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SDAV Publications


Esteban Rangel, Wei-keng Liao, Ankit Agrawal, Alok Choudhary, William Hendrix. “AGORAS: A Fast Algorithm for Estimating Medoids in Large Datasets,” In the Workshop on Computational Optimization, Modeling & Simulation, held in conjunction with the International Conference on Computational Science, San Diego, June, 2016.

Esteban Rangel, Nan Li, Salman Habib, Tom Peterka, Ankit Agrawal, Wei-Keng Liao, Alok Choudhary. “Parallel DTFE Surface Density Field Reconstruction,” In the IEEE International Conference on Cluster Computing, Taipei, Taiwan, Note: Best paper award, September, 2016.

Silvio Rizzi, Mark Hereld, Joseph A. Insley, Preeti Malakar, Michael E. Papka, Thomas Uram, Venkatram Vishwanath. “Coupling LAMMPS and the vl3 Framework for Co-Visualization of Atomistic Simulations,” In High Performance Data Analysis and Visualization (HPDAV) 2016, May, 2016.

Melissa Romanus, Fan Zhang, Tong Jin, Qian Sun, Hoang Bui, Ivan Rodero, Jong Choi, Salomon Janhunen, Robert Hager, Scott Klasky, Choong-Seock Chang, Manish Parashar. “Persistent Data Staging Services for Data Intensive In-Situ Scientific Workflows,” In The 7th International Workshop on Data-intensive Distributed Computing in conjunction with the 25th International ACM Symposium on High Performance Parallel and Distributed Computing(HPDC'16), Kyoto, Japan, Note: To Appear In, June, 2016.

Oliver Ruebel, Burlen Loring, Jean-Luc Vay, David P. Grote, Remi Lehe, Stepan Bulanov, Henri Vincenti,, E. Wes Bethel. “WarpIV: In Situ Visualization and Analysis of Ion Accelerator Simulations,” In IEEE Computer Graphics and Applications, Vol. 36, No. 3, pp. 22-35. may, 2016.
ISSN: 0272-1716
DOI: 10.1109/MCG.2016.62

U. Rüde, K. Willcox, L. C. McInnes, H. De Sterck, G. Biros, H. Bungartz, J. Corones, E. Cramer, J. Crowley, O. Ghattas, M. Gunzburger, M. Hanke, R. Harrison, M. Heroux, J. Hesthaven, P. Jimack, C. Johnson, K. E. Jordan, D. E. Keyes, R. Krause, V. Kumar, S. Mayer, J. Meza, K. M. Mørken, J. T. Oden, L. Petzold, P. Raghavan, S. M. Shontz, A. Trefethen, P. Turner, V. Voevodin, B. Wohlmuth, C. S. Woodward. “Research and Education in Computational Science and Engineering,” Subtitled “Report from a workshop sponsored by the Society for Industrial and Applied Mathematics (SIAM) and the European Exascale Software Initiative (EESI-2),” Aug, 2016.


Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.

Franz Sauer, Yubo Zhang, Weixing Wang, Stephane Ethier, Kwan-Liu Ma. “Visualization Techniques for Studying Large-Scale Flow Fields from Fusion Simulations,” In Computer Science and Engineering, Vol. 18, No. 2, IEEE, pp. 68-77. March, 2016.
DOI: 10.1109/MCSE.2015.107

Min Shih, Silvio Rizzi, Joseph Insley, Thomas Uram, Venkat Vishwanath, Mark Hereld, Michael E. Papka, Kwan-Liu Ma. “Parallel Distributed, GPU-Accelerated, Advanced Lighting Calculations for Large-Scale Volume Visualization,” In IEEE Symposium on Large Data Analysis and Visualization (LDAV) 2016, Note: Best Paper Honorable Mention Award, October, 2016.

Primoz Skraba, Paul Rosen, Bei Wang, Guoning Chen, Harsh Bhatia, Valerio Pascucci. “Critical Point Cancellation in 3D Vector Fields: Robustness and Discussion,” In IEEE Transactions on Visualization & Computer Graphics. Also Best Paper at PacificVis, April, 2016.

Shane Snyder, Philip Carns, Kevin Harms, Robert Ross, Glenn K. Lockwood, Nicholas J. Wright. “Modular HPC I/O Characterization with Darshan,” In Proceedings of 5th Workshop on Extreme-scale Programming Tools (ESPT 2016), 11, 2016.

H. De Sterck, C. Johnson,, L. C. McInnes. “Special Section on Two Themes: CSE Software and Big Data in CSE,” In SIAM J. Sci. Comput, Vol. 38, No. 5, SIAM, pp. S1--S2. 2016.


The 2015 SIAM Conference on Computational Science and Engineering (CSE) was held March 14-18, 2015, in Salt Lake City, Utah. The SIAM Journal on Scientific Computing (SISC) created this special section in association with the CSE15 conference. The special section focuses on two topics that are of significant current interest to CSE researchers: CSE software and big data in CSE.

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Qian Sun, Melissa Romanus, Tong Jin, Hongfeng Yu, Peer-Timo Bremer, Steve Petruzza, Scott Klasky, Manish Parashar. “In-Staging Data Placement for Asynchronous Coupling of Task-Based Scientific Workflows,” In The 2nd International Workshop on Extreme Scale Programming Models and Middleware(ESPM2'16) in conjunction with The International Conference on High Performance Computing, Networking, Storage and Analysis, Utah, USA, Note: Best paper award, Nov, 2016.

Houjun Tang, Suren Byna, Steven Harenberg, Xiaocheng Zou, Wenzhao Zhang, Kesheng Wu, Bin Dong, Oliver Rubel, Kristofer Bouchard, Scott Klasky, Nagiza Samatova. “Usage Pattern-Driven Dynamic Data Layout Reorganization,” In 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), May, 2016.


As scientific simulations and experiments move toward extremely large scales and generate massive amounts of data, the data access performance of analytic applications becomes crucial. A mismatch often happens between write and read patterns of data accesses, typically resulting in poor read performance. Data layout reorganization has been used to improve the locality of data accesses. However, current data reorganizations are static and focus on generating a single (or set of) optimized layouts that rely on prior knowledge of exact future access patterns. We propose a framework that dynamically recognizes the data usage patterns, replicates the data of interest in multiple reorganized layouts that would benefit common read patterns, and makes runtime decisions on selecting a favorable layout for a given read pattern. This framework supports reading individual elements and chunks of a multi-dimensional array of variables. Our pattern-driven layout selection strategy achieves multi-fold speedups compared to reading from the original dataset.

X. Tong, J. Edwards, C. Chen, H. Shen, C. R. Johnson, P. Wong. “View-Dependent Streamline Deformation and Exploration,” In Transactions on Visualization and Computer Graphics, Vol. 22, No. 7, IEEE, pp. 1788--1801. July, 2016.


Occlusion presents a major challenge in visualizing 3D flow and tensor fields using streamlines. Displaying too many streamlines creates a dense visualization filled with occluded structures, but displaying too few streams risks losing important features. We propose a new streamline exploration approach by visually manipulating the cluttered streamlines by pulling visible layers apart and revealing the hidden structures underneath. This paper presents a customized view-dependent deformation algorithm and an interactive visualization tool to minimize visual clutter in 3D vector and tensor fields. The algorithm is able to maintain the overall integrity of the fields and expose previously hidden structures. Our system supports both mouse and direct-touch interactions to manipulate the viewing perspectives and visualize the streamlines in depth. By using a lens metaphor of different shapes to select the transition zone of the targeted area interactively, the users can move their focus and examine the vector or tensor field freely.

Wathsala Widanagamaachchi, Yarden Livnat, Peer-Timo Bremer, Scott Duvall, Valerio Pascucci. “Interactive Visualization and Exploration of Patient Progression in a Hospital Setting,” In Proceedings of the 2016 Workshop on Visual Analytics in Healthcare, 2016.

Zheng Yuan, William Hendrix, Seung Woo Son, Christoph Federrath, An kit Agrawal, Wei-keng Liao, Alok Choudhary. “Parallel Implementation of Lossy Data Compression for Temporal Data Sets,” In the 23rd International Conference on High Performance Computing, Hyderabad, India, December, 2016.

Dawid Zawislak, William Allcock, Joseph Insley, Michael E. Papka, Silvio Rizzi, Brian Toonen. “Early Investigations Into Using a Remote RAM Pool with the vl3 Visualization Framework,” In In Situ Infrastructures for Enabling Extreme-scale Analysis and Visualization (ISAV) 2016, Salt Lake City, UT, November, 2016.

Wenzhao Zhang, Houjun Tang, Steven Harenberg, Suren Byna, Xiaocheng Zou, Dharshi Devendran, Daniel Martin, Kesheng Wu, Bin Dong, Scott Klasky, Nagiza Samatova. “AMRZone: A Runtime AMR Data Sharing Framework For Scientific Applications,” In 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), May, 2016.


Abstract—Frameworks that facilitate runtime data sharing across multiple applications are of great importance for scientific data analytics. Although existing frameworks work well over uniform mesh data, they can not effectively handle adaptive mesh refinement (AMR) data. Among the challenges to construct an AMR-capable framework include: (1) designing an architecture that facilitates online AMR data management; (2) achieving a load-balanced AMR data distribution for the data staging space at runtime; and (3) building an effective online index to support the unique spatial data retrieval requirements for AMR data. Towards addressing these challenges to support runtime AMR data sharing across scientific applications, we present the AMRZone framework. Experiments over real-world AMR datasets demonstrate AMRZone’s effectiveness at achieving a balanced workload distribution, reading/writing large-scale datasets with thousands of parallel processes, and satisfying queries with spatial constraints. Moreover, AMRZone’s performance and scalability are even comparable with existing state-of-the-art work when tested over uniform mesh data with up to 16384 cores; in the best case, our framework achieves a 46% performance improvement.

Yubo Zhang, Kwan-Liu Ma. “Decoupled Shading for Real-time Heterogeneous Volume Illumination,” In Proceedings of EuroVis 2016 (to appear), 2016.

Xiaocheng Zou, David Boyuka, Dhara Desai, Daniel Martin, Suren Byna, Kesheng Wu, Kushal Bansal, Bin Dong, Wenzhao Zhang, Houjun Tang, Dharshi Devendran, David Trebotich, Scott Klasky, Hans Johansen, Nagiza Samatova. “AMR-aware In Situ Indexing and Scalable Querying,” In The 24th High Performance Computing Symposium (HPC), April, 2016.


Query-driven analytics on scientific datasets is one of fundamental approaches for scientific discoveries. Existing studies have explored query-driven analytics on uniform resolution meshes. However, querying on adaptive mesh refinement (AMR) data has not been explored yet. As many simulations have been lately transitioning to AMR, new methods for efficient query-driven analysis on AMR data are needed. In this paper, we present the first work to support scalable AMR-aware analysis. We propose an AMR-aware hybrid index for supporting two common forms (i.e., spatial and value-based query selections) in query-driven analytics. To sustainably support future-scale analysis, we design an in situ (run-time) index building strategy with minimized performance impact to the co-located simulation. Additionally, we develop a parallel post-processing query method with an adaptive workload-balanced strategy. Our evaluation demonstrates the scalability of our in situ indexing and scalable querying methods up to 16,384 and 1,024 cores, respectively, using a Chombo-based benchmark. Compared to non-AMR-aware indexing and querying, we demonstrate up to 12.4x and 500x performance improvement, respectively.