Abstract
One key challenge when doing in situ processing is the investment required to add code to numerical simulations needed to take advantage of in situ processing. Such instrumentation code is often specialized, and tailored to a specific in situ method or infrastructure. Then, if a simulation wants to use other in situ tools, each of which has its own bespoke API [4], then the simulation code team will quickly become overwhelmed with having a different set of instrumentation APIs, one per in situ tool or method. In an ideal situation, such instrumentation need happen only once, and then the instrumentation API provides access to a large diversity of tools. In this way, a data producer’s instrumentation need not be modified if the user desires to take advantage of a different set of in situ tools. The SENSEI generic in situ interface addresses this challenge, which means that SENSEI-instrumented codes enjoy the benefit of being able to use a diversity of tools at scale, tools that include Libsim, Catalyst, Ascent, as well as user-defined methods written in C++ or Python. SENSEI has been shown to scale to greater than 1M-way concurrency on HPC platforms, and provides support for a rich and diverse collection of common scientific data models. This chapter presents the key design challenges that enable tool and processing portability at scale, some performance analysis, and example science applications of the methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Ayachit, U., Bauer, A., Duque, E.P.N., Eisenhauer, G., Ferrier, N., Gu, J., Jansen, K.E., Loring, B., Lukić, Z., Menon, S., Morozov, D., O’Leary, P., Ranjan, R., Rasquin, M., Stone, C.P., Vishwanath, V., Weber, G.H., Whitlock, B., Wolf, M., Wu, K.J., Bethel, E.W.: Performance analysis, design considerations, and applications of extreme-scale in situ infrastructures. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC ’16, pp. 79:1–79:12. IEEE Press, Piscataway, NJ, USA (2016). http://dl.acm.org/citation.cfm?id=3014904.3015010
Ayachit, U., Whitlock, B., Wolf, M., Loring, B., Geveci, B., Lonie, D., Bethel, E.W.: The SENSEI generic in situ interface. In: Proceedings of the 2nd Workshop on In Situ Infrastructures for Enabling Extreme-scale Analysis and Visualization, ISAV ’16, pp. 40–44. IEEE Press, Piscataway, NJ, USA (2016). https://doi.org/10.1109/ISAV.2016.13
Berger, M., Colella, P.: Local adaptive mesh refinement for shock hydrodynamics. J. Comput. Phys. 82(1), 64–84 (1989)
Childs, H., Ahern, S., Ahrens, J., Bauer, A.C., Bennett, J., Bethel, E.W., Bremer, P.T., Brugger, E., Cottam, J., Dorier, M., Dutta, S., Favre, J., Fogal, T., Frey, S., Garth, C., Geveci, B., Godoy, W.F., Hansen, C.D., Harrison, C., Hentschel, B., Insley, J., Johnson, C., Klasky, S., Knoll, A., Kress, J., Larsen, M., Lofstead, J., Ma, K.L., Malakar, P., Meredith, J., Moreland, K., Navratil, P., O’Leary, P., Parashar, M., Pascucci, V., Patchett, J., Peterka, T., Petruzza, S., Podhorszki, N., Pugmire, D., Rasquin, M., Rizzi, S., Rogers, D.H., Sane, S., Sauer, F., Sisneros, R., Shen, H.W., Usher, W., Vickery, R., Vishwanath, V., Wald, I., Wang, R., Weberr, G.H., Whitlock, B., Wolf, M., Yu, H., Ziegler, S.B.: A terminology for in situ visualization and analysis systems. Int. J. High Perform. Comput. Appl. 0(0) (2020). https://doi.org/10.1177/1094342020935991
Childs, H., Brugger, E., Whitlock, B., Meredith, J., Ahern, S., Pugmire, D., Biagas, K., Miller, M., Weber, G.H., Krishnan, H., Fogal, T., Sanderson, A., Garth, C., Bethel, E.W., Camp, D., Rübel, O., Durant, M., Favre, J., Navratil, P.: VisIt: an end-user tool for visualizing and analyzing very large data. In: Bethel, E.W., Childs, H., Hansen, C. (eds.) High performance visualization—enabling extreme-scale scientific insight, Chapman & Hall, CRC Computational Science, pp. 357–372. CRC Press/Francis–Taylor Group, Boca Raton, FL, USA (2012). http://www.crcpress.com/product/isbn/9781439875728. LBNL-6320E
David Lonie: vtkArrayDispatch and Related Tools. http://www.vtk.org/doc/nightly/html/VTK-7-1-ArrayDispatch.html. http://www.vtk.org/doc/nightly/html/VTK-7-1-ArrayDispatch.html, last accessed Aug, 2016
Kirby, A.C., Yang, Z., Mavriplis, D.J., Duque, E.P., Whitlock, B.J.: Visualization and data analytics challenges of large-scale high-fidelity numerical simulations of wind energy applications. In: 2018 AIAA Aerospace Sciences Meeting (2018). https://doi.org/10.2514/6.2018-1171. https://arc.aiaa.org/doi/abs/10.2514/6.2018-1171
Kress, J., Larsen, M., Choi, J., Kim, M., Wolf, M., Podhorszki, N., Klasky, S., Childs, H., Pugmire, D.: Comparing the efficiency of in situ visualization paradigms at scale. In: International Conference on High Performance Computing, pp. 99–117. Springer (2019)
Lipsa, D., Geveci, B.: Ghost and blanking (visibility) changes. https://blog.kitware.com/ghost-and-blanking-visibility-changes/ (2015). Last accessed: Aug 2018
Loring, B., Gu, J., Ferrier, N., Rizzi, S., Shudler, S., Kress, J., Logan, J., Wolf, M., Bethel, E.W.: Improving performance of m-to-n processing and data redistribution in in transit analysis and visualization. In: EuroGraphics Symposium on Parallel Graphics and Visualization (EGPGV). Norrköping, Sweden (2020)
Loring, B., Myers, A., Camp, D., Bethel, E.: Python-based in situ analysis and visualization. In: Proceedings of the Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization—ISAV ’18. ACM Press (2018). https://doi.org/10.1145/3281464.3281465
Moreland, K., Sewell, C., Usher, W., Lo, L., Meredith, J., Pugmire, D., Kress, J., Schroots, H., Ma, K.L., Childs, H., Larsen, M., Chen, C.M., Maynard, R., Geveci, B.: VTK-m: accelerating the visualization toolkit for massively threaded architectures. IEEE Computer Graphics and Applications (CG&A) 36(3), 48–58 (2016)
Morozov, D., Lukić, Z.: Master of puppets: cooperative multitasking for in situ processing. In: Proceedings of High-Performance Parallel and Distributed Computing (HPDC) (2016)
Offermans, N., Marin, O., Schanen, M., Gong, J., Fischer, P., Schlatter, P., Obabko, A., Peplinski, A., Hutchinson, M., Merzari, E.: On the strong scaling of the spectral element solver Nek5000 on petascale systems. In: Proceedings of the Exascale Applications and Software Conference 2016, EASC ’16, pp. 5:1–5:10. ACM, New York, NY, USA (2016). https://doi.org/10.1145/2938615.2938617
Shudler, S., Ferrier, N., Insley, J., Papka, M.E., Patel, S., Rizzi, S.: Fast mesh validation in combustion simulations through in-situ visualization. In: Childs, H., Frey, S. (eds.) Eurographics Symposium on Parallel Graphics and Visualization. The Eurographics Association (2019). https://doi.org/10.2312/pgv.20191105
Utkarsh Ayachit: The ParaView Guide: A parallel visualization application. Kitware, Inc. (2015)
Whitlock, B.: Representing ghost data (2012).. http://www.visitusers.org/index.php?title=Representing_ghost_data. Last accessed: June 2020
Acknowledgements
This work was supported by the Director, Office of Science, Office of Advanced Scientific Computing Research, of the U.S. Department of Energy under Contract Nos. DE-AC02-05CH11231 and DE-AC01-06CH11357, through the grant “Scalable Analysis Methods and In Situ Infrastructure for Extreme Scale Knowledge Discovery,” program manager Dr. Laura Biven. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. Argonne National Laboratory’s work was supported by and used the resources of the Argonne Leadership Computing Facility, which is a U.S. Department of Energy, Office of Science User Facility supported under contract DE-AC02-06CH11357.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bethel, E.W. et al. (2022). The SENSEI Generic In Situ Interface: Tool and Processing Portability at Scale. In: Childs, H., Bennett, J.C., Garth, C. (eds) In Situ Visualization for Computational Science. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-81627-8_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-81627-8_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-81626-1
Online ISBN: 978-3-030-81627-8
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)