Model Exploration Using OpenMOLE - a workflow engine for large scale distributed design of experiments and parameter tuning

TL;DR

OpenMOLE is a workflow engine that simplifies large-scale distributed experiments and parameter tuning, demonstrated through efficient multi-objective optimization of an agent simulation model using genetic algorithms.

Contribution

This work showcases OpenMOLE's ability to easily design and distribute complex optimization experiments on large computing infrastructures.

Findings

Efficiently performs multi-objective optimization on large populations.

Distributes computationally expensive experiments transparently.

Achieves one-hour evaluation of 200,000 individuals on European Grid.

Abstract

OpenMOLE is a scientific workflow engine with a strong emphasis on workload distribution. Workflows are designed using a high level Domain Specific Language (DSL) built on top of Scala. It exposes natural parallelism constructs to easily delegate the workload resulting from a workflow to a wide range of distributed computing environments. In this work, we briefly expose the strong assets of OpenMOLE and demonstrate its efficiency at exploring the parameter set of an agent simulation model. We perform a multi-objective optimisation on this model using computationally expensive Genetic Algorithms (GA). OpenMOLE hides the complexity of designing such an experiment thanks to its DSL, and transparently distributes the optimisation process. The example shows how an initialisation of the GA with a population of 200,000 individuals can be evaluated in one hour on the European Grid Infrastructure.