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Parallel Computing for Module-Based Computational Experiment

  • Zhuo Yao
  • Dali WangEmail author
  • Danial Riccuito
  • Fengming Yuan
  • Chunsheng Fang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11537)

Abstract

Large-scale scientific code plays an important role in scientific researches. In order to facilitate module and element evaluation in scientific applications, we introduce a unit testing framework and describe the demand for module-based experiment customization. We then develop a parallel version of the unit testing framework to handle long-term simulations with a large amount of data. Specifically, we apply message passing based parallelization and I/O behavior optimization to improve the performance of the unit testing framework and use profiling result to guide the parallel process implementation. Finally, we present a case study on litter decomposition experiment using a standalone module from a large-scale Earth System Model. This case study is also a good demonstration on the scalability, portability, and high-efficiency of the framework.

Keywords

Parallel computing Scientific software Message passing based parallelization Profiling 

Notes

Acknowledgement

Some part of this research is included in Yao’s Ph.D. dissertation (A Kernel Generation Framework for Scientific Legacy Code [12]) with the University of Tennessee, Knoxville. TN. This research was funded by the U.S. Department of Energy, Office of Science, Biological and Environmental Research program (E3SM and TES) and Advanced Scientific Computing Research program. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

References

  1. 1.
    Bonan, G.B., Hartman, M.D., Parton, W.J., Wieder, W.R.: Evaluating litter decomposition in earth system models with long-term litterbag experiments: an example using the community land model version 4 (CLM4). Glob. Change Biol. 19(3), 957–974 (2013)CrossRefGoogle Scholar
  2. 2.
    Brunst, H.: Integrative concepts for scalable distributed performance analysis and visualization of parallel programs. Shaker (2008)Google Scholar
  3. 3.
    Kim, Y., Dennis, J., Kerr, C., Kumar, R.R.P., Simha, A., Baker, A., Mickelson, S.: KGEN: a Python tool for automated Fortran kernel generation and verification. Procedia Comput. Sci. 80, 1450–1460 (2016)CrossRefGoogle Scholar
  4. 4.
    Loh, E.: The ideal HPC programming language. Commun. ACM 53(7), 42–47 (2010)CrossRefGoogle Scholar
  5. 5.
    Oleson, K.W., et al.: Technical description of version 4.0 of the community land model (CLM) (2010).  https://doi.org/10.5065/D6FB50WZ
  6. 6.
    Thornton, P.E., Rosenbloom, N.A.: Ecosystem model spin-up: estimating steady state conditions in a coupled terrestrial carbon and nitrogen cycle model. Ecol. Model. 189(1), 25–48 (2005)CrossRefGoogle Scholar
  7. 7.
    Vardi, M.: Science has only two legs. Commun. ACM 53, 5 (2010)Google Scholar
  8. 8.
    Wang, D., et al.: A scientific function test framework for modular environmental model development: application to the community land model. In: Proceedings of the 2015 International Workshop on Software Engineering for High Performance Computing in Science, pp. 16–23. IEEE Press (2015)Google Scholar
  9. 9.
    Wang, D., Post, W.M., Wilson, B.E.: Climate change modeling: computational opportunities and challenges. Comput. Sci. Eng. 13(5), 36–42 (2011)CrossRefGoogle Scholar
  10. 10.
    Wang, D., Schuchart, J., Janjusic, T., Winkler, F., Xu, Y., Kartsaklis, C.: Toward better understanding of the community land model within the earth system modeling framework. Procedia Comput. Sci. 29, 1515–1524 (2014)CrossRefGoogle Scholar
  11. 11.
    Wang, D., Yuan, F., Hernandez, B., Pei, Y., Yao, C., Steed, C.: Virtual observation system for earth system model: an application to acmeland model simulations. Int. J. Adv. Comput. Sci. Appl. 8(2) (2017).  https://doi.org/10.14569/IJACSA.2017.080223
  12. 12.
    Yao, Z.: A Kernel Generation Framework for Scientific Legacy Code. Ph.D. thesis, University of Tennessee, Knoxville (2018)Google Scholar
  13. 13.
    Yao, Z., Jia, Y., Wang, D., Steed, C., Atchley, S.: In situ data infrastructure for scientific unit testing platform. Procedia Comput. Sci. 80, 587–598 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of TennesseeKnoxvilleUSA
  2. 2.Oak Ridge National LaboratoryOak RidgeUSA
  3. 3.Jilin UniversityChangchunPeople’s Republic of China

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