Data Exploration at the Exascale

Hank Childs

doi:10.14529/jsfi150301

Authors

Hank Childs University of Oregon, Eugene Lawrence Berkeley National Laboratory, Berkeley

DOI:

https://doi.org/10.14529/jsfi150301

Abstract

In situ processing - i.e., coupling visualization routines to a simulation code to generate images in real-time - is predicted to be the dominant form for visualization on upcoming supercomputers. Unfortunately, traditional in situ techniques are largely incongruent with exploratory visualization, which is an important activity to enable understanding of simulation data. In re- sponse, a new paradigm is emerging: data is transformed and massively reduced in situ and then the resulting form is explored post hoc. The fundamental tension in this approach is between the extent of the data reduction and the loss in integrity in the resulting data. However, new oppor- tunities, in terms of increased access to data, may blunt this tension and allow for both sufficient data reduction and also more accurate analysis. With this paper, we describe the trends behind "data exploration at the exascale" and also summarize some recent results that confirmed that this new paradigm can produce superior results compared to the traditional one.

References

Alexy Agranovsky, David Camp, Christoph Garth, E. Wes Bethel, Kenneth I. Joy, and Hank Childs. Improved Post Hoc Flow Analysis Via Lagrangian Representations. In Proceedings of the IEEE Symposium on Large Data Visualization and Analysis (LDAV), pages 67–75, Paris, France, November 2014. DOI: 10.1109/ldav.2014.7013206.

Sean Ahern, Arie Shoshani, Kwan-Liu Ma, Alok Choudhary, Terence Critchlow, Scott Klasky, Valerio Pascucci, Jim Ahrens, E. Wes Bethel, Hank Childs, Jian Huang, Kenneth I. Joy, Quincey Koziol, Jay Lofstead, Jeremy Meredith, Ken Moreland, George Ostrouchov, Mike Papka, Venkat Vishwanath, Matthew Wold, Nick Wright, and K. John Wu. Scien- tific Discovery at the Exascale: Report for the DOE ASCR Workshop on Exascale Data Management, Analysis, and Visualization, July 2011.

James Ahrens, Sebastien Jourdain, Patrick O’Leary, John Patchett, David H. Rogers, and Mark Petersen. An image-based approach to extreme scale in situ visualization and analysis. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC ’14, pages 424–434, Piscataway, NJ, USA, 2014. IEEE Press. DOI: 10.1109/sc.2014.40.

David M. Butler, James C. Almond, R. Daniel Bergeron, Ken W. Brodlie, and Robert B. Haber. Visualization reference models. In Proceedings of the 4th conference on Visualization ’93, VIS ’93, pages 337–342, Washington, DC, USA, 1993. IEEE Computer Society.

Brian Cabral and Leith Casey Leedom. Imaging vector fields using line integral convolution. In Proceedings of the 20th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’93, pages 263–270, New York, NY, USA, 1993. ACM. DOI: 10.1145/166117.166151.

Hank Childs, Kwan-Liu Ma, Hongfeng Yu, Brad Whitlock, Jeremy Meredith, Jean Favre, Scott Klasky, Norbert Podhorszki, Karsten Schwan, Matthew Wolf, Manish Parashar, and Fan Zhang. In Situ Processing. In High Performance Visualization—Enabling Extreme-Scale Scientific Insight, pages 171–198. October 2012. DOI: 10.1201/b12985-12.

Hank Childs, David Pugmire, Sean Ahern, Brad Whitlock, Mark Howison, Prabhat, Gunther Weber, and E. Wes Bethel. Extreme Scaling of Production Visualization Software on Diverse Architectures. IEEE Computer Graphics and Applications (CG&A), 30(3):22–31, May/June 2010. DOI: 10.1109/mcg.2010.51.

Nathan Fabian, Kenneth Moreland, David Thompson, Andrew Bauer, Pat Marion, Berk Geveci, Michel Rasquin, and Kenneth Jansen. The paraview coprocessing library: A scal- able, general purpose in situ visualization library. In Large Data Analysis and Visualization (LDAV), 2011 IEEE Symposium on, pages 89–96. IEEE, 2011. DOI: 10.1109/ldav.2011.6092322.

G. Haller. Distinguished material surfaces and coherent structures in three-dimensional fluid flows. Physica D: Nonlinear Phenomena, 149(4):248 – 277, 2001. DOI: 10.1016/s0167-2789(00)00199-8.

J. P M Hultquist. Constructing stream surfaces in steady 3d vector fields. In Visualization, 1992. Visualization ’92, Proceedings., IEEE Conference on, pages 171–178, Oct 1992. DOI: 10.1109/visual.1992.235211.

Henry Lehmann and Bernhard Jung. In-situ multi-resolution and temporal data compression for visual exploration of large-scale scientific simulations. In Hank Childs, Renato Pajarola, and Venkatram Vishwanath, editors, 4th IEEE Symposium on Large Data Analysis and Visualization, LDAV 2014, Paris, France, November 9-10, 2014, pages 51–58. IEEE, 2014. DOI: 10.1109/ldav.2014.7013204.

Jay F. Lofstead, Scott Klasky, Karsten Schwan, Norbert Podhorszki, and Chen Jin. Flexible io and integration for scientific codes through the adaptable io system (adios). In Proceedings of the 6th international workshop on Challenges of large applications in distributed enviro nments, CLADE ’08, pages 15–24, New York, NY, USA, 2008. ACM. DOI: 10.1145/1383529.1383533.

Tony McLoughlin, Robert S. Laramee, Ronald Peikert, Frits H. Post, and Min Chen. Over Two Decades of Integration-Based, Geometric Flow Visualization. In EuroGraphics 2009 - State of the Art Reports, pages 73–92, April 2009.

Kenneth Moreland, Ron Oldfield, Pat Marion, Sebastien Jourdain, Norbert Podhorszki, Venkatram Vishwanath, Nathan Fabian, Ciprian Docan, Manish Parashar, Mark Hereld, et al. Examples of in transit visualization. In Proceedings of the 2nd international workshop on Petascal data analytics: challenges and opportunities, pages 1–6. ACM, 2011. DOI: 10.1145/2110205.2110207.

A. Tikhonova, C. Correa, and Kwan-Liu Ma. Visualization by Proxy: A Novel Framework for Deferred Interaction with Volume Data. IEEE Transactions on Visualization and Computer Graphics, 16(6):1551–1559, 2010. DOI: 10.1109/tvcg.2010.215.

A. Tikhonova, Hongfeng Yu, C. Correa, and Kwan-Liu Ma. A Preview and Exploratory Technique for Large Scale Scientific Simulations. In Proceedings of the Eurographics Symposium on Parallel Graphics and Visualization, pages 111–120, 2011.

V. Vishwanath, M. Hereld, and M.E. Papka. Toward simulation-time data analysis and i/o acceleration on leadership-class systems. In Large Data Analysis and Visualization (LDAV), 2011 IEEE Symposium on, pages 9–14, 2011. DOI: 10.1109/ldav.2011.6092178.

Chaoli Wang, Hongfeng Yu, and Kwan-Liu Ma. Importance-Driven Time-Varying Data Visualization. IEEE Transactions on Visualization and Computer Graphics, 14(6):1547– 1554, 2008. DOI: 10.1109/tvcg.2008.140.

Chaoli Wang, Hongfeng Yu, and Kwan-Liu Ma. Application-Driven Compression for Visualizing Large-Scale Time-Varying Data. IEEE Computer Graphics and Applications, 30(1):59– 69, January/February 2010. DOI: 10.1109/mcg.2010.3.

Brad Whitlock, Jean M Favre, and Jeremy S Meredith. Parallel in situ coupling of simulation with a fully featured visualization system. In Proceedings of the 11th Eurographics conference on Parallel Graphics and Visualization, pages 101–109. Eurographics Association, 2011.