Distributed simulation execution on a high-performance cluster using HeuristicLab Hive

Abstract

This paper presents a generic way of distributing simulation executions. The proposed method can be applied on high performance clusters in order to execute any simulation runs simultaneously and gather respective results. These results can then be collected into datasets, which one can use for data analysis and examination of different properties of the implemented simulation model and the underlying simulated process, e.g. production plants or logistics systems. The approach is showcased using a concrete simulation of a production process controlled by different disposition parameters. After executing 30 000 simulation runs, we are able to create respective datasets in order to further analyse the properties of the production process and build surrogate models of the simulation model, which in turn can be used in surrogate-assisted parameter optimization.

Distributed Simulation Execution | High Performance Cluster | Software Architecture | HeuristicLab 

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