Coupling the Uintah Framework and the VisIt Toolkit for Parallel In Situ Data Analysis and Visualization and Computational Steering

  • Allen SandersonEmail author
  • Alan Humphrey
  • John Schmidt
  • Robert Sisneros
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11203)


Data analysis and visualization are an essential part of the scientific discovery process. As HPC simulations have grown, I/O has become a bottleneck, which has required scientists to turn to in situ tools for simulation data exploration. Incorporating additional data, such as runtime performance data, into the analysis or I/O phases of a workflow is routinely avoided for fear of excaberting performance issues. The paper presents how the Uintah Framework, a suite of HPC libraries and applications for simulating complex chemical and physical reactions, was coupled with VisIt, an interactive analysis and visualization toolkit, to allow scientists to perform parallel in situ visualization of simulation and runtime performance data. An additional benefit of the coupling made it possible to create a “simulation dashboard” that allowed for in situ computational steering and visual debugging.


In situ visualization Runtime performance data Visual debugging Computational steering 



This material was based upon work supported by the Department of Energy, National Nuclear Security Administration, under Award Number(s) DE-NA0002375. The authors wish to thank the Uintah and VisIt development groups.

Disclaimer. This report was prepared as an account of work sponsored by an agency of the United States Government. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.


  1. 1.
  2. 2.
  3. 3.
  4. 4.
    Ahern, S., et al.: Scientific discovery at the exascale. Report from the DOE ASCR 2011 Workshop on Exascale. Data Management (2011)Google Scholar
  5. 5.
    Ayachit, U., et al.: ParaView catalyst: enabling in situ data analysis and visualization. In: Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization, pp. 25–29. ACM (2015)Google Scholar
  6. 6.
    Bauer, A.C., et al.: In situ methods, infrastructures, and applications on high performance computing platforms. Comput. Graph. Forum 35, 577–597 (2016)CrossRefGoogle Scholar
  7. 7.
    Brandt, J., et al.: The OVIS analysis architecture. Sandia Report SAND2010-5107, Sandia National Laboratories (2010)Google Scholar
  8. 8.
    Childs, H.: The in situ terminology project. Accessed 06 Apr 2018
  9. 9.
    Childs, H., et al.: VisIt: an end-user tool for visualizing and analyzing very large data. In: High Performance Visualization-Enabling Extreme-Scale Scientific Insight, pp. 357–372. CRC Press, October 2012Google Scholar
  10. 10.
    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. IEEE (2013)Google Scholar
  11. 11.
    Fabian, N., et al.: The ParaView coprocessing library: a scalable, general purpose in situ visualization library. In: 2011 IEEE Symposium on Large Data Analysis and Visualization (LDAV), pp. 89–96. IEEE (2011)Google Scholar
  12. 12.
    Geist, A., Lucas, R.: Major computer science challenges at exascale. Int. J. High Perform. Comput. Appl. 23(4), 427–436 (2009)CrossRefGoogle Scholar
  13. 13.
    Hoisie, A., Getov, V.: Extreme-scale computing - where ‘just more of the same’ does not work. Computer 42(11), 24–26 (2009)CrossRefGoogle Scholar
  14. 14.
    Huck, K.A., Potter, K., Jacobsen, D.W., Childs, H., Malony, A.D.: Linking performance data into scientific visualization tools. In: 2014 First Workshop on Visual Performance Analysis, pp. 50–57 (2014)Google Scholar
  15. 15.
    Isaacs, K.E., Landge, A.G., Gamblin, T., Bremer, P.-T., Pascucci, V., Hamann, B.: Exploring performance data with boxfish. In: 2012 SC Companion High Performance Computing, Networking, Storage and Analysis (SCC), pp. 1380–1381. IEEE (2012)Google Scholar
  16. 16.
    KitWare. ParaView.
  17. 17.
    Kumfert, G., et al.: How the common component architecture advances computational science. J. Phys.: Conf. Ser. 46(1), 479 (2006)Google Scholar
  18. 18.
    Luitjens, J., Berzins, M.: Improving the performance of Uintah: a large-scale adaptive meshing computational framework. In: Proceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2010, pp. 1–10, May 2010Google Scholar
  19. 19.
    Ma, K.-L.: In situ visualization at extreme scale: challenges and opportunities. IEEE Comput. Graph. Appl. 29(6), 14–19 (2009)CrossRefGoogle Scholar
  20. 20.
    Massie, M.L., Chun, B.N., Culler, D.E.: The ganglia distributed monitoring system: design, implementation, and experience. Parallel Comput. 30(7), 817–840 (2004)CrossRefGoogle Scholar
  21. 21.
    Meng, Q., Berzins, M.: Scalable large-scale fluid-structure interaction solvers in the Uintah framework via hybrid task-based parallelism algorithms. Concurr. Comput.: Pract. Exp. 26, 1388–1407 (2014)CrossRefGoogle Scholar
  22. 22.
    Meng, Q., Luitjens, J., Berzins, M.: Dynamic task scheduling for the Uintah framework. In: 2010 3rd Workshop on Many-Task Computing on Grids and Supercomputers, pp. 1–10 (2010)Google Scholar
  23. 23.
    Mulder, J.D., van Wijk, J.J., van Liere, R.: A survey of computational steering environments. Future Gener. Comput. Syst. 15(1), 119–129 (1999)CrossRefGoogle Scholar
  24. 24.
    Nord, H., Chambe-Eng, E.: The Qt company. Accessed 06 Apr 2018
  25. 25.
    Padron, O., Semeraro, D.: TorusVis: a topology data visualization tool (2014)Google Scholar
  26. 26.
    Rathmann, U., Wilgen, J.: Qwt user’s guide. Accessed 06 Apr 2018
  27. 27.
    Sisneros, R., Chadalavada, K.: Toward understanding congestion protection events on blue waters via visual analytics. In: Proceedings of the Cray User Group meeting (2014)Google Scholar
  28. 28.
    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 ParallelGraphics and Visualization, EGPGV 2011, Aire-la-Ville, Switzerland, pp. 101–109. Eurographics Association (2011)Google Scholar
  29. 29.
    Wood, C., Larsen, M., Gimenez, A., Harrison, C., Gamblin, T., Malony, A.: Projecting performance data over simulation geometry using SOSflow and ALPINE. In: 2017 Forth Workshop on Visual Performance Analysis, pp. 1–8, November 2017Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Allen Sanderson
    • 1
    Email author
  • Alan Humphrey
    • 1
  • John Schmidt
    • 1
  • Robert Sisneros
    • 2
  1. 1.Scientific Imaging and Computing InstituteUniversity of UtahSalt Lake CityUSA
  2. 2.National Center for Supercomputing ApplicationsUniversity of Illinois at Urbana-ChampaignUrbanaUSA

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