations research · 2026Review
- Bayesian Calibration of Stochastic Agent Based Model via Random Forest.Statistics in medicine · 2025Article
- Social network dynamics of tobacco smoking and alcohol use among persons involved with the criminal legal system (PCLS): A modeling study.International journal of alcohol and drug research · 2024Article
- DISTRIBUTED MODEL EXPLORATION WITH EMEWS.Proceedings of the ... Winter Simulation Conference. Winter Simulation Conference · 2024Article
- DISTRIBUTED AGENT-BASED SIMULATION WITH REPAST4PY.Proceedings of the ... Winter Simulation Conference. Winter Simulation Conference · 2022Article
- Characterization and Valuation of the Uncertainty of Calibrated Parameters in Microsimulation Decision Models.Frontiers in physiology · 2022Article
- Editorial: Integration of Machine Learning and Computer Simulation in Solving Complex Physiological and Medical Questions.Frontiers in physiology · 2022Article
- Optimizing Dosage-Specific Treatments in a Multi-Scale Model of a Tumor Growth.Frontiers in molecular biosciences · 2022Article
- Building and experimenting with an agent-based model to study the population-level impact of CommunityRx, a clinic-based community resource referral intervention.PLoS computational biology · 2021Article
- Article
- Distributed in-memory data management for workflow executions.PeerJ. Computer science · 2021Article
- DAPT: A package enabling distributed automated parameter testing.GigaByte (Hong Kong, China) · 2021Article
- Experiences in Developing a Distributed Agent-based Modeling Toolkit with Python.Proceedings of PYHPC 2020 : 9th Workshop on Python for High-Performance and Scientific Computing. Workshop on Python for High-Performance and Scientific Computing (9th : 2020 : Online) · 2020Article
- Learning-accelerated discovery of immune-tumour interactions.Molecular systems design & engineering · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
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 high-performance 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.