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Dynamic Multicore Processing for Pandemic Influenza Simulation.
Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Health and Developmental Care, Center for Public Health. Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Medicine and Health Sciences.
Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences.
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2016 (English)In: AMIA Annual Symposium Proceedings, American Medical Informatics Association , 2016, Vol. 2016, 534-540 p.Conference paper, Published paper (Refereed)
Abstract [en]

Pandemic simulation is a useful tool for analyzing outbreaks and exploring the impact of variations in disease, population, and intervention models. Unfortunately, this type of simulation can be quite time-consuming especially for large models and significant outbreaks, which makes it difficult to run the simulations interactively and to use simulation for decision support during ongoing outbreaks. Improved run-time performance enables new applications of pandemic simulations, and can potentially allow decision makers to explore different scenarios and intervention effects. Parallelization of infection-probability calculations and multicore architectures can take advantage of modern processors to achieve significant run-time performance improvements. However, because of the varying computational load during each simulation run, which originates from the changing number of infectious persons during the outbreak, it is not useful to us the same multicore setup during the simulation run. The best performance can be achieved by dynamically changing the use of the available processor cores to balance the overhead of multithreading with the performance gains of parallelization.

Place, publisher, year, edition, pages
American Medical Informatics Association , 2016. Vol. 2016, 534-540 p.
Series
AMIA Annual Symposium Proceedings, ISSN 1559-4076, E-ISSN 1942-597X
National Category
Computer and Information Science Infectious Medicine
Identifiers
URN: urn:nbn:se:liu:diva-137039PubMedID: 28269849OAI: oai:DiVA.org:liu-137039DiVA: diva2:1092270
Conference
AMIA 2016 Annual Symposium November 12-16, 2016, Chicago,IL
Available from: 2017-05-02 Created: 2017-05-02 Last updated: 2017-05-02

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Eriksson, HenrikTimpka, ToomasSpreco, ArminDahlström, Örjan
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Human-Centered systemsFaculty of Science & EngineeringDivision of Community MedicineFaculty of Medicine and Health SciencesCenter for Public HealthDepartment of Medical and Health Sciences
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CiteExportLink to record
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Citation style
  • apa
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