Abstract
The trends in high performance computing, where far more data can be computed that can ever be stored, have made in situ techniques an important area of research and development. Simulation campaigns, where domain scientists work with computer scientists to run a simulation and perform in situ analysis and visualization are important, and complex undertakings. In this paper we report our experiences performing in situ analysis and visualization on two campaigns. The two campaigns were related, but had important differences in terms of the codes that were used, the types of analysis and visualization required, and the visualization tools used. Further, we report the lessons learned from each campaign.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahrens, J., Geveci, B., Law, C.: Visualization in the paraview framework. In: Hansen, C., Johnson, C. (eds.) The Visualization Handbook, pp. 162–170 (2005)
Ainsworth, M., Tugluk, O., Whitney, B., Klasky, S.: MGARD: a multilevel technique for compression of floating-point data. In: DRBSD-2 Workshop at Supercomputing 2017, Colorado, USA (2017)
Ainsworth, M., Tugluk, O., Whitney, B., Klasky, S.: Multilevel techniques for compression and reduction of scientific data-the univariate case. Comput. Vis. Sci. (2017, submitted)
Ayachit, U., et al.: The SENSEI generic in situ interface. In: 2016 Second Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV), pp. 40–44, November 2016. https://doi.org/10.1109/ISAV.2016.013
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)
Bauer, A.C., et al.: In situ methods, infrastructures, and applications on high performance computing platforms, a state-of-the-art (STAR) report. In: Computer Graphics Forum, Proceedings of EuroVis 2016, vol. 35, no. 3, June 2016. LBNL-1005709
Bennett, J.C., et al.: Combining in-situ and in-transit processing to enable extreme-scale scientific analysis. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, SC 2012, pp. 49:1–49:9. IEEE Computer Society Press, Los Alamitos (2012). http://dl.acm.org/citation.cfm?id=2388996.2389063
Chang, C., et al.: Compressed ion temperature gradient turbulence in diverted tokamak edgea. Phys. Plasmas (1994-present) 16(5), 056108 (2009)
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, October 2012
Dayal, J., et al.: Flexpath: type-based publish/subscribe system for large-scale science analytics. In: 2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 246–255. IEEE (2014)
Di, S., Cappello, F.: Fast error-bounded lossy HPC data compression with SZ. In: 2016 IEEE International Parallel and Distributed Processing Symposium, IPDPS 2016, Chicago, IL, USA, 23–27 May 2016, pp. 730–739 (2016)
Docan, C., Parashar, M., Klasky, S.: Dataspaces: an interaction and coordination framework for coupled simulation workflows. Cluster Comput. 15(2), 163–181 (2012)
Dominski, J., et al.: A tight-coupling scheme sharing minimum information across a spatial interface between gyrokinetic turbulence codes. Phys. Plasmas 25(7), 072308 (2018). https://doi.org/10.1063/1.5044707
Dominski, J., Merlo, G., et al.: Gyrokinetic core-edge coupling of the continuum code GENE with the particle-in-cell code XGC (temporary title). (in preparation)
Foster, I., et al.: Computing just what you need: online data analysis and reduction at extreme scales. In: Rivera, F.F., Pena, T.F., Cabaleiro, J.C. (eds.) Euro-Par 2017. LNCS, vol. 10417, pp. 3–19. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64203-1_1
Görler, T., et al.: The global version of the gyrokinetic turbulence code gene. J. Comput. Phys. 230(18), 7053–7071 (2011). https://doi.org/10.1016/j.jcp.2011.05.034. http://www.sciencedirect.com/science/article/pii/S0021999111003457
Liu, Q., et al.: Hello ADIOS: the challenges and lessons of developing leadership class I/O frameworks. Concurrency Comput.: Pract. Exp. 26(7), 1453–1473 (2014). https://doi.org/10.1002/cpe.3125
Moreland, K., et al.: VTK-m: accelerating the visualization toolkit for massively threaded architectures. IEEE Comput. Graph. Appl. 36(3), 48–58 (2016)
Parker, S.G., Johnson, C.R.: SCIRun: a scientific programming environment for computational steering. In: Proceedings of the 1995 ACM/IEEE Conference on Supercomputing, p. 52. ACM (1995)
Shende, S.S., Malony, A.D.: The tau parallel performance system. Int. J. High Perform. Comput. Appl. 20(2), 287–311 (2006)
Tao, D., Di, S., Guo, H., Chen, Z., Cappello, F.: Z-checker: a framework for assessing lossy compression of scientific data. Int. J. High Perform. Comput. Appl. 1094342017737147 (2017). https://doi.org/10.1177/1094342017737147
Tchoua, R., et al.: ADIOS visualization schema: a first step towards improving interdisciplinary collaboration in high performance computing. In: 2013 IEEE 9th International Conference on e-Science, pp. 27–34, October 2013. https://doi.org/10.1109/eScience.2013.24
Whitlock, B., Favre, J., Meredith, J.: 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)
Zhang, F., et al.: In-memory staging and data-centric task placement for coupled scientific simulation workflows. Concurrency Comput.: Pract. Exp. 29(12), e4147 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Kim, M. et al. (2018). In Situ Analysis and Visualization of Fusion Simulations: Lessons Learned. In: Yokota, R., Weiland, M., Shalf, J., Alam, S. (eds) High Performance Computing. ISC High Performance 2018. Lecture Notes in Computer Science(), vol 11203. Springer, Cham. https://doi.org/10.1007/978-3-030-02465-9_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-02465-9_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02464-2
Online ISBN: 978-3-030-02465-9
eBook Packages: Computer ScienceComputer Science (R0)