A. Gyulassy, N. Kotava, M. Kim, C. Hansen, H. Hagen, and V. Pascucci. Direct Feature Visualization Using Morse-Smale Complexes, In IEEE Transactions on Visualization and Computer Graphics, Vol. 18, No. 9, pp. 1549--1562. September, 2012.
S. Kumar, V. Vishwanath, P. Carns, J.A. Levine, R. Latham, G. Scorzelli, H. Kolla, R. Grout, R. Ross, M.E. Papka, J. Chen, V. Pascucci. Efficient data restructuring and aggregation for I/O acceleration in PIDX, In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, IEEE Computer Society Press, pp. 50:1--50:11. 2012.
Hierarchical, multiresolution data representations enable interactive analysis and visualization of large-scale simulations. One promising application of these techniques is to store high performance computing simulation output in a hierarchical Z (HZ) ordering that translates data from a Cartesian coordinate scheme to a one-dimensional array ordered by locality at different resolution levels. However, when the dimensions of the simulation data are not an even power of 2, parallel HZ ordering produces sparse memory and network access patterns that inhibit I/O performance. This work presents a new technique for parallel HZ ordering of simulation datasets that restructures simulation data into large (power of 2) blocks to facilitate efficient I/O aggregation. We perform both weak and strong scaling experiments using the S3D combustion application on both Cray-XE6 (65,536 cores) and IBM Blue Gene/P (131,072 cores) platforms. We demonstrate that data can be written in hierarchical, multiresolution format with performance competitive to that of native data-ordering methods.
A.G. Landge, J.A. Levine, A. Bhatele, K.E. Isaacs, T. Gamblin, S. Langer, M. Schulz, P.-T. Bremer, V. Pascucci. Visualizing Network Traffic to Understand the Performance of Massively Parallel Simulations, In IEEE Transactions on Visualization and Computer Graphics, Vol. 18, No. 12, IEEE, pp. 2467--2476. Dec, 2012.
The performance of massively parallel applications is often heavily impacted by the cost of communication among compute nodes. However, determining how to best use the network is a formidable task, made challenging by the ever increasing size and complexity of modern supercomputers. This paper applies visualization techniques to aid parallel application developers in understanding the network activity by enabling a detailed exploration of the flow of packets through the hardware interconnect. In order to visualize this large and complex data, we employ two linked views of the hardware network. The first is a 2D view, that represents the network structure as one of several simplified planar projections. This view is designed to allow a user to easily identify trends and patterns in the network traffic. The second is a 3D view that augments the 2D view by preserving the physical network topology and providing a context that is familiar to the application developers. Using the massively parallel multi-physics code pF3D as a case study, we demonstrate that our tool provides valuable insight that we use to explain and optimize pF3D’s performance on an IBM Blue Gene/P system.
J.A. Levine, S. Jadhav, H. Bhatia, V. Pascucci, P.-T. Bremer. A Quantized Boundary Representation of 2D Flows, In Computer Graphics Forum, Vol. 31, No. 3 Pt. 1, pp. 945--954. June, 2012.
S. Liu, J.A. Levine, P.-T. Bremer, V. Pascucci. Gaussian Mixture Model Based Volume Visualization, In Proceedings of the IEEE Large-Scale Data Analysis and Visualization Symposium 2012, Note: Received Best Paper Award, pp. 73--77. 2012.
Keywords: Uncertainty Visualization, Volume Rendering, Gaussian Mixture Model, Ensemble Visualization
V. Pascucci, G. Scorzelli, B. Summa, P.-T. Bremer, A. Gyulassy, C. Christensen, S. Philip, S. Kumar. The ViSUS Visualization Framework, In High Performance Visualization: Enabling Extreme-Scale Scientific Insight, Chapman and Hall/CRC Computational Science, Ch. 19, Edited by E. Wes Bethel and Hank Childs (LBNL) and Charles Hansen (UofU), Chapman and Hall/CRC, 2012.
B. Summa, J. Tierny, V. Pascucci. Panorama weaving: fast and flexible seam processing, In ACM Trans. Graph., Vol. 31, No. 4, Note: ACM ID:2335434, ACM, New York, NY, USA pp. 83:1--83:11. July, 2012.
Keywords: digital panoramas, interactive image boundaries, panorama editing, panorama seams
J. Tierny, V. Pascucci. Generalized Topological Simplification of Scalar Fields on Surfaces, In IEEE Transactions on Visualization and Computer Graphics (TVCG), Vol. 18, No. 12, pp. 2005--2013. Dec, 2012.
W. Widanagamaachchi, C. Christensen, P.-T. Bremer, V. Pascucci. Interactive Exploration of Large-scale Time-varying Data using Dynamic Tracking Graphs, In 2012 IEEE Symposium on Large Data Analysis and Visualization (LDAV), pp. 9--17. 2012.
P.C. Wong, H. Shen, V. Pascucci. Extreme-Scale Visual Analytics, In IEEE Computer Graphics and Applications, Vol. 32, No. 4, pp. 23--25. 2012.
Extreme-scale visual analytics (VA) is about applying VA to extreme-scale data. The articles in this special issue examine advances related to extreme-scale VA problems, their analytical and computational challenges, and their real-world applications.
S. Ahern, A. Shoshani, K.L. Ma, A. Choudhary, T. Critchlow, S. Klasky, V. Pascucci. Scientific Discovery at the Exascale: Report from the (DOE) (ASCR) 2011 Workshop on Exascale Data Management, Analysis, and Visualization, Note: Office of Scientific and Technical Information (OSTI), January, 2011.
J.C. Bennett, V. Krishnamoorthy, S. Liu, R.W. Grout, E.R. Hawkes, J.H. Chen, J. Shepherd, V. Pascucci, P.-T. Bremer. Feature-Based Statistical Analysis of Combustion Simulation Data, In IEEE Transactions on Visualization and Computer Graphics, Proceedings of the 2011 IEEE Visualization Conference, Vol. 17, No. 12, pp. 1822--1831. 2011.
H. Bhatia, S. Jadhav, P.-T. Bremer, G. Chen, J.A. Levine, L.G. Nonato, V. Pascucci. Edge Maps: Representing Flow with Bounded Error, In Proceedings of IEEE Pacific Visualization Symposium 2011, Hong Kong, China, Note: Won Best Paper Award!, pp. 75--82. March, 2011.
H. Bhatia, S. Jadhav, P.-T. Bremer, G. Chen, J.A. Levine, L.G. Nonato, V. Pascucci. Flow Visualization with Quantified Spatial and Temporal Errors using Edge Maps, In IEEE Transactions on Visualization and Computer Graphics (TVCG), Vol. 18, No. 9, IEEE Society, pp. 1383--1396. 2011.
A. Cuadros-Vargas, L.G. Nonato, V. Pascucci. Combinatorial Laplacian Image Cloning, In Proceedings of XXIV Sibgrapi – Conference on Graphics, Patterns and Images, pp. 236--241. 2011.
Seamless image cloning has become one of the most important editing operation for photomontage. Recent coordinate-based methods have lessened considerably the computational cost of image cloning, thus enabling interactive applications. However, those techniques still bear severe limitations as to concavities and dynamic shape deformation. In this paper we present novel methodology for image cloning that turns out to be highly efficient in terms of computational times while still being more flexible than existing techniques. Our approach builds on combinatorial Laplacian and fast Cholesky factorization to ensure interactive image manipulation, handling holes, concavities, and dynamic deformations during the cloning process. The provided experimental results show that the proposed technique outperforms existing methods in requisites such as accuracy and flexibility.
T. Etiene, L.G. Nonato, C. Scheidegger, J. Tierny, T.J. Peters, V. Pascucci, R.M. Kirby, C.T. Silva. Topology Verfication for Isosurface Extraction, In IEEE Transactions on Visualization and Computer Graphics, pp. (accepted). 2011.
Attila Gyulassy, J.A. Levine, V. Pascucci. Visualization of Discrete Gradient Construction (Multimedia submission), In Proceedings of the 27th Symposium on Computational Geometry, Paris, France, ACM, pp. 289--290. June, 2011.
S. Jadhav, H. Bhatia, P.-T. Bremer, J.A. Levine, L.G. Nonato, V. Pascucci. Consistent Approximation of Local Flow Behavior for 2D Vector Fields, In Mathematics and Visualization, Springer, pp. 141--159. Nov, 2011.
DOI: 10.1007/978-3-642-23175-9 10
Typically, vector fields are stored as a set of sample vectors at discrete locations. Vector values at unsampled points are defined by interpolating some subset of the known sample values. In this work, we consider two-dimensional domains represented as triangular meshes with samples at all vertices, and vector values on the interior of each triangle are computed by piecewise linear interpolation.
Many of the commonly used techniques for studying properties of the vector field require integration techniques that are prone to inconsistent results. Analysis based on such inconsistent results may lead to incorrect conclusions about the data. For example, vector field visualization techniques integrate the paths of massless particles (streamlines) in the flow or advect a texture using line integral convolution (LIC). Techniques like computation of the topological skeleton of a vector field, require integrating separatrices, which are streamlines that asymptotically bound regions where the flow behaves differently. Since these integrations may lead to compound numerical errors, the computed streamlines may intersect, violating some of their fundamental properties such as being pairwise disjoint. Detecting these computational artifacts to allow further analysis to proceed normally remains a significant challenge.
S. Kumar, V. Vishwanath, P. Carns, B. Summa, G. Scorzelli, V. Pascucci, R. Ross, J. Chen, H. Kolla, R. Grout. PIDX: Efficient Parallel I/O for Multi-resolution Multi-dimensional Scientific Datasets, In Proceedings of The IEEE International Conference on Cluster Computing, pp. 103--111. September, 2011.
Valerio Pascucci, Xavier Tricoche, Hans Hagen, Julien Tierny. Topological Methods in Data Analysis and Visualization: Theory, Algorithms, and Applications (Mathematics and Visualization), Springer, 2011.