tetano
Editor, Senior Moderator
AMIA Annu Symp Proc. 2017 Feb 10;2016:534-540. eCollection 2016.
[h=1]Dynamic Multicore Processing for Pandemic Influenza Simulation.[/h] Eriksson H[SUP]1[/SUP], Timpka T[SUP]2[/SUP], Spreco A[SUP]3[/SUP], Dahlstr?m ?[SUP]3[/SUP], Str?mgren M[SUP]4[/SUP], Holm E[SUP]4[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] 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.
PMID: 28269849 PMCID: PMC5333304
[h=1]Dynamic Multicore Processing for Pandemic Influenza Simulation.[/h] Eriksson H[SUP]1[/SUP], Timpka T[SUP]2[/SUP], Spreco A[SUP]3[/SUP], Dahlstr?m ?[SUP]3[/SUP], Str?mgren M[SUP]4[/SUP], Holm E[SUP]4[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] 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.
PMID: 28269849 PMCID: PMC5333304