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Programmable In Situ System for Iterative Workflows

  • Erich Lohrmann
  • Zarija Lukić
  • Dmitriy Morozov
  • Juliane Müller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10773)

Abstract

We describe an in situ system for solving iterative problems. We specifically target inverse problems, where expensive simulations are approximated using a surrogate model. The model explores the parameter space of the simulation through iterative trials, each of which becomes a job managed by a parallel scheduler. Our work extends Henson [1], a cooperative multi-tasking system for in situ execution of loosely coupled codes.

Notes

Acknowledgements

We are grateful to Jack Deslippe for providing us the raw data on Edison queue times. This work was supported by Advanced Scientific Computing Research, Office of Science, U.S. Department of Energy, under Contract DE-AC02-05CH11231, and by the use of resources of the National Energy Research Scientific Computing Center (NERSC).

References

  1. 1.
    Morozov, D., Lukić, Z.: Master of puppets: cooperative multitasking for in situ processing. In: Proceedings of High-Performance Parallel and Distributed Computing, pp. 285–288 (2016)Google Scholar
  2. 2.
    Liu, Q., Logan, J., Tian, Y., Abbasi, H., Podhorszki, N., Choi, J.Y., Klasky, S., Tchoua, R., Lofstead, J., Oldfield, R., Parashar, M., Samatova, N., Schwan, K., Shoshani, A., Wolf, M., Wu, K., Yu, W.: Hello ADIOS: the challenges and lessons of developing leadership class I/O frameworks. Concurr. Comput. Pract. Exp. 26(7), 1453–1473 (2014)CrossRefGoogle Scholar
  3. 3.
    Sun, Q., Jin, T., Romanus, M., Bui, H., Zhang, F., Yu, H., Kolla, H., Klasky, S., Chen, J., Parashar, M.: Adaptive data placement for staging-based coupled scientific workflows. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2015, pp. 65:1–65:12. ACM, New York (2015)Google Scholar
  4. 4.
    Vishwanath, V., Hereld, M., Morozov, V., Papka, M.E.: Topology-aware data movement and staging for I/O acceleration on Blue Gene/P supercomputing systems. In: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2011, pp. 19:1–19:11. ACM, New York (2011)Google Scholar
  5. 5.
    Dorier, M., Sisneros, R., Peterka, T., Antoniu, G., Semeraro, D.: Damaris/Viz: a nonintrusive, adaptable and user-friendly in situ visualization framework. In: 2013 IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV), pp. 67–75, October 2013Google Scholar
  6. 6.
    Bauer, A.C., Geveci, B., Schroeder, W.: The ParaView Catalyst User’s Guide v2.0. Kitware Inc., New York (2015)Google Scholar
  7. 7.
    Whitlock, B., Favre, J.M., Meredith, J.S.: Parallel in situ coupling of simulation with a fully featured visualization system. In: Proceedings of the 11th Eurographics Conference on Parallel Graphics and Visualization, pp. 101–109 (2011)Google Scholar
  8. 8.
    Dorier, M., Dreher, M., Peterka, T., Antoniu, G., Raffin, B., Wozniak, J.M.: Lessons learned from building in situ coupling frameworks. In: First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization, Austin, United States, November 2015Google Scholar
  9. 9.
    Ayachit, U., et al.: Performance analysis, design considerations, and applications of extreme-scale in situ infrastructures. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis (SC) (2016)Google Scholar
  10. 10.
    Viel, M., Becker, G.D., Bolton, J.S., Haehnelt, M.G.: Warm dark matter as a solution to the small scale crisis: new constraints from high redshift Lyman-\(\alpha \) forest data. Phys. Rev. D 88(4), 043502 (2013)CrossRefGoogle Scholar
  11. 11.
    Wozniak, J.M., Armstrong, T.G., Wilde, M., Katz, D.S., Lusk, E., Foster, I.T.: Swift/T: large-scale application composition via distributed-memory dataflow processing. In: IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 95–102 (2013)Google Scholar
  12. 12.
    Booker, A.J., Dennis Jr., J.E., Frank, P.D., Serafini, D.B., Torczon, V., Trosset, M.W.: A rigorous framework for optimization of expensive functions by surrogates. Struct. Multi. Optim. 17, 1–13 (1999)CrossRefGoogle Scholar
  13. 13.
    Gutmann, H.-M.: A radial basis function method for global optimization. J. Global Optim. 19, 201–227 (2001)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Regis, R.G., Shoemaker, C.A.: A stochastic radial basis function method for the global optimization of expensive functions. INFORMS J. Comput. 19, 497–509 (2007)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Müller, J., Shoemaker, C.A.: Influence of ensemble surrogate models and sampling strategy on the solution quality of algorithms for computationally expensive black-box global optimization problems. J. Global Optim. 60, 123–144 (2014)MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Wang, G.G., Shan, S.: Review of metamodeling techniques in support of engineering design optimization. J. Mech. Des. 129, 370–380 (2007)CrossRefGoogle Scholar
  17. 17.
    Dinan, J., Krishnamoorthy, S., Balaji, P., Hammond, J.R., Krishnan, M., Tipparaju, V., Vishnu, A.: Noncollective communicator creation in MPI. In: Cotronis, Y., Danalis, A., Nikolopoulos, D.S., Dongarra, J. (eds.) EuroMPI 2011. LNCS, vol. 6960, pp. 282–291. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-24449-0_32 CrossRefGoogle Scholar
  18. 18.
    Lukić, Z., Stark, C.W., Nugent, P., White, M., Meiksin, A.A., Almgren, A.: The Lyman \(\alpha \) forest in optically thin hydrodynamical simulations. Mon. Not. R. Astron. Soc. 446, 3697–3724 (2015)CrossRefGoogle Scholar
  19. 19.
    Almgren, A.S., Bell, J.B., Lijewski, M.J., Lukić, Z., Van Andel, E.: Nyx: a massively parallel AMR code for computational cosmology. Astrophys. J. 765, 39 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Erich Lohrmann
    • 1
  • Zarija Lukić
    • 2
  • Dmitriy Morozov
    • 2
  • Juliane Müller
    • 2
  1. 1.Georgia Institute of TechnologyAtlantaUSA
  2. 2.Lawrence Berkeley National LaboratoryBerkeleyUSA

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