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Data Analysis and Visualization

In enabling scientific discovery the DAV group’s efforts lie between the support of domain-specific scientific workflows and direct, scientific contributions to the state of the art and the state of the practice in data analysis and visualization.  

DAV helps craft science-oriented, analytics-based visualizations both terrestrial and cosmic, including visual simulations of wind turbines, aircraft pressure, supernovae and even the magnetohydrodynamics of black holes. This work can involve anything from Blue Waters support to pioneering new visualization techniques, all within the frame of the scientific community.


David Bock

Lead Visualization Programmer, XSEDE ECSS

Mark Van Moer

Senior Visualization Programmer, Blue Waters Visualization

Jonathon Kim

Senior Visualization Programmer, Blue Waters Visualization

Robert Sisneros

Technical Program Manager, Data Analysis and Visualization

Project Highlights

Orbiting Black Hole Magnetohydrodynamics

Magnetohydrodynamic simulation of two orbiting blackholes done on the Blue Waters supercomputer. Visualization focused on the behavior of the field lines emanating from above the poles and how the orbits affected the particle density.

Wind Farm Visualization

Studying the interaction between large wind farms with multiple wind turbines and the atmospheric boundary layer flow.



  • Gregory H. Bauer, Brett Bode, Jeremy Enos, William T. Kramer, Scott Lathrop, Celso L. Mendes, and Robert Sisneros. “Best practices and lessons from deploying and operating a sustained-petascale system: the blue waters experience.” In Best Practices and Lessons from Deploying and Operating a Sustained-Petascale System: The Blue Waters Experience, p. 0. IEEE, 2018.
  • Scott Lathrop, Celso Mendes, Jeremy Enos, Brett Bode, Gregory Bauer, Robert Sisneros, and William Kramer. “Best practices for management and operation of large HPC installations.” Concurrency and Computation: Practice and Experience (2018): e2653.
    Allen Sanderson, Alan Humphrey, John Schmidt, and Robert Sisneros. “Coupling the Uintah Framework and the VisIt Toolkit for Parallel In Situ Data Analysis and Visualization and Computational Steering.”
  • Robert Sisneros, Jonghoon J. Kim, Mohammad Raji, and Kalyana Chadalavada. “How Deep is Your I/O? Toward Practical Large-Scale I/O Optimization via Machine Learning Methods.” In Proceedings of Cray User Group Meeting (CUG-2018). 2018.


  • Mohammad Raji, Alok Hota, Robert Sisneros, Peter Messmer, and Jian Huang. “Photo-Guided Exploration of Volume Data Features.” arXiv preprint arXiv:1710.06815 (2017).
  • Cong Xu, Shane Snyder, Vishwanath Venkatesan, Philip Carns, Omkar Kulkarni, Suren Byna, Robert Sisneros, and Kalyana Chadalavada. “DXT: Darshan eXtended Tracing.” Argonne National Lab.(ANL), Argonne, IL (United States), 2017.
  • Matthieu Dorier, Gabriel Antoniu, Franck Cappello, Marc Snir, Robert Sisneros, Orcun Yildiz, Shadi Ibrahim, Tom Peterka, and Leigh Orf. “Damaris: addressing performance variability in data management for post-petascale simulations.” ACM Transactions on Parallel Computing (TOPC) 3, no. 3 (2016): 15.


  • Robert Sisneros, Mohammad Raji, Mark W. Van Moer, and David Bock. “Chasing Rainbows: A Color-Theoretic Framework for Improving and Preserving Bad Colormaps.” In International Symposium on Visual Computing, pp. 391-402. Springer, Cham, 2016.
  • Matthieu Dorier, Robert Sisneros, Leonardo Bautista Gomez, Tom Peterka, Leigh Orf, Lokman Rahmani, Gabriel Antoniu, and Luc Boug. “Adaptive performance-constrained in situ visualization of atmospheric simulations.” In Cluster Computing (CLUSTER), 2016 IEEE International Conference on, pp. 269-278. IEEE, 2016.
  • Robert Sisneros and David Pugmire. “Tuned to Terrible: A Study of Parallel Particle Advection State of the Practice.” In Parallel and Distributed Processing Symposium Workshops, 2016 IEEE International, pp. 1058-1067. IEEE, 2016.
  • John E.Stone, Peter Messmer, Robert Sisneros, and Klaus Schulten. “High performance molecular visualization: In-situ and parallel rendering with EGL.” In IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum:[proceedings]. IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, vol. 2016, p. 1014. NIH Public Access, 2016.
  • Lokman Rahmani, Matthieu Dorier, Luc Boug, Gabriel Antoniu, Robert Sisneros, and Tom Peterka. “Towards Smart Visualization Framework for Climate Simulations.” (2016).
  • Robert Sisneros. “A classification of parallel I/O toward demystifying HPC I/O best practices.” In Proceedings of Cray User Group Meeting (CUG-2016). 2016.
  • Cong Xu, Suren Byna, Vishwanath Venkatesan, Robert Sisneros, Omkar Kulkarni, Mohamad Chaarawi, and Kalyana Chadalavada. “LIOProf: Exposing Lustre File System Behavior for I/O Middleware.” In Cray User Group Conference. 2016.
  • Matthieu Dorier, Robert Sisneros, Leonardo Bautista-Gomez, Tom Peterka, Leigh Orf, Rob Ross, Lokman Rahmani, Gabriel Antoniu, and Luc Boug. “Performance-Constrained In Situ Visualization of Atmospheric Simulations.” PhD diss., INRIA Rennes-Bretagne Atlantique, 2016.


  • David Bock, Leigh Orf, and Robert Sisneros. “Visualization of a Tornado-producing Thunderstorm: A Study of Visual Representation.” Supercomputing, 2015.
  • Robert Sisneros, “A Feature-first Approach to Clustering for Highlighting Regions of Interest in Scientific Data”, In Proceedings of International Conference on Computational Science (ICCS), Procedia Computer Science, 51C, pp. 2207-2216, 2015.
  • Robert Sisneros, “Visualizing the Big (and Large) Data from an HPC Resource.” In Numerical Modeling of Space Plasma Flows ASTRONUM-2014, vol. 498, p. 240. 2015.
  • Surendra Byna, Robert Sisneros, Kalyana Chadalavada, and Quincey Koziol, “Tuning Parallel I/O on Blue Waters for Writing 10 Trillion Particles”, In Proceedings of Cray User Group Meeting (CUG), 2015.


  • M. Scot Breitenfeld, Kalyana Chadalavada, Robert Sisneros, and Surendra Byna “Recent Progress in Tuning Performance of Large-scale I/O with Parallel HDF5”, In Proceedings of 9th Parallel Data Storage Workshop, SC 2014.
  • B.D. Semeraro, Robert Sisneros, Joshi Fullop, and G.H. Bauer, “It Takes a Village: Monitoring the Blue Waters Supercomputer”, IEEE Cluster Workshop on Monitoring and Analysis for High Performance Computing Systems Plus Applications, 2014.
  • Robert Sisneros, Joshi Fullop, B. David Semeraro, and Greg Bauer, “Ribbons: Enabling the Effective Use of HPC Utilization Data for System Support Staff”, In EuroVis Workshop on Visual Analytics, pp. 13-17. The Eurographics Association, 2014.
    Robert Sisneros and Kalyana Chadalavada, “Toward Understanding Congestion Protection Events on Blue Waters Via Visual Analytics”, In Proceedings of Cray User Group Meeting (CUG), 2014.
  • Joshi Fullop and Robert Sisneros, “A Diagnostic Utility For Analyzing Periods Of Degraded Job Performance”, In Proceedings of Cray User Group Meeting (CUG), 2014.


  • Robert Sisneros, L.G. Orf, G.H. Bryan, “Ultra-high Resolution Simulation of a Downburst-producing Thunderstorm”, Scientific Visualization Showcase, High Performance Computing, Networking, Storage and Analysis (SCC), 2013 SC Companion, 2013.
  • Robert Sisneros and Mark Van Moer, Expanding Plus-Minus for Visual and Statistical Analysis of NBA Box-Score Data, In Proceedings of IEEE Vis Workshop on Sports Data Visualization, 2013.
  • Matthieu Dorier, Robert Sisneros, Tom Peterka, Gabriel Antoniu, and Dave Semeraro, “Damaris/Viz: A Nonintrusive, Adaptable and User-friendly in Situ Visualization Framework”, In Large-Scale Data Analysis and Visualization (LDAV), 2013 IEEE Symposium on, pp. 67-75, IEEE, 2013.
  • Kalyana Chadalavada and Robert Sisneros, “Analysis of the Blue Waters File System Architecture for Application I/O Performance”, In Proceedings of Cray User Group Meeting (CUG), 2013.
  • Robert Sisneros, Jian Huang, George Ostrouchov, Sean Ahern, and Dave Semeraro, “Contrasting Climate Ensembles: A Model-Based Visualization Approach for Analyzing Extreme Events”, In Proceedings of International Conference on Computational Science (ICCS), Procedia Computer Science, 18, pp. 2347-2356, 2013.
  • Jingyuan Wang, Robert Sisneros, and Jian Huang, “Interactive Selection of Multivariate Features in Large Spatiotemporal Data”, In Visualization Symposium (PacificVis), 2013 IEEE Pacific, pp. 145-152, IEEE, 2013.

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