TerraME HPA: parallel simulation of multi-agent systems over SMPs

Abstract

Construction of prognoses about environmental changes demands simulations of massive multi-agent models. This work evaluates the hypothesis that the combined use of techniques such as annotation and bag of tasks can result in flexible and scalable platforms for multi-agent simulation. Although these are well known techniques, most environmental modeling platforms use other approaches to provide high performance computing. In general, the approach used is dependent of the modeling paradigm theses platforms implement. We are looking for approaches that can cope with multiple modeling paradigms. To evaluate our hypothesis, the TerraME modeling platform was extended to run over SMPs (Symmetric Multiprocessors) architectures and used in real case studies. While annotation allows modelers to implement different parallelization strategies without prevent models to run over sequential architectures, the bag of tasks provides load balancing over multiprocessors. The results demonstrated that 35% of linear speedup can be obtained for models with high dependence among tasks, when 8 processors are used. Moreover, for models that have low data or control dependencies, around 90% of linear speedup can be obtained.