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Optimize data-driven multi-agent simulation for COVID-19 transmission
BACKGROUND: Multi-Agent Simulation is an essential technique for exploring complex systems. In research of contagious diseases, it is widely exploited to analyze their spread mechanisms, especially for preventing COVID-19. Nowadays, transmission dynamics and interventions of COVID-19 have been elabo...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9250248/ https://www.ncbi.nlm.nih.gov/pubmed/35778688 http://dx.doi.org/10.1186/s12859-022-04799-4 |
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author | Jin, Chao Zhang, Hao Yin, Ling Zhang, Yong Feng, Sheng-zhong |
author_facet | Jin, Chao Zhang, Hao Yin, Ling Zhang, Yong Feng, Sheng-zhong |
author_sort | Jin, Chao |
collection | PubMed |
description | BACKGROUND: Multi-Agent Simulation is an essential technique for exploring complex systems. In research of contagious diseases, it is widely exploited to analyze their spread mechanisms, especially for preventing COVID-19. Nowadays, transmission dynamics and interventions of COVID-19 have been elaborately established by this method, but its computation performance is seldomly concerned. As it usually suffers from inadequate CPU utilization and poor data locality, optimizing the performance is challenging and important for real-time analyzing its spreading. RESULTS: This paper explores approaches to optimize multi-agent simulation for COVID-19 disease. The focus of this work is on the algorithm and data structure designs for improving performance, as well as its parallelization strategies. We propose two successive methods to optimize the computation. We construct a case-focused iteration algorithm to improve data locality, and propose a fast data-mapping scheme called hierarchical hash table to accelerate hash operations. As a result, The case-focused method degrades [Formula: see text] cache references and achieves [Formula: see text] speedup. Hierarchical hash table can further boost computation speed by 47%. And parallel implementation with 20 threads on CPU achieves [Formula: see text] speedup consequently. CONCLUSIONS: In this work, we propose optimizations for multi-agent simulation of COVID-19 transmission from aspects of algorithm and data structure. Benefit from improvement of locality and multi-thread implementation, our methods can significantly accelerate the simulation computation. It is promising in supporting real-time prevention of COVID-19 and other infectious diseases in the future. |
format | Online Article Text |
id | pubmed-9250248 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-92502482022-07-03 Optimize data-driven multi-agent simulation for COVID-19 transmission Jin, Chao Zhang, Hao Yin, Ling Zhang, Yong Feng, Sheng-zhong BMC Bioinformatics Research BACKGROUND: Multi-Agent Simulation is an essential technique for exploring complex systems. In research of contagious diseases, it is widely exploited to analyze their spread mechanisms, especially for preventing COVID-19. Nowadays, transmission dynamics and interventions of COVID-19 have been elaborately established by this method, but its computation performance is seldomly concerned. As it usually suffers from inadequate CPU utilization and poor data locality, optimizing the performance is challenging and important for real-time analyzing its spreading. RESULTS: This paper explores approaches to optimize multi-agent simulation for COVID-19 disease. The focus of this work is on the algorithm and data structure designs for improving performance, as well as its parallelization strategies. We propose two successive methods to optimize the computation. We construct a case-focused iteration algorithm to improve data locality, and propose a fast data-mapping scheme called hierarchical hash table to accelerate hash operations. As a result, The case-focused method degrades [Formula: see text] cache references and achieves [Formula: see text] speedup. Hierarchical hash table can further boost computation speed by 47%. And parallel implementation with 20 threads on CPU achieves [Formula: see text] speedup consequently. CONCLUSIONS: In this work, we propose optimizations for multi-agent simulation of COVID-19 transmission from aspects of algorithm and data structure. Benefit from improvement of locality and multi-thread implementation, our methods can significantly accelerate the simulation computation. It is promising in supporting real-time prevention of COVID-19 and other infectious diseases in the future. BioMed Central 2022-07-01 /pmc/articles/PMC9250248/ /pubmed/35778688 http://dx.doi.org/10.1186/s12859-022-04799-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Jin, Chao Zhang, Hao Yin, Ling Zhang, Yong Feng, Sheng-zhong Optimize data-driven multi-agent simulation for COVID-19 transmission |
title | Optimize data-driven multi-agent simulation for COVID-19 transmission |
title_full | Optimize data-driven multi-agent simulation for COVID-19 transmission |
title_fullStr | Optimize data-driven multi-agent simulation for COVID-19 transmission |
title_full_unstemmed | Optimize data-driven multi-agent simulation for COVID-19 transmission |
title_short | Optimize data-driven multi-agent simulation for COVID-19 transmission |
title_sort | optimize data-driven multi-agent simulation for covid-19 transmission |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9250248/ https://www.ncbi.nlm.nih.gov/pubmed/35778688 http://dx.doi.org/10.1186/s12859-022-04799-4 |
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