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A comparative analysis of parallel processing and super-individual methods for improving the computational performance of a large individual-based model

机译：改进大型个人模型的计算性能的并行处理和超个体方法的比较分析

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Individual-based modelling approaches are being used to simulate larger complex spatial systems in ecology and in other fields of research. Several novel model development issues now face researchers: in particular how to simulate large numbers of individuals with high levels of complexity, given finite computing resources. A case study of a spatially-explicit simulation of aphid population dynamics was used to assess two strategies for coping with a large number of individuals: the use of 'super-individuals' and parallel computing. Parallelisation of the model maintained the model structure and thus the simulation results were comparable to the original model. However, the super-individual implementation of the model caused significant changes to the model dynamics, both spatially and temporally. When super-individuals represented more than around 10 individuals it became evident that aggregate statistics generated from a super-individual model can hide more detailed deviations from an individual-level model. improvements in memory use and model speed were perceived with both approaches. For the parallel approach, significant speed-up was only achieved when more than five processors were used and memory availability was only increased once five or more processors were used. The super-individual approach has potential to improve model speed and memory use dramatically, however this paper cautions the use of this approach for a density-dependent spatially-explicit model, unless individual variability is better taken into account
机译：基于个体的建模方法正被用于模拟生态学和其他研究领域中的大型复杂空间系统。现在，研究人员面临着几种新颖的模型开发问题：特别是在给定的计算资源有限的情况下，如何模拟大量具有高度复杂性的个体。对蚜虫种群动态进行空间明确模拟的案例研究用于评估应对大量个体的两种策略：“超级个体”的使用和并行计算。模型的并行化保持了模型结构，因此仿真结果与原始模型相当。但是，模型的超个性化实现在空间和时间上对模型动力学造成了重大变化。当超个人代表约10个以上的个人时，很明显，从超个人模型生成的汇总统计信息可以隐藏与个人级别模型的更详细的偏差。两种方法都可以改善内存使用和模型速度。对于并行方法，仅当使用五个以上的处理器时，才可以实现显着的加速，并且仅在使用五个以上的处理器时才提高内存可用性。超级个体方法有可能极大地提高模型速度和内存使用率，但是，本文警告说，除非更好地考虑了个体可变性，否则应将这种方法用于依赖密度的空间显式模型

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