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Highly Interactive, Steered Scientific Workflows on HPC Systems: Optimizing Design Solutions

  • John R. Ossyra
  • Ada SedovaEmail author
  • Matthew B. Baker
  • Jeremy C. Smith
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11887)

Abstract

Scientific workflows are becoming increasingly important in high performance computing (HPC) settings, as the feasibility and appeal of many simultaneous heterogeneous tasks increases with increasing hardware capabilities. Currently no HPC-based workflow platform supports a dynamically adaptable workflow with interactive steering and analysis at run-time. Furthermore, for most workflow programs, compute resources are fixed for a given instance, resulting in a possible waste of expensive allocation resources when tasks are spawned and killed. Here we describe the design and testing of a run-time-interactive, adaptable, steered workflow tool capable of executing thousands of parallel tasks without an MPI programming model, using a database management system to facilitate task management through multiple live connections. We find that on the Oak Ridge Leadership Computing Facility pre-exascale Summit supercomputer it is possible to launch and interactively steer workflows with thousands of simultaneous tasks with negligible latency. For the case of particle simulation and analysis tasks that run for minutes to hours, this paradigm offers the prospect of a robust and efficient means to perform simulation-space exploration with on-the-fly analysis and adaptation.

Keywords

High performance computing Scientific workflows External steering Adaptable workflows 

Notes

Acknowledgements

An award of computer time was provided by the Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR227525. JCS acknowledges ORNL LDRD funds. The authors would like to thank Oscar Hernandez, Frank Noé and group, Cecilia Clementi and group, and Shantenu Jha and group, for valuable insight and discussions.

References

  1. 1.
    Ailamaki, A., Ioannidis, Y.E., Livny, M.: Scientific workflow management by database management. In: Proceedings of the Tenth International Conference on Scientific and Statistical Database Management (Cat. No. 98TB100243), pp. 190–199. IEEE (1998)Google Scholar
  2. 2.
    Amaro, R.E., et al.: Ensemble docking in drug discovery. Biophys. J. 114, 2271–2278 (2018)CrossRefGoogle Scholar
  3. 3.
    Bernardi, R.C., Melo, M.C., Schulten, K.: Enhanced sampling techniques in molecular dynamics simulations of biological systems. Biochim. Biophys. Acta 1850(5), 872–877 (2015)CrossRefGoogle Scholar
  4. 4.
    Bowman, G.R., Pande, V.S., Noé, F. (eds.): An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation. AEMB, vol. 797. Springer, Dordrecht (2014).  https://doi.org/10.1007/978-94-007-7606-7CrossRefzbMATHGoogle Scholar
  5. 5.
    Buchete, N.V., Hummer, G.: Peptide folding Kinetics from replica exchange molecular dynamics. Phys. Rev. E 77(3), 030902 (2008)CrossRefGoogle Scholar
  6. 6.
    Dorier, M., Wozniak, J.M., Ross, R.: Supporting task-level fault-tolerance in HPC workflows by launching MPI jobs inside MPI jobs. In: Proceedings of the 12th Workshop on Workflows in Support of Large-Scale Science, p. 5. ACM (2017)Google Scholar
  7. 7.
    Eastman, P., et al.: OpenMM 7: rapid development of high performance algorithms for molecular dynamics. PLoS Comput. Biol. 13(7), e1005659 (2017)CrossRefGoogle Scholar
  8. 8.
    Garcia, A.E., Herce, H., Paschek, D.: Simulations of temperature and pressure unfolding of peptides and proteins with replica exchange molecular dynamics. Annu. Rep. Comput. Chem. 2, 83–95 (2006)CrossRefGoogle Scholar
  9. 9.
    Hänggi, P., Talkner, P., Borkovec, M.: Reaction-rate theory: fifty years after Kramers. Rev. Mod. Phys. 62(2), 251 (1990)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Hruska, E., Abella, J.R., Nüske, F., Kavraki, L.E., Clementi, C.: Quantitative comparison of adaptive sampling methods for protein dynamics. J. Chem. Phys. 149(24), 244119 (2018)CrossRefGoogle Scholar
  11. 11.
    Hummer, G.: Position-dependent diffusion coefficients and free energies from Bayesian analysis of equilibrium and replica molecular dynamics simulations. New J. Phys. 7(1), 34 (2005)CrossRefGoogle Scholar
  12. 12.
    Husic, B.E., McGibbon, R.T., Sultan, M.M., Pande, V.S.: Optimized parameter selection reveals trends in Markov state models for protein folding. J. Chem. Phys. 145(19), 194103 (2016)CrossRefGoogle Scholar
  13. 13.
    Jain, A., et al.: FireWorks: a dynamic workflow system designed for high-throughput applications. Concurr. Comput.: Pract. Exp. 27(17), 5037–5059 (2015)CrossRefGoogle Scholar
  14. 14.
    Kasson, P.M., Jha, S.: Adaptive ensemble simulations of biomolecules. Curr. Opin. Struct. Biol. 52, 87–94 (2018)CrossRefGoogle Scholar
  15. 15.
    Kubo, R.: The fluctuation-dissipation theorem. Rep. Prog. Phys. 29(1), 255 (1966)CrossRefGoogle Scholar
  16. 16.
    Kumar, S., Rosenberg, J.M., Bouzida, D., Swendsen, R.H., Kollman, P.A.: The weighted histogram analysis method for free-energy calculations on biomolecules. I. The method. J. Comput. Chem. 13(8), 1011–1021 (1992)CrossRefGoogle Scholar
  17. 17.
    Noé, F., Horenko, I., Schütte, C., Smith, J.C.: Hierarchical analysis of conformational dynamics in biomolecules: transition networks of metastable states. J. Chem. Phys. 126(15), 04B617 (2007)CrossRefGoogle Scholar
  18. 18.
    Ossyra, J.R., Sedova, A., Tharrington, A., Noé, F., Clementi, C., Smith, J.C.: Porting adaptive ensemble molecular dynamics workflows to the summit supercomputer. In: Proceedings of ISC 19; IWOPH. SLNCS (2019, in press)Google Scholar
  19. 19.
    Pérez-Hernández, G., Paul, F., Giorgino, T., De Fabritiis, G., Noé, F.: Identification of slow molecular order parameters for Markov model construction. J. Chem. Phys. 139(1), 07B604\_1 (2013)CrossRefGoogle Scholar
  20. 20.
    Pouya, I., Pronk, S., Lundborg, M., Lindahl, E.: Copernicus, a hybrid dataflow and peer-to-peer scientific computing platform for efficient large-scale ensemble sampling. Future Gener. Comput. Syst. 71, 18–31 (2017)CrossRefGoogle Scholar
  21. 21.
    Prinz, J.H., et al.: Markov models of molecular Kinetics: generation and validation. J. Chem. Phys. 134(17), 174105 (2011)CrossRefGoogle Scholar
  22. 22.
    Scherer, M.K., et al.: PyEMMA 2: a software package for estimation, validation, and analysis of Markov models. J. Chem. Theory Comput. 11(11), 5525–5542 (2015)CrossRefGoogle Scholar
  23. 23.
    da Silva, R.F., Filgueira, R., Pietri, I., Jiang, M., Sakellariou, R., Deelman, E.: A characterization of workflow management systems for extreme-scale applications. Future Gener. Comput. Syst. 75, 228–238 (2017)CrossRefGoogle Scholar
  24. 24.
    Sorin, E.J., Pande, V.S.: Exploring the helix-coil transition via all-atom equilibrium ensemble simulations. Biophys. J. 88(4), 2472–2493 (2005)CrossRefGoogle Scholar
  25. 25.
    Souza, R., Silva, V., Oliveira, D., Valduriez, P., Lima, A.A., Mattoso, M.: Parallel execution of workflows driven by a distributed database management system. In: ACM/IEEE Conference on Supercomputing, Poster (2015)Google Scholar
  26. 26.
    Weinan, E., Ren, W., Vanden-Eijnden, E.: String method for the study of rare events. Phys. Rev. B 66(5), 052301 (2002)Google Scholar
  27. 27.
    Woolf, T.B., Roux, B.: Conformational flexibility of o-phosphorylcholine and o-phosphorylethanolamine: a molecular dynamics study of solvation effects. J. Am. Chem. Soc. 116(13), 5916–5926 (1994)CrossRefGoogle Scholar
  28. 28.
    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: 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, pp. 95–102. IEEE (2013)Google Scholar
  29. 29.
    Wu, H., Paul, F., Wehmeyer, C., Noé, F.: Multiensemble Markov models of molecular thermodynamics and Kinetics. Proc. Natl. Acad. Sci. 113, E3221–E3230 (2016).  https://doi.org/10.1073/pnas.1525092113CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of TennesseeKnoxvilleUSA
  2. 2.Oak Ridge National LaboratoryOak RidgeUSA

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