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RAMBO: Resource-Aware Model-Based Optimization with Scheduling for Heterogeneous Runtimes and a Comparison with Asynchronous Model-Based Optimization

  • Helena Kotthaus
  • Jakob Richter
  • Andreas Lang
  • Janek Thomas
  • Bernd Bischl
  • Peter Marwedel
  • Jörg Rahnenführer
  • Michel Lang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10556)

Abstract

Sequential model-based optimization is a popular technique for global optimization of expensive black-box functions. It uses a regression model to approximate the objective function and iteratively proposes new interesting points. Deviating from the original formulation, it is often indispensable to apply parallelization to speed up the computation. This is usually achieved by evaluating as many points per iteration as there are workers available. However, if runtimes of the objective function are heterogeneous, resources might be wasted by idle workers. Our new knapsack-based scheduling approach aims at increasing the effectiveness of parallel optimization by efficient resource utilization. Derived from an extra regression model we use runtime predictions of point evaluations to efficiently map evaluations to workers and reduce idling. We compare our approach to five established parallelization strategies on a set of continuous functions with heterogeneous runtimes. Our benchmark covers comparisons of synchronous and asynchronous model-based approaches and investigates the scalability.

Keywords

Black-box optimization Model-based optimization Global optimization Resource-aware scheduling Performance management Parallelization 

Notes

Acknowledgments

J. Richter and H. Kotthaus — These authors contributed equally. This work was partly supported by Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Center SFB 876, A3 and by Competence Network for Technical, Scientific High Performance Computing in Bavaria (KONWIHR) in the project “Implementierung und Evaluation eines Verfahrens zur automatischen, massiv-parallelen Modellselektion im Maschinellen Lernen”.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Helena Kotthaus
    • 1
  • Jakob Richter
    • 2
  • Andreas Lang
    • 1
  • Janek Thomas
    • 3
  • Bernd Bischl
    • 3
  • Peter Marwedel
    • 1
  • Jörg Rahnenführer
    • 2
  • Michel Lang
    • 2
  1. 1.Department of Computer Science 12TU Dortmund UniversityDortmundGermany
  2. 2.Department of StatisticsTU Dortmund UniversityDortmundGermany
  3. 3.Department of StatisticsLMU MünchenMunichGermany

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