A. Gyulassy, P.-T. Bremer, R. Grout, H. Kolla, J. Chen,, V. Pascucci. Stability of dissipation elements: A case study in combustion, In Computer Graphics Forum, 2014.
Attila Gyulassy, David Guenther, Joshua A. Levine, Julien Tierny, Valerio Pascucci. Conforming Morse-Smale Complexes, In Trans. of Vis. and Comp. Graphics, Proc. IEEE Visualization, 2014.
Jeff R. Hammond, Andreas Schäfer, Rob Latham. To INT_MAX...and beyond!, Subtitled Exploring large-count support in MPI, In Workshop on Exascale MPI at Supercomputing Conference 2014, Nov, 2014.
Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization, In Mathematics and Visualization, Springer, 2014.C.D. Hansen, M. Chen, C.R. Johnson, A.E. Kaufman, H. Hagen (Eds.).
Heitmann,K.,Habib,S.,Finkel,H.,Frontiere,N.,Pope,A.,Morozov,V.,Rangel,S.,Kovacs,E.,Kwan,J.,Li, N., Rizzi, S., Insley, J., Vishwanath, V., Peterka, T., Daniel, D., Fasel, P., Zagaris, G.. Large Scale Simulations of Sky Surveys, In Computing in Science and Engineering, Sept-Oct, 2014.
Jian Huang, Xuechen Zhang, Greg Eisenhauer, Karsten Schwan, Matthew Wolf, Stephane Ethier, Scott Klasky. Scibox: Online Sharing of Scientific Data via the Cloud, In International Conference on Parallel and Distributed Processing (IPDPS), IEEE, Feb, 2014.
K. A. Huck, K. Potter, D. W. Jacobsen, H. Childs,, A. D. Malony. Linking Performance Data into Scientific Visualization Tools, In 1st Workshop on Visual Performance Analysis (VPA), held in conjuction with SC14, New Orleans, LA, Nov. 2014, 2014.
A. Huebl, D. Pugmire, F. Schmitt, R. Pausch, M. Bussman. Visualizing the Radiation of the Kelvin-Helmholtz Instability, In 7th Triennial Special Issue of the IEEE Images on Plasma Science, In IEEE Images on Plasma Science, April, 2014.
John Jenkins, Xiaocheng Zou, Houjun Tang, Dries Kimpe, Robert Ross,, Nagiza F. Samatova. RADAR: Runtime asymmetric data-access driven scientific data replication, In Proceedings of the 2014 International Supercomputing Conference, 2014.
T. Jin, F. Zhang, Q. Sun, H. Bui, N. Podhorszki, S. Klasky, H. Kolla, J. Chen, R. Hager, C.S. Chang, M. Parashar. Leveraging Deep Memory Hierarchies for Data Staging in Coupled Data Intensive Simulation Workflows, In IEEE Cluster 2014, 2014.
Karimabadi H., Roytershteyn V., Vu H.X., Omelchenko Y.A., Scudder J., Daughton W., Dimmock A., Nykyri K., Wan M., Sibeck D., Tatineni M., Majumdar A., Loring B., Geveci B.. The link between shocks, turbulence, and magnetic reconnection in collisionless plasmas, In Physics of Plasmas, AIP Publishing, 2014.
S. Klasky, Q. Liu, H. Abbasi, N. Podhorszki, J. Chen, Hemanth Kolla. Scaling up Parallel I/O in S3D to 100K cores with ADIOS, In High Performance Parallel I/O, Edited by Prabhat, Q. Koziol, Taylor and Francis, 2014.
S. Kumar, C. Christensen, J. A. Schmidt, P.-T. Bremer, E. Brugger, V. Vishwanath, P. Carns, H. Kolla, R. Grout, J. Chen, M. Berzins, V. Pascucci. Fast Multi-Resolution Reads of Massive Simulation Datasets, In Proc. Int. Supercomputing Conference , pp. 314-330. 2014.
S. Kumar, J. Edwards, P.-T. Bremer, A. Knoll, C. Christensen, V. Vishwanath, P. Carns, J. A. Schmidt, V. Pascucci. Efficient I/O and storage of adaptive resolution data, In Proc. Supercomputing (SC) 2014, 2014.
Sriram Lakshminarasimhan, Xiaocheng Zou, David A Boyuka II, Saurabh V Pendse, John Jenkins, Venkatram Vishwanath, Michael E Papka, Scott Klasky,, Nagiza F Samatova..
Diraq: scalable in situ data-and resource-aware indexing for optimized query performance, In Cluster Computing, Springer,US, pp. 1-19. 2014.
Aaditya G. Landge, Valerio Pascucci, Attila Gyulassy, Janine C. Bennett, Hemanth Kolla, Jacqueline Chen, Peer-Timo Bremer. In-Situ Feature Extraction of Large Scale Combustion Simulations Using Segmented Merge Trees, In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, SC14, 2014.
R. Latham. Parallel-NetCDF, In High Performance Parallel I/O, CRC Press, Taylor and Francis Group, October, 2014.
W. Liao, R. Thakur. MPI-IO, In High Performance Parallel I/O, CRC Press, Taylor and Francis Group, October, 2014.
S. Liu, Bei Wang, P.-T. Bremer, V. Pascucci. Distortion-Guided Structure-Driven Interactive Exploration of High-Dimensional Data, In Computer Graphics Forum (CGF) (Proceedings of EuroVis), Vol. 33, No. 3, pp. 101--110. 2014.
Dimension reduction techniques are essential for feature selection and feature extraction of complex high-dimensional data. These techniques, which construct low-dimensional representations of data, are typically geometrically motivated, computationally efficient and approximately preserve certain structural properties of the data. However, they are often used as black box solutions in data exploration and their results can be difficult to interpret. To assess the quality of these results, quality measures, such as co-ranking [ LV09 ], have been proposed to quantify structural distortions that occur between high-dimensional and low-dimensional data representations. Such measures could be evaluated and visualized point-wise to further highlight erroneous regions [ MLGH13 ]. In this work, we provide an interactive visualization framework for exploring high-dimensional data via its two-dimensional embeddings obtained from dimension reduction, using a rich set of user interactions. We ask the following question: what new insights do we obtain regarding the structure of the data, with interactive manipulations of its embeddings in the visual space? We augment the two-dimensional embeddings with structural abstrac- tions obtained from hierarchical clusterings, to help users navigate and manipulate subsets of the data. We use point-wise distortion measures to highlight interesting regions in the domain, and further to guide our selection of the appropriate level of clusterings that are aligned with the regions of interest. Under the static setting, point-wise distortions indicate the level of structural uncertainty within the embeddings. Under the dynamic setting, on-the-fly updates of point-wise distortions due to data movement and data deletion reflect structural relations among different parts of the data, which may lead to new and valuable insights.
S. Liu, B. Wang, J. Thiagarajan, P.-T. Bremer, V. Pascucci. Multivariate Volume Visualization through Dynamic Projections, In Proc. IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV), 2014.