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SDAV Publications


C.R. Johnson, K. Potter. “Visualization,” In The Princeton Companion to Applied Mathematics, Edited by Nicholas J. Higham, Princeton University Press, pp. 843-846. September, 2015.
ISBN: 9780691150390

C.R. Johnson. “Visualization,” In Encyclopedia of Applied and Computational Mathematics, Edited by Björn Engquist, Springer, pp. 1537-1546. 2015.
ISBN: 978-3-540-70528-4

Mark Kim, Charles Hansen. “Surface Flow Visualization using the Closest Point Embedding,” In IEEE Pacific Visualization, April, 2015.

Mark Kim, Charles Hansen. “GPU Surface Extraction using the Closest Point Embedding,” In SPIE Visualization and Data Analysis, February, 2015.

SeongJo Kim, Yuanrui Zhang, SeungWoo Son, Mahmut Kandemir, Wei-keng Liao, Rajeev Thakur, Alok Choudhary. “IOPro: a parallel I/O profiling and visualization framework for high-performance storage systems,” In Supercomputing, Vol. 71, No. 3, Springer US, pp. 840-870. March, 2015.

M. Koo, W. Yoo, A. Sim . “I/O Performance Analysis Framework on Measurement Data from Scientific Clusters,” In International Conference for High Performance Computing, Networking, Storage and Analysis (SC'15), ACM Student Research Competition (SRC), November, 2015.

James Kress, Scott Klasky, Norbert Podhorszki, Jong Choi, Hank Childs,, Dave Pugmire. “Loosely Coupled In Situ Visualization: A Perspective on Why it's Here to Stay,” In In Situ infrastructures for Enabling Extreme-scale Analysis and Visualization (ISAV-15), held in conjunction with SC15, November, 2015.

A. Landge, P.-T. Bremer, A. Gyulassy, V. Pascucci. “Notes on the Distributed Computation of Merge Trees on CW-complexes,” In Proc. TopoInVis, May, 2015.

Matthew Larsen, Jeremy Meredith, Paul Navratil, Hank Childs. “Ray-Tracing Within a Data Parallel Framework,” In IEEE Pacific Visualization (PacificVis), April, 2015.

M. Larsen, S. Labasan, P. Navrátil, J.S. Meredith,, H. Childs. “Volume Rendering Via Data-Parallel Primitives,” In Eurographics Symposium on Parallel Graphics and Visualization, May, 2015.

Matthew Larsen, Eric Brugger, Hank Childs, Jim Eliot, Kevin Griffin,, Cyrus Harrison. “Strawman - A Batch In Situ Visualization and Analysis Infrastructure for Multi-Physics Simulation Codes,” In In Situ infrastructures for Enabling Extreme-scale Analysis and Visualization (ISAV-15), held in conjunction with SC15, November, 2015.

Kelvin Li, Jia-Kai Chou, Kwan-Liu Ma. “High Performance Heterogeneous Computing for Collaborative Visual Analysis,” In Proceedings of 2015 Symposium on Visualization in High Performance Computing, co-located with ACM SIGGRAPH Asia 2015 , ACM, pp. 1-4. November, 2015.

Yaxiong Liang, Xu Ji, Hoang Bui, Fan Zhang, Jeremy Logan, Wei Xue, Lizhe Wang, Manish Parashar, Scott Klasky,Weimin Zheng. “TCP Based Data Staging on Supercomputers,” In Supercomputing Frontiers, March, 2015.


Emerging scientific HPC applications running on extreme-scale supercomputers are facing severe I/O challenges. Traditional post-processing workflows involving writing data to shared storage and reading the data back later for analysis are too expensive to support fine-grained or real-time analysis. Thus data staging service is becoming one promising solution, which can avoid the unexpected read and write over remote storage with heavy contention by staging the output data in memory and supporting of coupled application workflow. In this paper, we develop a TCP version of communication substrate for the data staging framework DataSpaces, which allows DataSpaces service to work easily and efficiently on most of today's underlying interconnect networks, even in more generic scenarios, such as cloud and WAN. The details of our system design and implementation are presented along with performance tuning efforts on high-end supercomputers including TianHe-1A. Performance evaluation over two operational supercomputers shows that TCP-based data staging can get acceptable performance and can work well in different network environments.

O. Anatole von Lilienfeld, Raghunathan Ramakrishanan, Matthias Rupp,, Aaron Knoll. “Fourier Series of Atomic Radial Distribution Functions: A Molecular Fingerprint for Machine Learning Models of Quantum Chemical Properties,” In International Journal of Quantum Chemistry, August, 2015.

Shaomeng Li, Kenny Gruchalla, Kristin Potter, John Clyne, Hank Childs. “Evaluating the Efficacy of Wavelet Configurations on Turbulent-Flow Data,” In Proceedings of IEEE Symposium on Large Data Analysis and Visualization, October, 2015.

Xiaotong Liu, Han-Wei Shen. “The Effects of Representation and Juxtaposition on Graphical Perception of Matrix Visualization,” In ACM Computer-Human Interaction (CHI'2015), April, 2015.

Shusen Liu, Bei Wang, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Valerio Pascucci. “Visual Exploration of High-Dimensional Data through Subspace Analysis and Dynamic Projections,” In Eurographics Conference on Visualization (EuroVis), May, 2015.

Ruoqian Liu, Ankit Agrawal, Wei-keng Liao, Alok Choudhary, Zhengzhang Chen. “Pruned Search: A Machine Learning Based Meta-Heuristic Approach for Const rained Continuous Optimization,” In the Eighth International Conference on Contemporary Computing, August, 2015.

Liu, Xiaotong, Shen, Han-Wei, Hu, Yifan. “Supporting multifaceted viewing of word clouds with focus plus context display,” In Information Visualization Journal, Vol. 14, no. 2, pp. 168-180. April, 2015.

Xiaotong Liu, Han-Wei Shen. “Association Analysis for Visual Exploration of Multivariate Scientific Data Sets,” In IEEE Scientific Visualization (SciVis), October, 2015.