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SDAV Publications


Chun-Ming Chen, Ayan Biswas, Han-Wei Shen. “Uncertainty Modeling and Error Reduction for Pathline Computation in Time-varying Flow Fields,” In IEEE Pacific Vis 2015, Hangzhou, China, April, 2015.

Chun-Ming Chen, Soumya Dutta, Xiaotong Liu, Gregory Heinlein, Han-Wei Shen, Jen-Ping Chen. “Visualization and Analysis of Rotating Stall for Transonic Jet Engine Simulation,” In IEEE Scientific Visualization (SciVis), October, 2015.

Jong Choi, Yuan Tian, Gary Liu, Norbert Podhorszki, David Pugmire, Scott Klasky, Eun-Kyu Byun, Soonwook Hwang, Alex Sim, Lingfei Wu, John Wu, Mehmet Aktas, Manish Parashar, Michael Churchill, C.S. Chang, Tahsin Kurc, Xinyan Yan, Matthew Wolf,. “ICEE: Enabling Data Stream Processing For Remote Data Analysis Over Wide Area Networks,” In Supercomputing Frontiers, March, 2015.

Jai Dayal, Jay Lofstead, Greg Eisenhauer, Karsten Schwan, Matthew Wolf,Hasan Abbasi, Scott Klasky. “SODA: Science-driven Orchestration of Data Analytics,” In The 11th International Conference on eScience, September, 2015.

Ewa Deelman, Tom Peterka, others. “The Future of Scientific Workflows: Report of the DOE NGNS/CS Scientific Workflows Workshop,” April, 2015.

Bin Dong, Suren Byna, Kesheng Wu. “Spatially Clustered Join on Heterogeneous Scientific Data Sets,” In 2015 IEEE International Conference on Big Data (IEEE BigData), 2015.

Soumya Dutta, Han-Wei Shen. “Distribution Driven Extraction and Tracking of Features for Time-varying Data Analysis,” In IEEE Scientific Visualization (SciVis), October, 2015.

J. Edwards, E. Daniel, V. Pascucci,, C. Bajaj. “The Generalized Voronoi Diagram of Closely-Spaced Objects,” In Computer Graphics Forum, June, 2015.

Gonzalo A. Bello, Michael Angus, Navya Pedemane, Jitendra K. Harlalka, Fredrick H. M. Semazzi, Vipin Kumar, Nagiza F. Samatova. “Response-Guided Community Detection: Application to Climate Index Discovery,” In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD, September, 2015.

Lucio Grandinetti, Gerhard Joubert, Marcel Kunze, Valerio Pascucci. “Big Data and High Performance Computing,” In Big Data and High Performance Computing, IOS Press, October, 2015.

Pascal Grosset, Manasa Prasad, Cameron Christensen, Aaron Knoll, Charles D. Hansen. “TOD-Tree: Task-Overlapped Direct send Tree Image Compositing for Hybrid MPI Parallelism,” In Eurographics Parallel Graphics and Visualization (EGPGV), May, 2015.

Attila Gyulassy, Aaron Knoll, Kah Chun Lau, Bei Wang, Peer-Timo Bremer, Valerio Pasucci, Michael E. Papka, Larry Curtiss. “Morse-Smale Analysis of Ion Diffusion in Ab Initio Battery Materials Simulations,” In Topology-Based Methods in Visualization (TopoInVis), June, 2015.

Gyulassy, A.; Knoll, A.; Lau, K.; Wang, B.; Bremer, P.; Papka, M.; Curtiss, L.; Pascucci, V.. “Interstitial and Interlayer Ion Diffusion Geometry Extraction in Graphitic Nanosphere Battery Materials,” In IEEE Transactions on Visualization and Computer Graphics, August, 2015.
DOI: 10.1109/TVCG.2015.2467432


Large-scale molecular dynamics (MD) simulations are commonly used for simulating the synthesis and ion diffusion of battery materials. A good battery anode material is determined by its capacity to store ion or other diffusers. However, modeling of ion diffusion dynamics and transport properties at large length and long time scales would be impossible with current MD codes. To analyze the fundamental properties of these materials, therefore, we turn to geometric and topological analysis of their structure. In this paper, we apply a novel technique inspired by discrete Morse theory to the Delaunay triangulation of the simulated geometry of a thermally annealed carbon nanosphere. We utilize our computed structures to drive further geometric analysis to extract the interstitial diffusion structure as a single mesh. Our results provide a new approach to analyze the geometry of the simulated carbon nanosphere, and new insights into the role of carbon defect size and distribution in determining the charge capacity and charge dynamics of these carbon based battery materials.

Salman Habib, Adrian Pope, Hal Finkel, Nicholas Frontiere, Katrin Heitmann, David Daniel, Patricia Fasel, Vitali Morozov, George Zagaris, Tom Peterka, Venkatram Vishwanath, Zarija Lukic, Saba Sehrish, Wei-keng Liao. “HACC: Simulating Sky Surveys on State-of-the-Art Supercomputing Architectures,” In New Astronomy, Vol. 42, pp. 49-65. July, 2015.
DOI: 10.1016/j.newast.2015.06.003

Katrin Heitmann, Nicholas Frontiere, Chris Sewell, Salman Habib, Adrian Pope, Hal Finkel, Silvio Rizzi, Joe Insley, Suman Bhattacharya. “The Q Continuum Simulation: Harnessing the Power of GPU Accelerated Supercomputers,” In Astrophysical Journal Supplement Series, Vol. 219, No. 34, August, 2015.

Dan Huang, Jiangling Yin, Jun Wang, Xuhong Zhang, Jian Zhou, Qing Liu. “SideIO: A Side I/O Framework System for Eliminating Analysis Data Migration,” In Supercomputing Frontiers, March, 2015.

Jie Jiang, Mark Hereld, Joseph A. Insley, Michael E. Papka, Silvio Rizzi, Thomas Uram, Venkatram Vishwanath. “Streaming Ultra High Resolution Images to Large Tiled Display at Nearly Interactive Frame Rates with vl3,” In IEEE Symposium on Large Data Analysis and Visualization (LDAV) - poster, Note: Best Poster Award, October, 2015.

Ye Jin, Xiaosong Ma, Gary Liu, Mingliang Liu, Jeremy Logan, Norbert Podhorszki, Jong Youl Choi,, Scott Klasky. “Combining Phase Identification and Statistical Modeling for Automated Parallel Benchmark Generation,” In ACM SIGMETRICS, June, 2015.

Ye Jin, Mingliang Liu, Xiaosong Ma, Gary Liu, Jeremy S. Logan, Norbert Podhorszki, Jong Youl Choi,, Scott Klasky. “Combining Phase Identification and Statistic Modeling for Automated Parallel Benchmark Generation,” In PPoPP, accepted as a poster, February, 2015.

Tong Jin, Fan Zhang, Qian Sun, Melissa Romanus, , Norbert Podhorszki, Scott Klasky, Hemanth Kolla, Jacqueline Chen, , Robert Hager, Choong-Seock Chang, Manish Parashar . “Exploring Data Staging Across Deep Memory Hierarchies for Coupled Data Intensive Simulation Workflows,” In 29th IEEE International Parallel & Distributed Processing Symposium, May, 2015.


As applications target extreme scales, data staging and in-situ/in-transit data processing have been proposed to address the data challenges and improve scientific discovery. However, further research is necessary in order to understand how growing data sizes from data intensive simulations coupled with the limited DRAM capacity in High End Computing systems will impact the effectiveness of this approach. In this paper, we explore how we can use deep memory levels for data staging, and develop a multi-tiered data staging method that spans both DRAM and solid state disks (SSD). This approach allows us to support both code coupling and data management for data intensive simulation workflows. We also show how an adaptive application-aware data placement mechanism can dynamically manage and optimize data placement across the DRAM and SSD storage levels in this multi-tiered data staging method. We present an experimental evaluation of our approach using two OLCF resources: an Infiniband cluster (Sith) and a Cray XK7 system (Titan), and using combustion (S3D) and fusion (XGC1) simulations