Conference article

Job-Scheduling of Distributed Simulation-Based Optimization with Support for Multi-Level Parallelism

Peter Nordin
Department of Management and Engineering, Linköping University, Sweden

Robert Braun
Department of Management and Engineering, Linköping University, Sweden

Petter Krus
Department of Management and Engineering, Linköping University, Sweden

Download articlehttp://dx.doi.org/10.3384/ecp15119187

Published in: Proceedings of the 56th Conference on Simulation and Modelling (SIMS 56), October, 7-9, 2015, Linköping University, Sweden

Linköping Electronic Conference Proceedings 119:19, p. 187-197

Show more +

Published: 2015-11-25

ISBN: 978-91-7685-900-1

ISSN: 1650-3686 (print), 1650-3740 (online)

Abstract

In many organizations the utilization of available computer power is very low. If it could be harnessed for parallel simulation and optimization, valuable time could be saved. A framework monitoring available computer resources and running distributed simulations is proposed. Users build their models locally, and then let a job scheduler determine how the simulation work should be divided among remote computers providing simulation services. Typical applications include sensitivity analysis, co-simulation and design optimization. The latter is used to demonstrate the framework. Optimizations can be parallelized either across the algorithm or across the model. An algorithm for finding the optimal distribution of the different levels of parallelism is proposed. An initial implementation of the framework, using the Hopsan simulation tool, is presented. Three parallel optimization algorithms have been used to verify the method and a thorough examination of their parallel speed-up is included.

Keywords

job-scheduling; parallelism; distributed simulation; optimization

References

David P Anderson. Public computing: Reconnecting people to science. In Conference on Shared Knowledge and the Web, pages 17–19, 2003.

T. Blochwitz, M. Otter, M. Arnold, C. Bausch, C. Clauß, H. Elmqvist, A. Junghanns, J. Mauss, M. Monteiro, T. Neidhold, D. Neumerkel, H. Olsson, J.-V. Peetz, and S.Wolf. The functional mockup interface for tool independent exchange of simulation models. In 8th International Modelica Conference 2011, Como, Italy, September 2009.

M. J. Box. A new method of constrained optimization and a comparison with other methods. The Computer Journal, 8 (1):42–52, 1965. doi:10.1093/comjnl/8.1.42.

Robert Braun, Peter Nordin, Björn Eriksson, and Petter Krus. High Performance System Simulation Using Multiple Processor Cores. In The Twelfth Scandinavian International Conference On Fluid Power, Tampere, Finland, May 2011.

John E Dennis, Jr and Virginia Torczon. Direct search methods on parallel machines. SIAM Journal on Optimization, 1(4): 448–474, 1991. doi: 10.1137/0801027.

MS Eldred, WE Hart, BD Schimel, and BG van BloemenWaanders. Multilevel parallelism for optimization on MP computers: Theory and experiment. In Proc. 8th AIAA/USAF/-NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, number AIAA-2000-4818, Long Beach, CA, volume 292, pages 294–296, 2000. doi: 10.2514/6.2000-4818.

B. Eriksson, P. Nordin, and P. Krus. Hopsan NG, A C++ Implementation Using The TLM Simulation Technique. In The 51st Conference On Simulation And Modelling, Oulu, Finland, 2010. URL http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-60644.

Robert Fourer, Jun Ma, and Kipp Martin. Optimization services: A framework for distributed optimization. Operations Research, 58(6):1624–1636, 2010.
doi: 10.1287/opre.1100.0880.

Björn Gehlsen and Bernd Page. A framework for distributed simulation optimization. In Proceedings of the 33nd conference on Winter simulation, pages 508–514. IEEE Computer Society, 2001. doi: 10.1109/WSC.2001.977331.

David E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1st edition, 1989. ISBN 0201157675.

J. A. Guin. Modification of the complex method of constrained optimization. Computer Journal, 10(4):416, 1968. ISSN 00104620. doi: 10.1093/comjnl/10.4.416.

Pieter Hintjens. ZeroMQ: Messaging for Many Applications."O’Reilly Media, Inc.", 2013.

Robert Hooke and T. A. Jeeves. “Direct Search” Solution of Numerical and Statistical Problems. J. ACM, 8(2):212–229, April 1961. ISSN 0004-5411. doi: 10.1145/321062.321069.

J. Kennedy and R. Eberhart. Particle swarm optimization. In Neural Networks, 1995. Proceedings., IEEE International Conference on, volume 4, pages 1942–1948 vol.4, 1995.

P. Krus, A. Jansson, J-O. Palmberg, and K. Weddfelt. Distributed simulation of hydromechanical systems. In The Third Bath International Fluid Power Workshop, Bath, England, 1990.

Petter Krus and Johan Ölvander. Optimizing optimization for design optimization. In Design Engineering Technical Conferences and Computers and Information in Engineering Conference,2003. ASME Press, 2003. doi: 10.1115/DETC2003/DAC-48803.

Donghoon Lee and Matthew Wiswall. A parallel implementation of the simplex function minimization routine. Computational Economics, 30(2):171–187, 2007.
doi: 10.1007/s10614-007-9094-2.

J. A. Nelder and R. Mead. A simplex method for function minimization. The Computer Journal, 7(4):308–313, 1965. doi: 10.1093/comjnl/7.4.308.

N. Sadashiv and S.M.D. Kumar. Cluster, grid and cloud computing: A detailed comparison. In Computer Science Education (ICCSE), 2011 6th International Conference on, pages 477–482, Aug 2011. doi: 10.1109/ICCSE.2011.6028683.

Rainer Storn and Kenneth Price. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4):341–359, 1997. doi: 10.1023/A:1008202821328.

Enver Yücesan, Yuh-Chuyn Luo, Chun-Hung Chen, and Insup Lee. Distributed web-based simulation experiments for optimization. Simulation Practice and Theory, 9(1):73–90, 2001. doi: 10.1016/S0928-4869(01)00037-4.

Citations in Crossref