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Water-Level scheduling for parallel tasks in compute-intensive application components

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Abstract

The development of complex simulations with high computational demands often requires an efficient parallel execution of a large number of numerical simulation tasks. Exploiting heterogeneous compute resources for the execution of parallel tasks can be achieved by integrating dedicated scheduling methods into the complex simulation code. In this article, the efforts for developing an application from the area of engineering optimization consisting of various individual components are described. Several scheduling methods for distributing parallel simulation tasks among heterogeneous compute nodes are presented. Performance results and comparisons are shown for two novel scheduling methods and several existing scheduling algorithms for parallel tasks. A heterogeneous compute cluster is used to demonstrate the scheduling and execution of benchmark tasks and FEM simulation tasks.

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  1. MERGE Technologies for Multifunctional Lightweight Structures, http://www.tu-chemnitz.de/merge.

References

  1. Arabnejad H, Barbosa J (2014) List scheduling algorithm for heterogeneous systems by an optimistic cost table. Trans Parallel Distrib Syst 25(3):682–694

    Article  Google Scholar 

  2. Bansal S, Kumar P, Singh K (2006) An improved two-step algorithm for task and data parallel scheduling in distributed memory machines. Parallel Comput 32(10):759–774

    Article  MathSciNet  Google Scholar 

  3. Belkhale K, Banerjee P (1990) An approximate algorithm for the partitionable independent task scheduling problem. In: Proceedings of the 1990 International Conference on Parallel Processing, (ICPP’90), pp 72–75

  4. Bernholdt D, Allan B, Armstrong R, Bertrand F, Chiu K, Dahlgren T, Damevski K, Elwasif W, Epperly T, Govindaraju M, Katz D, Kohl J, Krishnan M, Kumfert G, Larson J, Lefantzi S, Lewis M, Malony A, Mclnnes L, Nieplocha J, Norris B, Parker S, Ray J, Shende S, Windus T, Zhou S (2006) A component architecture for high-performance scientific computing. Int J High Perform Comput Appl 20(2):163–202

    Article  Google Scholar 

  5. Beuchler S, Meyer A, Pester M (2001) SPC-PM3AdH v1.0—Programmer’s manual. Preprint SFB/393 01-08, TU-Chemnitz

  6. Bongo LA, Ciegis R, Frasheri N, Gong J, Kimovski D, Kropf P, Margenov S, Mihajlovic M, Neytcheva M, Rauber T, Rünger G, Trobec R, Wuyts R, Wyrzykowski R (2015) Applications for ultrascale computing. Supercomput Front Innov 2(1):19–48

    Google Scholar 

  7. Dümmler J, Kunis R, Rünger G (2007) A comparison of scheduling algorithms for multiprocessortasks with precedence constraints. In: Proceedings of the High Performance Computing & Simulation Conference (HPCS’07), pp 663–669. ECMS

  8. Henderson Squillacote A (2008) The ParaView guide: a parallel visualization application. Kitware, New York

    Google Scholar 

  9. Hofmann M, Ospald F, Schmidt H, Springer R (2014) Programming support for the flexible coupling of distributed software components for scientific simulations. In: Proceedings of the 9th International Conference on Software Engineering and Applications (ICSOFT-EA 2014), pp 506–511. SciTePress, Setúbal

  10. Hofmann M, Rünger G (2015) Sustainability through flexibility: Building complex simulation programs for distributed computing systems. In: Simulation Modelling Practice and Theory, Special Issue on Techniques And Applications For Sustainable Ultrascale Computing Systems, 58(1):65–78

  11. Jasak H, Jemcov A, Tukovic Z (2007) OpenFOAM: A C++ library for complex physics simulations. In: Proc. of the Int. Workshop on Coupled Methods in Numerical Dynamics (CMND’07), pp 1–20

  12. Kleijnen J, van Beers W, van Nieuwenhuyse I (2012) Expected improvement in efficient global optimization through bootstrapped kriging. J Global Optim 54(1):59–73

    Article  MathSciNet  MATH  Google Scholar 

  13. Leung J (ed) (2004) Handbook of scheduling: algorithms, models, and performance analysis. CRC Press, Boca Raton

    MATH  Google Scholar 

  14. Montgomery D (2001) Design and analysis of experiments, 5th edn. Wiley, New York

    Google Scholar 

  15. N’Takpé T, Suter F (2006) Critical path and area based scheduling of parallel task graphs on heterogeneous platforms. In: Proc. of the 12th Int. Conf. on Parallel and Distributed Systems (ICPADS’06), pp 1–8. IEEE

  16. OpenBLAS: An optimized BLAS library. http://www.openblas.net/. Accessed 3 Dec 2015

  17. Parker S (2006) A component-based architecture for parallel multi-physics PDE simulation. Future Gener Comput Syst 22(1–2):204–216

    Article  Google Scholar 

  18. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Re 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  19. Pinedo M (2012) Scheduling: theory, algorithms, and systems. Springer, New York

    Book  MATH  Google Scholar 

  20. pyDOE: Design of experiments for Python. http://pythonhosted.org/pyDOE. Accessed 3 Dec 2015

  21. Radulescu A, Nicolescu C, van Gemund A, Jonker P (2001) CPR: Mixed task and data parallel scheduling for distributed systems. In: Proc. of the 15th Int. Parallel and Distributed Processing Symposium (IPDPS’01). IEEE, pp 1–8

  22. Radulescu A, van Gemund A (2001) A low-cost approach towards mixed task and data parallel scheduling. In: Proc. of the Int. Conf. on Parallel Processing (ICPP’01). IEEE, pp 69–76

  23. Rauber T, Rünger G (1999) Compiler support for task scheduling in hierarchical execution models. J Syst Archit 45(6–7):483–503

    Article  Google Scholar 

  24. Roux W, Stander N, Haftka R (1998) Response surface approximations for structural optimization. Int J Numer Methods Eng 42(3):517–534

    Article  MATH  Google Scholar 

  25. Sacks J, Welch W, Mitchell T, Wynn H (1989) Design and analysis of computer experiments. Stat Sci 4(4):409–423

    Article  MathSciNet  MATH  Google Scholar 

  26. Schroeder W, Martin K, Lorensen B (2006) The Visualization Toolkit: an object-oriented approach to 3D graphics. Kitware, New York

    Google Scholar 

  27. Suter F (2007) Scheduling \(\Delta \)-critical tasks in mixed-parallel applications on a national grid. In: Proc. of the 8th IEEE/ACM Int. Conf. on Grid Computing. IEEE, pp 2–9

  28. Topcuoglu H, Hariri S, Wu MY (1999) Task scheduling algorithms for heterogeneous processors. In: Proc. of the 8th Heterogeneous Computing Workshop (HCW’99). IEEE, pp 3–14

  29. Turek J, Wolf J, Yu P (1992) Approximate algorithms scheduling parallelizable tasks. In: Proc. of the 4th Annual ACM Symposium on Parallel Algorithms and Architectures (SPAA’92). ACM, pp 323–332

  30. van der Walt S, Colbert S, Varoquaux G (2011) The NumPy array: a structure for efficient numerical computation. Comput Sci Eng 13(2):22–30

    Article  Google Scholar 

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Acknowledgments

This work was performed within the Federal Cluster of Excellence EXC 1075 “MERGE Technologies for Multifunctional Lightweight Structures” and supported by the German Research Foundation (DFG). Financial support is gratefully acknowledged.

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Authors and Affiliations

  1. Department of Computer Science, Technische Universität Chemnitz, Chemnitz, Germany

    Robert Dietze, Michael Hofmann & Gudula Rünger

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  1. Robert Dietze
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  2. Michael Hofmann
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  3. Gudula Rünger
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Correspondence to Michael Hofmann.

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Dietze, R., Hofmann, M. & Rünger, G. Water-Level scheduling for parallel tasks in compute-intensive application components. J Supercomput 72, 4047–4068 (2016). https://doi.org/10.1007/s11227-016-1711-1

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  • DOI: https://doi.org/10.1007/s11227-016-1711-1

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