Xiaotong Liu, Yifan Hu, Stephen North,, Han-Wei Shen. CorrelatedMultiples: Spatially Coherent Small Multiples with Constrained Multidimensional Scaling, In Computer Graphics Forum (CGF), January, 2015.
Xiaotong Liu, Srinivasan Parthasarathy, Han-Wei Shen,, Yifan Hu. GalaxyExplorer: Influence-Driven Visual Exploration of Context-Specific Social Media Interactions, In International World Wide Web Conference (WWW), May, 2015.
Shusen Liu, Dan Maljovec, Bei Wang, Peer-Timo Bremer, Valerio Pascucci. Visualizing High-Dimensional Data: Advances in the Past Decade, In Proceedings of Eurographics Conference on Visualization (EuroVis), STAR -- State of The Art Report, May, 2015.
Jeremy Logan, Scott Klasky, Norbert Podhorszki, Lizhe Wang, Wei Xue. Creating Skeletons for Task-Based Scientific Workflows, In Supercomputing Frontiers, March, 2015.
B. Loring, H. Karimabadi,, V. Rortershteyn. A Screen Space GPGPU Surface LIC Algorithm for Distributed Memory Data Parallel Sort Last Rendering Infrastructures, In Proceedings of the 9th International Conference on Numerical Modeling of Space Plasma Flows (ASTRONUM-2014), Long Beach, CA, USA March, 2015.
Kewei Lu, Han-Wei Shen. A compact multivariate histogram representation for query-driven visualization, In IEEE Symposium on Large Data Analysis and Visualization (LDAV), October, 2015.
Xaioqing Luo, Frank Mueller, Philip Carns, John Jenkins, Robert Ross, Shane Snyder, Robert Latham. ScalaIOExtrap: Elastic I/O Tracing and Extrapolation, In Proceedings of the Workshop on Extreme-Scale Programming Tools (ESPT 2015), November, 2015.
Huong Luu, Marianne Winslett, William Gropp, Kevin Harms, Philip Carns, Robert Ross, Yushu Yao, Suren Byna,, Prabhat. A Multiplatform Study of I/O Behavior on Petascale Supercomputers, In Proceedings of the 24th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC 2015), ACM, June, 2015.
P. Malakar, V. Vishwanath, T. Munson, C. Knight, M. Hereld, S. Leyffer, M. Papka. Optimal Scheduling of In Situ Analysis for Large-Scale Scientific Simulations, In Proceedings of the 28th IEEE/ACM International Conference for High Performance Computing, Networking, Storage and Analysis (SC 2015), Austin, Texas, USA, November, 2015.
P. Malakar, V. Vishwanath. Hierarchical Read-write Optimizations for Scientific Applications with Multi-variable Structured Datasets, In Proceedings of the 12th Annual IFIP International Conference on Network and Parallel Computing (NPC), New York City, New York, USA, September, 2015.
D. Maljovec, S. Liu, B. Wang, V. Pascucci, P.-T. Bremer, D. Mandelli, C. Smith. Analyzing simulation-based PRA data through clustering: a BWR station blackout case study, In Reliability Engineering & System Safety, Note: In Press, submitted, June, 2015.
Formal Metrics for Large-Scale Parallel Performance, In High Performance Computing, July, 2015.
Performance measurement of parallel algorithms is well studied and well understood. However, a flaw in traditional performance metrics is that they rely on comparisons to serial performance with the same input. This comparison is convenient for theoretical complexity analysis but impossible to perform in large-scale empirical studies with data sizes far too large to run on a single serial computer. Consequently, scaling studies currently rely on ad hoc methods that, although effective, have no grounded mathematical models. In this position paper we advocate using a rate-based model that has a concrete meaning relative to speedup and efficiency and that can be used to unify strong and weak scaling studies.
Kenneth Moreland, Matthew Larsen, Hank Childs.
Visualization for Exascale: Portable Performance is Critical, In Supercomputing Frontiers and Innovations, Vol. 2, No. 3, 2015.
Researchers face a daunting task to provide scientific visualization capabilities for exascale computing. Of the many fundamental changes we are seeing in HPC systems, one of the most profound is a reliance on new processor types optimized for execution bandwidth over latency hiding. Multiple vendors create such accelerator processors, each with significantly different features and performance characteristics. To address these visualization needs across multiple platforms, we are embracing the use of data parallel primitives that encapsulate highly efficient parallel algorithms that can be used as building blocks for conglomerate visualization algorithms. We can achieve performance portability by optimizing this small set of data parallel primitives whose tuning conveys to the conglomerates.
Visualizing the velocity decomposition of a group of objects has applications to many studied data types, such as Lagrangian-based flow data or geospatial movement data. Traditional visualization techniques are often subject to a trade-off between visual clutter and loss of detail, especially in a large scale setting. The use of 2D velocity histograms can alleviate these issues. While they have been used throughout domain specific areas on a basic level, there has been very little work in the visualization community on leveraging them to perform more advanced visualization tasks. In this work, we develop an interactive system which utilizes velocity histograms to visualize the velocity decomposition of a group of objects. In addition, we extend our tool to utilize two schemes for histogram generation: an on-the-fly sampling scheme as well as an in situ scheme to maintain interactivity in extreme scale applications.
Harald Obermaier, Kenneth I. Joy. An Automated Approach for Slicing Plane Placement in Visual Data Analysis, In IEEE Transactions on Visualization and Computer Graphics, May, 2015.
S. Philip, B. Summa, J. Tierny, P. Bremer, V. Pascucci. . Distributed seams for gigapixel panoramas, In IEEE Transactions on Visualization and Computer Graphics,, Vol. 21, No. 3, pp. 350–362. March, 2015.
Prabhat, S.Byna, V. Vishwanath, E. Dart, M. Wehner, W. Collins. TECA: Petscale Pattern Recognition for Climate Science, In Proceedings of the 16th International Conference on Computer Analysis of Images and Patterns (CAIP), Valletta, Malta, September, 2015.
James Kress, Scott Klasky, Norbert Podhorszki, Jong Choi, Hank Childs, David Pugmire. Loosely Coupled In Situ Visualization: A Perspective on Why It’s Here to Stay, In Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization, Nov, 2015.
In this position paper, we argue that the loosely coupled in situ processing paradigm will play an important role in high performance computing for the foreseeable future. Loosely coupled in situ is an enabling technique that addresses many of the current issues with tightly coupled in situ, including, ease-of-integration, usability, and fault tolerance. We sur- vey the prominent positives and negatives of both tightly coupled and loosely coupled in situ and present our recom- mendation as to why loosely coupled in situ is an enabling technique that is here to stay. We then report on some re- cent experiences with loosely coupled in situ processing, in an e ort to explore each of the discussed factors in a real- world environment.
Stephen Ranshous, Shitian Shen, Danai Koutra, Steve Harenberg, Christos Faloutsos, Nagiza F. Samatova. Anomaly Detection in Dynamic Networks: A Survey, In Wiley Interdisciplinary Reviews: Computational Statistics, June, 2015.
Silvio Rizzi, Mark Hereld, Joseph A. Insley, Michael E. Papka, Thomas Uram, Venkatram Vishwanath. Large-Scale Parallel Visualization of Particle-Based Simulations using Point Sprites and Level-Of-Detail, In Eurographics Parallel Graphics and Visualization (EGPGV), May, 2015.