ArticleProceedings 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)2020
Experiences in Developing a Distributed Agent-based Modeling Toolkit with Python.
Article in 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), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- Understanding social context in HIV and drug use via community-informed computational modeling: SILOS study protocol.Frontiers in public health · 2025Article
- Agent-Based Model of Combined Community- and Jail-Based Take-Home Naloxone Distribution.JAMA network open · 2024Article
- DISTRIBUTED AGENT-BASED SIMULATION WITH REPAST4PY.Proceedings of the ... Winter Simulation Conference. Winter Simulation Conference · 2022Article
- Application of Distributed Agent-based Modeling to Investigate Opioid Use Outcomes in Justice Involved Populations.IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum : [proceedings]. IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum · 2021Article
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Authors and funding
3 authors.
Funding
Abstract
Distributed agent-based modeling (ABM) on high-performance computing resources provides the promise of capturing unprecedented details of large-scale complex systems. However, the specialized knowledge required for developing such ABMs creates barriers to wider adoption and utilization. Here we present our experiences in developing an initial implementation of Repast4Py, a Python-based distributed ABM toolkit. We build on our experiences in developing ABM toolkits, including Repast for High Performance Computing (Repast HPC), to identify the key elements of a useful distributed ABM toolkit. We leverage the Numba, NumPy, and PyTorch packages and the Python C-API to create a scalable modeling system that can exploit the largest HPC resources and emerging computing architectures.
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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.