PyDGGA: Distributed GGA for Automatic Configuration

Abstract We present PyDGGA, a Python tool that implements a distributed version of the automatic algorithm configurator GGA, which is a specialized genetic algorithm to find high quality parameters for solvers and algorithms. PyDGGA implements GGA using an event-driven architecture and runs a simulation of future generations of the genetic algorithm to maximize the usage of the available computing resources. Overall, PyDGGA offers a friendly interface to deploy elastic distributed AC scenarios on shared high-performance computing clusters.

Authors

• Carlos Ansótegui
• Josep Pon
• Meinolf Sellmann
• Kevin Tierney

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