Abstract
BackgroundInfectious disease modeling and computational power have evolved such that large-scale agent-based models (ABMs) have become feasible. However, the increasing hardware complexity requires adapted software designs to achieve the full potential of current high-performance workstations.ResultsWe have found large performance differences with a discrete-time ABM for close-contact disease transmission due to data locality. Sorting the population according to the social contact clusters reduced simulation time by a factor of two. Data locality and model performance can also be improved by storing person attributes separately instead of using person objects. Next, decreasing the number of operations by sorting people by health status before processing disease transmission has also a large impact on model performance. Depending of the clinical attack rate, target population and computer hardware, the introduction of the sort phase decreased the run time from 26 % up to more than 70 %. We have investigated the application of parallel programming techniques and found that the speedup is significant but it drops quickly with the number of cores. We observed that the effect of scheduling and workload chunk size is model specific and can make a large difference.ConclusionsInvestment in performance optimization of ABM simulator code can lead to significant run time reductions. The key steps are straightforward: the data structure for the population and sorting people on health status before effecting disease propagation. We believe these conclusions to be valid for a wide range of infectious disease ABMs. We recommend that future studies evaluate the impact of data management, algorithmic procedures and parallelization on model performance.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0612-2) contains supplementary material, which is available to authorized users.
Highlights
Infectious disease modeling and computational power have evolved such that large-scale agent-based models (ABMs) have become feasible
ABMs are increasingly used to model infectious disease transmission, but little attention is given in the literature to model implementation and performance, e.g., in [1,2,3,4,5,6,7,8,9,10]
Performance is implementation specific and we compared different close-contact infectious disease simulators starting from two published ABMs for pandemic influenza: FluTE from Chao et al [6] and FRED from Grefenstette et al [10]
Summary
Infectious disease modeling and computational power have evolved such that large-scale agent-based models (ABMs) have become feasible. Performance is implementation specific and we compared different close-contact infectious disease simulators starting from two published ABMs for pandemic influenza: FluTE from Chao et al [6] and FRED (a Framework for Reconstructing Epidemic Dynamics) from Grefenstette et al [10]. Both simulators are written in C++ and are free, open source software (FOSS) under the GNU General Public License and the BSD 3-Clause, respectively.
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