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SDAV Publications
2017
Rob Latham, Matthieu Dorier, Robert Ross.
“Get out of the way! Applying compression to internal data structures,” In Pdsw-discs 2016: 1st joint international workshop on parallel data storage & data intensive scalable computing systems, held in conjunction with SC2016, 2017.
As the amount of memory per core decreases in post-petascale machines, the memory footprint of any libraries and middleware used by HPC applications must be reduced. While scientific data can contain a great deal of entropy and require specialized compression techniques, the \em descriptions of scientific data layouts, as opposed to contents, turn out to be highly compressible. In this paper we present two approaches to compressing scientific data layout descriptions. We also describe two data structures for managing the compressed data. We incorporated our approach into the ROMIO MPI-IO implementation to reduce the memory consumption, observing an 89x reduction in memory overhead with a 25% increase in CPU overhead.
W. Widanagamaachchi, A. Jacques, B. Wang, E. Crosman, P.-T. Bremer, V. Pascucci, J Horel.
“Exploring the Evolution of Pressure-Perturbations to Understand Atmospheric Phenomena,” In IEEE Pacific Visualization Symposium (PacificVis) , 2017.
2016
Andrew C. Bauer, Hasan Abbasi, James Ahrens, Hank Childs, Berk Geveci, Scott Klasky, Kenneth Moreland, Patrick O'Leary, Venkatram Vishwanath, Brad Whitlock, E. Wes Bethel.
“In Situ Methods, Infrastructures, and Applications on High Performance Computing Platforms,” In Computer Graphics Forum, Vol. 32, No. 3, pp. 577--597. June, 2016.
DOI: 10.1111/cgf.12930
The considerable interest in the high performance computing (HPC) community regarding analyzing and visualization data without first writing to disk, i. e., in situ processing, is due to several factors. First is an I/O cost savings, where data is analyzed/visualized while being generated, without first storing to a filesystem. Second is the potential for increased accuracy, where fine temporal sampling of transient analysis might expose some complex behavior missed in coarse temporal sampling. Third is the ability to use all available resources, CPU's and accelerators, in the computation of analysis products. This STAR paper brings together researchers, developers and practitioners using in situ methods in extreme-scale HPC with the goal to present existing methods, infrastructures, and a range of computational science and engineering applications using in situ analysis and visualization.
Kevin Bensema, Luke J. Gosink, Harald Obermaier, Kenneth I. Joy.
“Modality-Driven Classification and Visualization of Ensemble Variance,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 22, No. 10, 2016.
Harsh Bhatia, Attila G. Gyulassy, Valerio Pascucci, Martina Bremer, Mitchell T. Ong, Vincenzo Lordi, Erik W. Draeger, John E. Pask, & Peer-Timo Bremer.
“Interactive Exploration of Atomic Trajectories Through Relative-Angle Distribution and Associated Uncertainties,” In 2016 IEEE Pacific Visualization Symposium (PacificVis), pp. 120-127. April, 2016.
Biswas, Ayan; Strelitz, Richard; Woodring, Jonathan; Chen, Chun-Ming; Shen, Han-Wei.
“A Scalable Streamline Generation Algorithm Via Flux-Based Isocontour Extraction,” In Eurographics Symposium on Parallel Graphics and Visualization (EGPGV16), june, 2016.
Ebru Bozdag Daniel Peter Matthieu Lefebvre Dimitri Komatitsch Jeroen Tromp Judith Hill Norbert Podhorszki David Pugmire.
“Global adjoint tomography: first-generation model,” In Geophysical Journal International, Vol. 207, No. 3, Oxford University Press, pp. 1739-1766. Dec, 2016.
DOI: 10.1093/gji/ggw356
We present the first-generation global tomographic model constructed based on adjoint tomography, an iterative full-waveform inversion technique. Synthetic seismograms were calculated using GPU-accelerated spectral-element simulations of global seismic wave propagation, accommodating effects due to 3-D anelastic crust & mantle structure, topography & bathymetry, the ocean load, ellipticity, rotation, and self-gravitation. Fréchet derivatives were calculated in 3-D anelastic models based on an adjoint-state method. The simulations were performed on the Cray XK7 named ‘Titan’, a computer with 18 688 GPU accelerators housed at Oak Ridge National Laboratory. The transversely isotropic global model is the result of 15 tomographic iterations, which systematically reduced differences between observed and simulated three-component seismograms. Our starting model combined 3-D mantle model S362ANI with 3-D crustal model Crust2.0. We simultaneously inverted for structure in the crust and mantle, thereby eliminating the need for widely used ‘crustal corrections’. We used data from 253 earthquakes in the magnitude range 5.8 ≤ Mw ≤ 7.0. We started inversions by combining ∼30 s body-wave data with ∼60 s surface-wave data. The shortest period of the surface waves was gradually decreased, and in the last three iterations we combined ∼17 s body waves with ∼45 s surface waves. We started using 180 min long seismograms after the 12th iteration and assimilated minor- and major-arc body and surface waves. The 15th iteration model features enhancements of well-known slabs, an enhanced image of the Samoa/Tahiti plume, as well as various other plumes and hotspots, such as Caroline, Galapagos, Yellowstone and Erebus. Furthermore, we see clear improvements in slab resolution along the Hellenic and Japan Arcs, as well as subduction along the East of Scotia Plate, which does not exist in the starting model. Point-spread function tests demonstrate that we are approaching the resolution of continental-scale studies in some areas, for example, underneath Yellowstone. This is a consequence of our multiscale smoothing strategy in which we define our smoothing operator as a function of the approximate Hessian kernel, thereby smoothing gradients less wherever we have good ray coverage, such as underneath North America.
P.-T. Bremer, A. Gruber, J. Bennett, A. Gyulassy, H. Kolla, J. Chen, R.W. Grout.
“Identifying turbulent structures through topological segmentation,” In Com. in App. Math. and Comp. Sci., Vol. 11, No. 1, pp. 37-53. 2016.
P.-T. Bremer.
“ADAPT - Adaptive Thresholds for Feature Extraction,” In Topology-Based Methods in Visualization, Springer, 2016.
Hamish Carr, Gunther Weber, Christopher Sewell, James Ahrens.
“Parallel Peak Pruning for Scalable SMP Contour Tree Computation,” In Proceedings of the IEEE Symposium on Large Data Analysis and Visualization (LDAV), Baltimore, Maryland, Note: Best Paper Award. The results reported in this paper stem from the PISTON / VTK-m work established by SDAV; the specific work for this paper was funded under the ASCR XVIS project., October, 2016.
Chen, Chun-Ming, Dutta, Soumya, liu, Xiaotong, Heinlein, Gregory, Shen, Han-Wei, Chen, Jen-Ping.
“Visualization and Analysis of Rotating Stall for Transonic Jet Engine Simulation,” In IEEE SciVis 2015, also in IEEE Transactions on Visualization and Computer Graphics (TVCG), vol. 22, no. 1, 2016.
Jong Youl Choi, Tahsin Kurc, Jeremy Logan, Matthew Wolf, Eric Suchyta, James Kress, David Pugmire, Norbert Podhorszki, Eun-Kyu Byun, Mark Ainsworth, Manish Parashar, Scott Klasky.
“Stream processing for near real-time scientific data analysis,” In Scientific Data Summit (NYSDS), IEEE Xplore, IEEE, pp. 1-8. August, 2016.
DOI: 10.1109/NYSDS.2016.7747804
The demand for near real-time analysis of streaming data is increasing rapidly in scientific projects. This trend is driven by the fact that it is expensive and time consuming to design and execute complex experiments and simulations. During an experiment, the research team and the team at the experiment facility will want to analyze data as it is generated, interpret it, and collaboratively make decisions to modify the experiment parameters or abort the experiment in order to prevent events that may damage experimental instruments or to avoid wasting resources if there is a problem. The increasing velocity and volume of streaming data and the multi-institutional nature of large-scale scientific projects present challenges to near real-time analysis of streaming data. In this work we develop a framework to address these challenges. This framework provides an interface for applications to define and interact with named, self-describing streams, takes advantage of advanced network technologies, and implements support for the reduction and compression of data at the source. We describe this framework and demostrate its application in three scientific applications.
Dharshi Devendran, Suren Byna, Bin Dong, Brian van Straalen, Hans Johansen, Noel Keen, Nagiza Samatova.
“Collective I/O Optimizations for Adaptive Mesh Refinement Data Writes on Lustre File System,” In Cray User Group (CUG) , May, 2016.
Bin Dong, Suren Byna, Kesheng Wu.
“SDS-Sort: Scalable Dynamic Skew-aware Parallel Sorting,” In The ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC), July, 2016.
Dutta, Soumya, Shen, Han-Wei.
“Distribution Driven Extraction and Tracking of Features for Time-varying Data Analysis,” In IEEE SciVIS 2015, also in IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 1, 2016.
Dianwei Han, Ankit Agrawal, Wei-keng Liao, Alok Choudhary.
“A Novel Scalable DBSCAN Algorithm with Spark,” In the 5th International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics, held in conjunction with the International Parallel & Distributed Processing Symposium, Chicago, May, 2016.
Chien-Hsin Hsueh, Jacqueline Chu, Kwan-Liu Ma, Joyce Ma, Jennifer Frazier.
“Fostering Comparisons: Designing an Interactive Exhibit that Visualizes Marine Animal Behaviors,” In Proceedings of PacificVis 2016 (to appear), 2016.
Qiao Kang, Wei-keng Liao, Ankit Agrawal, Alok Choudhary.
“A Filtering-based Clustering Algorithm for Improving Spatio-temporal Kriging Interpolation Accuracy,” In the 25th ACM International Conference on Information and Knowledge Management, Indianapolis, Indiana, October, 2016.
James Kress, Randy Michael Churchill, Scott Klasky, Mark Kim, Hank Childs, David Pugmire.
“Preparing for In Situ Processing on Upcoming Leading-edge Supercomputers,” In Supercomputing Frontiers and Innovations, Vol. 3, No. 4, pp. 49-65. 2016.
DOI: 10.14529/jsfi160404
High performance computing applications are producing increasingly large amounts of data and placing enormous stress on current capabilities for traditional post-hoc visualization techniques. Because of the growing compute and I/O imbalance, data reductions, including in situ visualization, are required. These reduced data are used for analysis and visualization in a variety of different ways. Many of he visualization and analysis requirements are known a priori, but when they are not, scientists are dependent on the reduced data to accurately represent the simulation in post hoc analysis. The contributions of this paper is a description of the directions we are pursuing to assist a large scale fusion simulation code succeed on the next generation of supercomputers. These directions include the role of in situ processing for performing data reductions, as well as the tradeoffs between data size and data integrity within the context of complex operations in a typical scientific workflow.
James Kress David Pugmire Scott Klasky Hank Childs.
“Visualization and analysis requirements for in situ processing for a large-scale fusion simulation code,” In ISAV '16 Proceedings of the 2nd Workshop on In Situ Infrastructures for Enabling Extreme-scale Analysis and Visualization, pp. 45-50. 2016.
DOI: 10.1109/ISAV.2016.14
In situ techniques have become a very active research area since they have been shown to be an effective way to combat the issues associated with the ever growing gap between computation and I/O bandwidth. In order to take full advantage of in situ techniques with a large-scale simulation code, it is critical to understand the breadth and depth of its analysis requirements. In this paper, we present the results of a survey done with members of the XGC1 fusion simulation code team in order to gather their requirements for analysis and visualization. We look at these requirements from the perspective of in situ processing and present a list of XGC1 analysis tasks performed by its physicists, engineers, and visualization specialists. This analysis of the specific needs and use cases of a single code is important in understanding the nature of the needs that simulations have in terms of data movement and usage for visualization and analysis, now and in the future.
Larsen, Matthew, Harrison, Cyrus, Kress, James, Pugmire, David, Meredith, Jeremy S., Childs, Hank.
“Performance Modeling of In Situ Rendering,” In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC16), Salt Lake City, Utah, pp. 24:1--24:12. Nov, 2016.
ISBN: 978-1-4673-8815-3
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Matthew Larsen, Kenneth Moreland, Chris R. Johnson, Hank Childs.
“Optimizing Multi-Image Sort-Last Parallel Rendering,” In Proceedings of IEEE Symposium on Large Data Analysis and Visualization (LDAV), Baltimore, MD pp. 37--46. Oct, 2016.
Sunwoo Lee, Wei-keng Liao, Ankit Agrawal, Nikos Hardavellas, Alok Choudhary.
“Evaluation of K-Means Data Clustering Algorithm on Intel Xeon Phi,” In the Workshop on Advances in Software and Hardware for Big Data to Knowledge Disc overy, held in conjunction with the IEEE Bigdata Conference, Washington, D.C., December, 2016.
Liu, Xiaotong, Shen, Han-Wei.
“Association Analysis for Visual Exploration of Multivariate Scientific Data Sets,” In IEEE SciVis 2015, also in IEEE Transactions on Visualization and Computer Graphics (TVCG), vol. 22, no. 1, 2016.
S. Liu, P.-T. Bremer, J. Thiagarajan, B. Wang, B. Summa, V. Pascucci.
“Grassmannian Atlas: A General Framework for Exploring Linear Projections of High-Dimensional Data. Shusen Liu,” In Comput. Graph. Forum, 2016.
Dan Maljovec, Bei Wang, Paul Rosen, Andrea Alfonsi, Giovanni Pastore, Cristian Rabiti, Valerio Pascucci.
“Rethinking Sensitivity Analysis of Nuclear Simulations with Topology,” In IEEE Pacific Visualization Symposium (PacificVis), pp. 64-71. April, 2016.
DOI: 10.1109/PACIFICVIS.2016.7465252
In nuclear engineering, understanding the safety margins of the nuclear reactor via simulations is arguably of paramount importance in predicting and preventing nuclear accidents. It is therefore crucial to perform sensitivity analysis to understand how changes in the model inputs affect the outputs. Modern nuclear simulation tools rely on numerical representations of the sensitivity information - inherently lacking in visual encodings - offering limited effectiveness in communicating and exploring the generated data. In this paper, we design a framework for sensitivity analysis and visualization of multidimensional nuclear simulation data using partition-based, topology-inspired regression models and report on its efficacy. We rely on the established Morse-Smale regression technique, which allows us to partition the domain into monotonic regions where easily interpretable linear models can be used to assess the influence of inputs on the output variability. The underlying computation is augmented with an intuitive and interactive visual design to effectively communicate sensitivity information to nuclear scientists. Our framework is being deployed into the multipurpose probabilistic risk assessment and uncertainty quantification framework RAVEN (Reactor Analysis and Virtual Control Environment). We evaluate our framework using a simulation dataset studying nuclear fuel performance.
Changsung Moon, Dakota Medd, Paul Jones, Steve Harenberg, William Oxbury, Nagiza F. Samatova.
“Online Prediction of User Actions through an Ensemble Vote from Vector Representation and Frequency Analysis Models,” In SIAM International Conference on Data Mining (SDM), May, 2016.
The history of interactions between a user and a piece of technology can be represented as a sequence of actions. The ability to predict a user's next action is useful to many applications. For example, a user-interface that can anticipate the actions of a user is able to provide a more positive experience through just-in-time recommendations and pro-actively allocating or caching resources. Existing sequence prediction techniques have failed to address some of the challenges associated with this task, such as predicting an action that has never appeared for a given context. Techniques for an analogous task in the field of Natural Language Processing (NLP) avoid this issue; however, applying these NLP techniques directly to user action prediction would result in the loss of action frequency and action order, both of which are critically important. Therefore, we propose a method that unifies ideas from NLP with the task of sequence prediction. Our method, Frequency Vector (FVEC) prediction, is an online algorithm that predicts the top-N most likely next actions by combining scores from two models: a frequency analysis model and a vector representation model. In the frequency model, the score of an action is calculated based on the frequency that the action has occurred right after a given context. In the vector representation model, a vector for each action is learned, and a score for an action is calculated based on the similarity of its vector and the mean of the vectors for each action in a given context. Evaluations of FVEC on three real-world datasets resulted in a consistently higher prediction accuracy (and lower standard deviation) than all tested sequence prediction algorithms.
Kenneth Moreland.
“The Tensions of In Situ Visualization,” In IEEE Computer Graphics and Applications, Vol. 36, No. 2, pp. 5-9. March/April, 2016.
DOI: 10.1109/MCG.2016.35
In situ visualization is the coupling of visualization software with a simulation or other data producer to process the data "in memory" before the data are offloaded to a storage system. Although in situ visualization provides superior analysis, it has implementation tradeoffs resulting from conflicts with some traditional expected requirements. Numerous conflicting requirements create tensions that lead to difficult implementation tradeoffs. This article takes a look at the most prevailing tensions of in situ visualization.
Kenneth Moreland, Christopher Sewell, William Usher, Li-ta Lo, Jeremy Meredith, David Pugmire, James Kress, Hendrik Schroots, Kwan-Liu Ma, Hank Childs, Matthew Larsen, Chun-Ming Chen, Robert Maynard, Berk Geveci.
“VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures,” In IEEE Computer Graphics and Applications, Vol. 36, No. 3, pp. 48--58. May/June, 2016.
DOI: 10.1109/MCG.2016.48
One of the most critical challenges for high-performance computing (HPC) scientific visualization is execution on massively threaded processors. 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. Our current production scientific visualization software is not designed for these new types of architectures. To address this issue, the VTK-m framework serves as a container for algorithms, provides flexible data representation, and simplifies the design of visualization algorithms on new and future computer architecture.
Kenneth Moreland.
“Why We Use Bad Color Maps and What You Can Do About It,” In Proceedings of Human Vision and Electronic Imaging (HVEI), February, 2016.
DOI: 10.2352/ISSN.2470-1173.2016.16.HVEI-133
We know the rainbow color map is terrible, and it is emphatically reviled by the visualization community, yet its use continues to persist. Why do we continue to use a this perceptual encoding with so many known flaws? Instead of focusing on why we should not use rainbow colors, this position statement explores the rational for why we do pick these colors despite their flaws. Often the decision is influenced by a lack of knowledge, but even experts that know better sometimes choose poorly. A larger issue is the expedience that we have inadvertently made the rainbow color map become. Knowing why the rainbow color map is used will help us move away from it. Education is good, but clearly not sufficient. We gain traction by making sensible color alternatives more convenient. It is not feasible to force a color map on users. Our goal is to supplant the rainbow color map as a common standard, and we will find that even those wedded to it will migrate away.
Chris Muelder, Biao Zhu, Wei Chen, Hongxin Zhang, Kwan-Liu Ma.
“Visual Analysis of Cloud Computing Performance Using Behavioral Lines,” In Proceedings of PacificVis 2016 (to appear), 2016.
Tyson Neuroth, Franz Sauer, Weixing Wang, Stephane Ethier, Choong-Seock Chang,, Kwan-Liu Ma.
“Scalable Visualization of Time-varying Multi-parameter Distributions Using Spatially Organized Histograms,” In IEEE Transactions on Visualization and Computer Graphics, Vol. PP, No. 99, 2016.
Harald Obermaier, Kevin Bensema, Kenneth I. Joy.
“Visual Trends Analysis in Time-Varying Ensembles,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 22, No. 10, 2016.
Diana Palsetia, William Hendrix, Sunwoo Lee, Ankit Agrawal, Wei-keng Liao, Alok Choudhary.
“Parallel Community Detection Algorithm Using a Data Partitioning Strategy with Pairwise Subdomain Duplication,” In the 31st International Supercomputing Conference, Frankfurt, Germany, June, 2016.
Paris Perdikaris, Joseph A. Insley, Leopold Grinberg, Yue Yu, Michael E. Papka, George Em. Karniadakis.
“Visualizing Multiphysics, Fluid-Structure Interaction Phenomena in Intracranial Aneurysms,” In Parallel Computing journal, Vol. 55, pp. 9-16. July, 2016.
DOI: 10.1016/j.parco.2015.10.016
Annie Preston, Ramyar Ghods, Jinrong Xie, Franz Sauer, Nick Leaf, Kwan-Liu Ma, Esteban Rangel, Eve Kovacs, Katrin Heitmann, Salman Habib.
“An Integrated Visualization System for Interactive Analysis of Large, Heterogeneous Cosmology Data,” In Proceedings of PacificVis 2016 (to appear), 2016.
David Pugmire; James Kress; Hank Childs; Matthew Wolf; Greg Eisenhauer; Randy Churchill; Tahsin Kurc; Jong Choi; Scott Klasky; Kesheng Wu; Alex Sim; Junmin Gu.
“Visualization and Analysis for Near-Real-Time Decision Making in Distributed Workflows,” In High Performance Data Analysis and Visualization (HPDAV) 2016 held in conjuction with IPDPS 2016, Chicago, May, 2016.
Roberto Sisneros, David Pugmire.
“Tuned to Terrible: A Study of Parallel Particle Advection State of the Practice.,” In High Performance Data Analysis and Visualization (HPDAV) 2016 held in conjuction with IPDPS 2016, Chicago, May, 2016.
Dave Pugmire, Jeremy Meredith, Scott Klasky, Jong Choi, Norbert Podhorszki, James Kress, Hank Childs.
“Visualization Plugins using VTKm for In-Transit Visualization with ADIOS,” In Supercomputing Frontiers 2016, Singapore, March, 2016.
Stephen Ranshous, Steve Harenberg, Kshitij Sharma, Nagiza F. Samatova.
“A Scalable Approach for Outlier Detection in Edge Streams Using Sketch-based Approximations,” In SIAM International Conference on Data Mining (SDM), May, 2016.
Esteban Rangel, Wei-keng Liao, Ankit Agrawal, Alok Choudhary, William Hendrix.
“AGORAS: A Fast Algorithm for Estimating Medoids in Large Datasets,” In the Workshop on Computational Optimization, Modeling & Simulation, held in conjunction with the International Conference on Computational Science, San Diego, June, 2016.
Esteban Rangel, Nan Li, Salman Habib, Tom Peterka, Ankit Agrawal, Wei-Keng Liao, Alok Choudhary.
“Parallel DTFE Surface Density Field Reconstruction,” In the IEEE International Conference on Cluster Computing, Taipei, Taiwan, Note: Best paper award, September, 2016.
Silvio Rizzi, Mark Hereld, Joseph A. Insley, Preeti Malakar, Michael E. Papka, Thomas Uram, Venkatram Vishwanath.
“Coupling LAMMPS and the vl3 Framework for Co-Visualization of Atomistic Simulations,” In High Performance Data Analysis and Visualization (HPDAV) 2016, May, 2016.
Melissa Romanus, Fan Zhang, Tong Jin, Qian Sun, Hoang Bui, Ivan Rodero, Jong Choi, Salomon Janhunen, Robert Hager, Scott Klasky, Choong-Seock Chang, Manish Parashar.
“Persistent Data Staging Services for Data Intensive In-Situ Scientific Workflows,” In The 7th International Workshop on Data-intensive Distributed Computing in conjunction with the 25th International ACM Symposium on High Performance Parallel and Distributed Computing(HPDC'16), Kyoto, Japan, Note: To Appear In, June, 2016.
Oliver Ruebel, Burlen Loring, Jean-Luc Vay, David P. Grote, Remi Lehe, Stepan Bulanov, Henri Vincenti,, E. Wes Bethel.
“WarpIV: In Situ Visualization and Analysis of Ion Accelerator Simulations,” In IEEE Computer Graphics and Applications, Vol. 36, No. 3, pp. 22-35. may, 2016.
ISSN: 0272-1716
DOI: 10.1109/MCG.2016.62
U. Rüde, K. Willcox, L. C. McInnes, H. De Sterck, G. Biros, H. Bungartz, J. Corones, E. Cramer, J. Crowley, O. Ghattas, M. Gunzburger, M. Hanke, R. Harrison, M. Heroux, J. Hesthaven, P. Jimack, C. Johnson, K. E. Jordan, D. E. Keyes, R. Krause, V. Kumar, S. Mayer, J. Meza, K. M. Mørken, J. T. Oden, L. Petzold, P. Raghavan, S. M. Shontz, A. Trefethen, P. Turner, V. Voevodin, B. Wohlmuth, C. S. Woodward.
“Research and Education in Computational Science and Engineering,” Subtitled “Report from a workshop sponsored by the Society for Industrial and Applied Mathematics (SIAM) and the European Exascale Software Initiative (EESI-2),” Aug, 2016.
Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.
Franz Sauer, Yubo Zhang, Weixing Wang, Stephane Ethier, Kwan-Liu Ma.
“Visualization Techniques for Studying Large-Scale Flow Fields from Fusion Simulations,” In Computer Science and Engineering, Vol. 18, No. 2, IEEE, pp. 68-77. March, 2016.
DOI: 10.1109/MCSE.2015.107
Min Shih, Silvio Rizzi, Joseph Insley, Thomas Uram, Venkat Vishwanath, Mark Hereld, Michael E. Papka, Kwan-Liu Ma.
“Parallel Distributed, GPU-Accelerated, Advanced Lighting Calculations for Large-Scale Volume Visualization,” In IEEE Symposium on Large Data Analysis and Visualization (LDAV) 2016, Note: Best Paper Honorable Mention Award, October, 2016.
Primoz Skraba, Paul Rosen, Bei Wang, Guoning Chen, Harsh Bhatia, Valerio Pascucci.
“Critical Point Cancellation in 3D Vector Fields: Robustness and Discussion,” In IEEE Transactions on Visualization & Computer Graphics. Also Best Paper at PacificVis, April, 2016.
Shane Snyder, Philip Carns, Kevin Harms, Robert Ross, Glenn K. Lockwood, Nicholas J. Wright.
“Modular HPC I/O Characterization with Darshan,” In Proceedings of 5th Workshop on Extreme-scale Programming Tools (ESPT 2016), 11, 2016.
H. De Sterck, C. Johnson,, L. C. McInnes.
“Special Section on Two Themes: CSE Software and Big Data in CSE,” In SIAM J. Sci. Comput, Vol. 38, No. 5, SIAM, pp. S1--S2. 2016.
The 2015 SIAM Conference on Computational Science and Engineering (CSE) was held March 14-18, 2015, in Salt Lake City, Utah. The SIAM Journal on Scientific Computing (SISC) created this special section in association with the CSE15 conference. The special section focuses on two topics that are of significant current interest to CSE researchers: CSE software and big data in CSE.
Qian Sun, Melissa Romanus, Tong Jin, Hongfeng Yu, Peer-Timo Bremer, Steve Petruzza, Scott Klasky, Manish Parashar.
“In-Staging Data Placement for Asynchronous Coupling of Task-Based Scientific Workflows,” In The 2nd International Workshop on Extreme Scale Programming Models and Middleware(ESPM2'16) in conjunction with The International Conference on High Performance Computing, Networking, Storage and Analysis, Utah, USA, Note: Best paper award, Nov, 2016.
Houjun Tang, Suren Byna, Steven Harenberg, Xiaocheng Zou, Wenzhao Zhang, Kesheng Wu, Bin Dong, Oliver Rubel, Kristofer Bouchard, Scott Klasky, Nagiza Samatova.
“Usage Pattern-Driven Dynamic Data Layout Reorganization,” In 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), May, 2016.
As scientific simulations and experiments move toward extremely large scales and generate massive amounts of data, the data access performance of analytic applications becomes crucial. A mismatch often happens between write and read patterns of data accesses, typically resulting in poor read performance. Data layout reorganization has been used to improve the locality of data accesses. However, current data reorganizations are static and focus on generating a single (or set of) optimized layouts that rely on prior knowledge of exact future access patterns. We propose a framework that dynamically recognizes the data usage patterns, replicates the data of interest in multiple reorganized layouts that would benefit common read patterns, and makes runtime decisions on selecting a favorable layout for a given read pattern. This framework supports reading individual elements and chunks of a multi-dimensional array of variables. Our pattern-driven layout selection strategy achieves multi-fold speedups compared to reading from the original dataset.
X. Tong, J. Edwards, C. Chen, H. Shen, C. R. Johnson, P. Wong.
“View-Dependent Streamline Deformation and Exploration,” In Transactions on Visualization and Computer Graphics, Vol. 22, No. 7, IEEE, pp. 1788--1801. July, 2016.
Occlusion presents a major challenge in visualizing 3D flow and tensor fields using streamlines. Displaying too many streamlines creates a dense visualization filled with occluded structures, but displaying too few streams risks losing important features. We propose a new streamline exploration approach by visually manipulating the cluttered streamlines by pulling visible layers apart and revealing the hidden structures underneath. This paper presents a customized view-dependent deformation algorithm and an interactive visualization tool to minimize visual clutter in 3D vector and tensor fields. The algorithm is able to maintain the overall integrity of the fields and expose previously hidden structures. Our system supports both mouse and direct-touch interactions to manipulate the viewing perspectives and visualize the streamlines in depth. By using a lens metaphor of different shapes to select the transition zone of the targeted area interactively, the users can move their focus and examine the vector or tensor field freely.
Wathsala Widanagamaachchi, Yarden Livnat, Peer-Timo Bremer, Scott Duvall, Valerio Pascucci.
“Interactive Visualization and Exploration of Patient Progression in a Hospital Setting,” In Proceedings of the 2016 Workshop on Visual Analytics in Healthcare, 2016.
Zheng Yuan, William Hendrix, Seung Woo Son, Christoph Federrath, An kit Agrawal, Wei-keng Liao, Alok Choudhary.
“Parallel Implementation of Lossy Data Compression for Temporal Data Sets,” In the 23rd International Conference on High Performance Computing, Hyderabad, India, December, 2016.
Dawid Zawislak, William Allcock, Joseph Insley, Michael E. Papka, Silvio Rizzi, Brian Toonen.
“Early Investigations Into Using a Remote RAM Pool with the vl3 Visualization Framework,” In In Situ Infrastructures for Enabling Extreme-scale Analysis and Visualization (ISAV) 2016, Salt Lake City, UT, November, 2016.
Wenzhao Zhang, Houjun Tang, Steven Harenberg, Suren Byna, Xiaocheng Zou, Dharshi Devendran, Daniel Martin, Kesheng Wu, Bin Dong, Scott Klasky, Nagiza Samatova.
“AMRZone: A Runtime AMR Data Sharing Framework For Scientific Applications,” In 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), May, 2016.
Abstract—Frameworks that facilitate runtime data sharing across multiple applications are of great importance for scientific data analytics. Although existing frameworks work well over uniform mesh data, they can not effectively handle adaptive mesh refinement (AMR) data. Among the challenges to construct an AMR-capable framework include: (1) designing an architecture that facilitates online AMR data management; (2) achieving a load-balanced AMR data distribution for the data staging space at runtime; and (3) building an effective online index to support the unique spatial data retrieval requirements for AMR data. Towards addressing these challenges to support runtime AMR data sharing across scientific applications, we present the AMRZone framework. Experiments over real-world AMR datasets demonstrate AMRZone’s effectiveness at achieving a balanced workload distribution, reading/writing large-scale datasets with thousands of parallel processes, and satisfying queries with spatial constraints. Moreover, AMRZone’s performance and scalability are even comparable with existing state-of-the-art work when tested over uniform mesh data with up to 16384 cores; in the best case, our framework achieves a 46% performance improvement.
Yubo Zhang, Kwan-Liu Ma.
“Decoupled Shading for Real-time Heterogeneous Volume Illumination,” In Proceedings of EuroVis 2016 (to appear), 2016.
Xiaocheng Zou, David Boyuka, Dhara Desai, Daniel Martin, Suren Byna, Kesheng Wu, Kushal Bansal, Bin Dong, Wenzhao Zhang, Houjun Tang, Dharshi Devendran, David Trebotich, Scott Klasky, Hans Johansen, Nagiza Samatova.
“AMR-aware In Situ Indexing and Scalable Querying,” In The 24th High Performance Computing Symposium (HPC), April, 2016.
Query-driven analytics on scientific datasets is one of fundamental approaches for scientific discoveries. Existing studies have explored query-driven analytics on uniform resolution meshes. However, querying on adaptive mesh refinement (AMR) data has not been explored yet. As many simulations have been lately transitioning to AMR, new methods for efficient query-driven analysis on AMR data are needed. In this paper, we present the first work to support scalable AMR-aware analysis. We propose an AMR-aware hybrid index for supporting two common forms (i.e., spatial and value-based query selections) in query-driven analytics. To sustainably support future-scale analysis, we design an in situ (run-time) index building strategy with minimized performance impact to the co-located simulation. Additionally, we develop a parallel post-processing query method with an adaptive workload-balanced strategy. Our evaluation demonstrates the scalability of our in situ indexing and scalable querying methods up to 16,384 and 1,024 cores, respectively, using a Chombo-based benchmark. Compared to non-AMR-aware indexing and querying, we demonstrate up to 12.4x and 500x performance improvement, respectively.
2015
Alexy Agranovsky, David Camp Kenneth I. Joy,, Hank Chids.
“Subsampling-based Compression and Flow Visualizaiton,” In SPIE, Visual Data Analysis Conference, San Francisco CA, February, 2015.
Alexy Agranovsky, Harald Obermaier, Christoph Garth, Kenneth I. Joy.
“A Multi-resolution Interpolation Scheme for Pathline-based Lagrangian Flow Representations,” In SPIE, Visual Data Analysis Conference, San Francisco CA, February, 2015.
Utkarsh Ayachit, Andrew Bauer, Berk Geveci, Patrick O'Leary, Kenneth Moreland, Nathan Fabian, Jeffrey Mauldin.
“ParaView Catalyst: Enabling In Situ Data Analysis and Visualization,” In Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV 2015), pp. 25-29. November, 2015.
DOI: 10.1145/2828612.2828624
Computer simulations are growing in sophistication and producing results of ever greater fidelity. This trend has been enabled by advances in numerical methods and increasing computing power. Yet these advances come with several costs including massive increases in data size, difficulties examining output data, challenges in configuring simulation runs, and difficulty debugging running codes. Interactive visualization tools, like ParaView, have been used for post- processing of simulation results. However, the increasing data sizes, and limited storage and bandwidth make high fidelity post-processing impractical. In situ analysis is recognized as one of the ways to address these challenges. In situ analysis moves some of the post-processing tasks in line with the simulation code thus short circuiting the need to communicate the data between the simulation and analysis via storage. ParaView Catalyst is a data processing and visualization library that enables in situ analysis and visualization. Built on and designed to interoperate with the standard visualization toolkit VTK and the ParaView application, Catalyst enables simulations to intelligently per- form analysis, generate relevant output data, and visualize results concurrent with a running simulation. In this paper, we provide an overview of the Catalyst framework and some of the success stories.
Babak Behzad, Suren Byna, Stefan Wild, Prabhat, Marc Snir.
“Dynamic Model-driven Parallel I/O Performance Tuning,” In IEEE Cluster 2015, Chicago, https://sdm.lbl.gov/~sbyna/research/papers/201509-Cluster-Autotune.pdf, September, 2015.
J. Bennett, F. Vivodtzev,, V. Pascucci.
“Topological and statistical methods for complex data. Mathematics and Visualization,” In Topological and statistical methods for complex data. Mathematics and Visualization, Springer, May, 2015.
E. Wes Bethel, David Camp, David Donofrio, Mark Howison.
“Improving Performance of Structured-memory, Data-Intensive Applications on Multi-core Platforms via a Space-Filling Curve Memory Layout,” In International Workshop on High Performance Data Intensive Computing, and IEEE International Parallel and Distributed Processing Symposium (IPDPS) workshop, Hyderabad, India May, 2015.
Harsh Bhatia, Bei Wang, Gregory Norgard, Valerio Pascucci, Peer-Timo Bremer.
“Local, Smooth, and Consistent Jacobi Set Simplification,” In Computational Geometry: Theory and Applications (CGTA), 48(4), pp. 311-332. May, 2015.
Ayan Biswas, Wenbin He, Qi Deng, Chun-Ming Chen, Han-Wei Shen, Raghu Machiraju, Anand Rangarajan.
“An Uncertainty-Driven Approach to Vortex Analysis Using Oracle Consensus and Spatial Proximity,” In IEEE Pacific Vis, Hangzhou, China, April, 2015.
Drew A. Boyuka, Xiaocheng Zou, Nagiza Samatova, Junmin Gu, Kesheng Wu, Norbert Podhorszki, Scott Klasky.
“ADIOS Query Interface Design ,” In Supercomputing Frontiers, June, 2015.
David A. Boyuka II, Houjun Tang, Kushal Bansal, Xiaocheng Zou, Scott Klasky, Nagiza F. Samatova.
“The Hyperdyadic Index and Generalized Indexing and Query with PIQUE,” In International Conference on Scientific And Statistical Database Management (SSDBM), June, 2015.
Many scientists rely on indexing and query to identify trends and anomalies within extreme-scale scientific data. Compressed bitmap indexing (e.g., FastBit) is the go-to indexing method for many scientific datasets and query workloads. Recently, the ALACRITY compressed inverted index was shown as a viable alternative approach. Notably, though FastBit and ALACRITY employ very different data structures (inverted list vs. bitmap) and binning methods (bit-wise vs. decimal-precision), close examination reveals marked similarities in index structure. Motivated by this observation, we ask two questions. First, "Can we generalize FastBit and ALACRITY to an index model encompassing both?" And second, if so, "Can such a generalized framework enable other, new indexing methods?" This paper answers both questions in the affrmative. First, we present PIQUE, a Parallel Indexing and Query Unified Engine, based on formal mathematical decomposition of the indexing process. PIQUE factors out commonalities in indexing, employing algorithmic/data structure "plugins" to mix orthogonal indexing concepts such as FastBit compressed bitmaps with ALACRITY binning, all within one framework. Second, we define the hyperdyadic tree index, distinct from both bitmap and inverted indexes, demonstrating good index compression while maintaining high query performance. We implement the hyperdyadic tree index within PIQUE, reinforcing our unified indexing model. We conduct a performance study of the hyperdyadic tree index vs. WAH compressed bitmaps, both within PIQUE and compared to FastBit, a state-of-the-art bitmap index system. The hyperdyadic tree index shows a 1.14-1.90x storage reduction vs. compressed bitmaps, with comparable or better query performance under most scenarios tested.
Peer-Timo Bremer, Dan Maljovec, Avishek Saha, Bei Wang, Jim Gaffney, Brian K. Spears, Valerio Pascucci.
“ND2AV: N-Dimensional Data Analysis and Visualization - Analysis for the National Ignition Campaign,” In Computing and Visualization in Science, February, 2015.
Huy Bui, Robert Jacob, Preeti Malakar, Venkatram Vishwanath , Andrew Johnson, Michael Papka,, Jason Leigh.
“Multipath Load Balancing for M × N Communication Patterns on the Blue Gene/Q Supercomputer Interconnection Network,” In 1st IEEE International Workshop on High-Performance Interconnection Networks Towards the Exascale and Big-Data Era, co-located with IEEE Cluster, Chicago, IL, USA, September, 2015.
Roxana Bujack, Jens Kasten, Vijay Natarajan, Gerik Scheuermann, Kenneth I. Joy.
“Clustering Moment Invariants to Identify Similarity within 2D Flow Fields,” In Eurographics Conference on Visualization (EuroVis) - short paper, May, 2015.
Roxana Bujack, Kenneth Joy.
“Lagrangian Representations of Flow Fields with Parameter Curves,” In IEEE Symposium on Large Data Analysis and Visualization (LDAV), October, 2015.
Michael Bussmann, Axel Huebl, René Widera, Felix Schmitt, Sebastian Grottel, Norbert Podhorszki, Dave Pugmire, Scott Klasky.
“Breaking the Simulation/Analysis Chain,” In Supercomputing Frontiers, March, 2015.
Suren Byna, Robert Sisneros, Kalyana Chadalavada, Quincey Koziol.
“Tuning Parallel I/O on Blue Waters for Writing 10 Trillion Particles,” In Cray User Group (CUG) meeting, 2015.
Hamish Carr, Zhao Geng, Julien Tierny, Amit Chattopadhyay, Aaron Knoll.
“Fiber Surfaces: Generalizing Isosurfaces to Bivariate Data,” In Computer Graphics Forum, proc. Eurovis, May, 2015.
Abon Chaudhuri, Teng-Yok Lee, Han-Wei Shen, Rephael Wenger.
“Exploring Flow Fields Using Space-filling Analysis of Streamlines,” In IEEE Transactions on Visualization and Computer Graphics (TVCG), January, 2015.
Jennifer Chandler, Harald Obermaier, Kenneth I. Joy.
“Interpolation-Based Pathline Tracing in Particle-Based Flow Visualization,” In IEEE Transactions on Visualization and Computer Graphics, Vol 21, No. 1, January, 2015.
Jennifer Chandler, Harald Obermaier,, Kenneth I. Joy.
“WebGL-Enabled Remote Visualization of Smoothed Particle Hydrodynamics Simulations,” In Eurographics Conference on Visualization (EuroVis) - short paper, May, 2015.
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
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.
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.
Kenneth Moreland, Ron Oldfield.
“Formal Metrics for Large-Scale Parallel Performance,” In High Performance Computing, July, 2015.
DOI: 10.1007/978-3-319-20119-1_34
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.
DOI: 10.14529/jsfi150306
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.
Tyson Neuroth, Franz Sauer, Weixing Wang, Stephane Ethier, Kwan-Liu Ma.
“Scalable Visualization of Discrete Velocity Decompositions using Spatially Organized Histograms,” In Proceedings of LDAV 2015, IEEE, pp. 65-72. October, 2015.
DOI: 10.1109/LDAV.2015.7348073
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.
Silvio Rizzi, Mark Hereld, Joseph A. Insley, Michael E. Papka, Thomas Uram, Venkatram Vishwanath.
“Large-Scale Co-Visualization for LAMMPS using vl3,” In IEEE Symposium on Large Data Analysis and Visualization (LDAV) - poster, October, 2015.
William Schroeder, Robert Maynard, Berk Geveci.
“Flying Edges: A High-Performance Scalable Isocontouring Algorithm,” In To appear in the Proceedings of the IEEE Symposium on Large Data Analysis and Visualization (LDAV), Chicago, Illinois, October, 2015.
Hendrik A. Schroots, Kwan-Liu Ma.
“Volume Rendering with Data Parallel Visualization Frameworks for Emerging High Performance Computing Architectures,” In Proceedings of 2015 Symposium on Visualization in High Performance Computing, co-located with ACM SIGGRAPH Asia 2015, ACM, pp. 1-4. November, 2015.
Christopher Sewell, Li-ta Lo, Katrin Heitmann, Salman Habib,, James Ahrens.
“Utilizing Many-Core Accelerators for Halo and Center Finding within a Cosmology Simulation,” In Proceedings of the IEEE Symposium on Large Data Analysis and Visualization (LDAV), Chicago, Illinois, October, 2015.
Christopher Sewell, Katrin Heitmann, Hal Finkel, George Zagaris, Suzanne T. Parete-Koon, Patricia K. Fasel, Adrian Pope, Nicholas Frontiere, Li-ta Lo, Bronson Messer, Salman Habib,, James Ahrens.
“Large-Scale Compute-Intensive Analysis via a Combined In-situ and Co-scheduling Workflow Approach,” In Proceedings of The International Conference for High Performance Computing, Networking, Storage, and Analysis, Austin, Texas, November, 2015.
S. Shannigrahi, A. J. Barczyk, C. Papadopoulos, A. Sim, I. Monga, H. Newman, K. Wu, E. Yeh.
“Named Data Networking in Climate Research and HEP Applications,” In 21st International Conference on Computing in High Energy and Nuclear Physics (CHEP), 2015.
Min Shih, Yubo Zhang, Kwan-Liu Ma.
“Advanced Lighting for Unstructured-Grid Data Visualization,” In PacificVis, April, 2015.
Shane Snyder, Philip Carns, Robert Latham, Misbah Mubarak, Rob Ross, Christopher Carothers, Babak Behzad, Huong Vu Thanh Luu, Surendra Byna, Prabhat.
“Techniques for Modeling Large-Scale HPC I/O Workloads,” In Proceedings of the 6th International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS15), November, 2015.
B. Summa, A. A. Gooch, G. Scorzelli, V. Pascucci.
“Paint and Click: Unified Interactions for Image Boundaries,” In Computer Graphics Forum, May, 2015.
Q. Sun, T. Jin, M. Romanus, H. Bui, F. Zhang, H. Yu, H. Kolla, S. Klasky, J. Chen, M. Parashar.
“Adaptive Data Placement For Staging-Based Coupled Scientific Workflows,” In ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, Austin, USA, November, 2015.
Yuzuru Tanahashi, Chien-Hsin Hsueh, Kwan-Liu Ma.
“An Efficient Framework for Generating Storyline Visualizations from Streaming Data,” In IEEE Transactions on Visualization and Computer Graphics, June, 2015.
Xin Tong, Chun-Ming Chen, Han-Wei Shen, Pak Chung Wong.
“Interactive Streamline Exploration and Manipulation Using Deformation,” In IEEE Pacific Vis 2015, Hangzhou, China, April, 2015.
Wathsala Widanagamaachchi, Karl D. Hammond, Li-Ta Lo, Brian D. Wirth, Francesca Samsel, Christopher Sewell, James Ahrens,, Valerio Pascucci.
“Visualization of Large-Scale Atomistic Simulations of Plasma-Surface Interactions,” In Proceedings of EuroVis (short paper), Cagliari, Italy, Note: The results reported in this paper stem from the PISTON / VTK-m work established by SDAV; the specific work for this paper was funded under the SciDAC Plasma Surface Interactions project, May, 2015.
W. Widanagamaachchi, P. Klacansky, H. Kolla, J. Chen, A. Bhagatwala, V. Pascucci, P.-T. Bremer.
“Tracking Features in Embedded Surfaces: Understanding Extinction in Turbulent Combustion,” In Proc. IEEE Symposium Large-Scale Data Analysis and Visualization, October, 2015.
L. Wu, K. Wu, A. Sim, M. Churchill, J. Y. Choi, A. Stathopoulos, C.S. Chang, S. Klasky.
“Towards Real-Time Detection and Tracking of Blob-Filaments in Fusion Plasma Big Data,” In Department of Computer Science, College of William and Mary, WM-CS-2015-01, 2015.
Jinrong Xie, Franz Sauer, Kwan-Liu Ma.
“Fast Uncertainty-driven Large-scale Volume Feature Extraction on Desktop PCs,” In Proceedings of LDAV 2015, IEEE, pp. 17-24. October, 2015.
DOI: 10.1109/LDAV.2015.7348067
Yucong Ye, Yang Wang, Robert Miller, Kwan-Liu Ma, Kenji Ono.
“In Situ Depth Maps Based Feature Extraction and Tracking,” In Proceedings of LDAV 2015, IEEE, pp. 1-8. October, 2015.
DOI: 10.1109/LDAV.2015.7348065
W. Yoo, M. Koo, Y. Cao, A. Sim, P. Nugent, K. Wu.
“PATHA: Performance Analysis Tool for HPC Applications,” In the 34th IEEE International Performance Computing and Communications Conference (IPCCC), 2015.
Hongfeng Yu, Jinrong Xie, Kwan-Liu Ma, Hemanth Kolla, Jacqueline H. Chen.
“Scalable Parallel Distance Field Construction for Large-Scale Applications,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 21, No. 10, pp. 1187-1200. August, 2015.
Wenzhao Zhang, Houjun Tang, Xiaocheng Zou, Steven Harenberg, Qing Liu, Scott Klasky, Nagiza F. Samatova.
“Exploring Memory Hierarchy to Improve Scientific Data Read Performance,” In Cluster Computing, September, 2015.
Yanwei Zhang, Matthew Wolf, Karsten Schwan, Qing Liu, Greg Eisenhauer,Scott Klasky.
“Co-Sites: The Autonomous Distributed Dataflows in Collaborative Scientific Discovery,” In 10th Workshop on Workflows in Support of Large-Scale Science (WORKS ’15), in conjunction with SC'15, November, 2015.
Xiaocheng Zou, Kesheng Wu, David A. Boyuka II, Daniel F. Martin, Suren Byna, Houjun Tang, Kushal Bansal, Terry J. Ligocki, Hans Johansen,, Nagiza F. Samatova.
“Parallel In Situ Detection of Connected Components in Adaptive Mesh Refinement Data,” In Proc. Cluster, Cloud and Grid Computing (CCGrid), May, 2015.
Adaptive Mesh Refinement (AMR) represents a significant advance for scientific simulation codes, greatly reducing memory and compute requirements by dynamically varying simulation resolution over space and time. As simulation codes transition to AMR, existing analysis algorithms must also make this transition. One such algorithm, connected component detection, is of vital importance in many simulation and analysis contexts, with some simulation codes even relying on parallel, in situ connected component detection for correctness. Yet, current detection algorithms designed for uniform meshes are not applicable to hierarchical, non-uniform AMR, and to the best of our knowledge, AMR connected component detection has not been explored in the literature. Therefore, in this paper, we formally define the general problem of connected component detection for AMR, and present a general solution. Beyond solving the general detection problem, achieving viable in situ detection performance is even more challenging. The core issue is the conflict between the communication-intensive nature of connected component detection (in general, and especially for AMR data) and the requirement that in situ processes incur minimal performance impact on the co-located simulation. We address this challenge by presenting the first connected component detection methodology for structured AMR that is applicable in a parallel, in situ context. Our key strategy is the incorporation of an multi-phase AMR-aware communication pattern that synchronizes connectivity information across the AMR hierarchy. In addition, we distill our methodology to a generic framework within the Chombo AMR infrastructure, making connected component detection services available for many existing applications. We demonstrate our method’s efficacy by showing its ability to detect ice calving events in real time within the real-world BISICLES ice sheet modeling code. Results show up to a 6.8x speedup of our algorithm over the existing specialized BISICLES algorithm. We also show scalability results for our method up to 4,096 cores using a parallel Chombo-based benchmark.
Xiaocheng (Chris) Zou, Suren Byna, Hans Johansen, Daniel Martin, Nagiza F. Samatova, Arie Shoshani, John Wu.
“Six-fold Speedup of Ice Calving Detection Achieved by AMR-aware Parallel Connected Component Labeling,” In SciDAC PI Meeting, July, 2015.
2014
Alexy Agranovsky, David Camp, Christoph Garth, E. Wes Bethel, Kenneth I. Joy, Hank Childs.
“Improved Post Hoc Flow Analysis vis Lagrangian Representations,” In Proceedings of the Large Data Analysis and Visualization Symposium (LDAV), Paris, France, Note: Best Paper Award, pp. 67–75. November, 2014.
H. Bhatia, V. Pascucci, R.M. Kirby, P.-T. Bremer.
“Extracting Features from Time-Dependent Vector Fields Using Internal Reference Frames,” In Computer Graphics Forum (Proceedings of EuroVis), Vol. 33, No. 3, pp. 21--30. June, 2014.
Extracting features from complex, time-dependent flow fields remains a significant challenge despite substantial research efforts, especially because most flow features of interest are defined with respect to a given reference frame. Pathline-based techniques, such as the FTLE field, are complex to implement and resource intensive, whereas scalar transforms, such as λ2, often produce artifacts and require somewhat arbitrary thresholds. Both approaches aim to analyze the flow in a more suitable frame, yet neither technique explicitly constructs one.
This paper introduces a new data-driven technique to compute internal reference frames for large-scale complex flows. More general than uniformly moving frames, these frames can transform unsteady fields, which otherwise require substantial processing of resources, into a sequence of individual snapshots that can be analyzed using the large body of steady-flow analysis techniques. Our approach is simple, theoretically well-founded, and uses an embarrassingly parallel algorithm for structured as well as unstructured data. Using several case studies from fluid flow and turbulent combustion, we demonstrate that internal frames are distinguished, result in temporally coherent structures, and can extract well-known as well as notoriously elusive features one snapshot at a time.
G.P. Bonneau, H.C. Hege, C.R. Johnson, M.M. Oliveira, K. Potter, P. Rheingans, T. Schultz.
“Overview and State-of-the-Art of Uncertainty Visualization,” In Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization, Ch. 1, Edited by M. Chen and H. Hagen and C.D. Hansen and C.R. Johnson and A. Kauffman, Springer-Verlag, pp. 3--27. 2014.
The goal of visualization is to effectively and accurately communicate data. Visualization research has often overlooked the errors and uncertainty which accompany the scientific process and describe key characteristics used to fully understand the data. The lack of these representations can be attributed, in part, to the inherent difficulty in defining, characterizing, and controlling this uncertainty, and in part, to the difficulty in including additional visual metaphors in a well designed, potent display. However, the exclusion of this information cripples the use of visualization as a decision making tool due to the fact that the display is no longer a true representation of the data. This systematic omission of uncertainty commands fundamental research within the visualization community to address, integrate, and expect uncertainty information. In this chapter, we outline sources and models of uncertainty, give an overview of the state-of-the-art, provide general guidelines, outline small exemplary applications, and finally, discuss open problems in uncertainty visualization.
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David A. Boyuka II, Sriram Lakshminarasimhan, Xiaocheng Zou, Zhenhuan Gong, John Jenkins, Eric R. Schendel, Norbert Podhorszki, Qing Liu, Scott Klasky,, Nagiza F. Samatova..
“ Transparent in situ data transformations in ADIOS,” In Proc. Cluster, Cloud and Grid Computing (CCGrid), May, 2014.
P.-T. Bremer, I. Hotz, V. Pascucci, R. Peikert.
“Topological Methods in Data Analysis and Visualization III,” Mathematics and Visualization, 2014.
ISBN: Bre2014a
H. Bui, V. Vishwanath, H. Finkel, K. Harms, J. Leigh, S. Habib, K. Heitmann, M. E. Papka.
“Scalable parallel I/O on Blue Gene/Q supercomputer using compression, topology-aware data aggregation, and subfiling,” In Proceedings of the 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP 2014), Turin, Italy, February, 2014.
H. Bui, E.S. Jung, V. Vishwanath, J. Leigh, M. Papka.
“Improving Data Movement Performance for Sparse Data Patterns on Blue Gene/Q Supercomputer,” In 7th International Workshop on Parallel Programming Models and Systems Software for High-End Computing (P2S2) held in conjunction with the 43rd International Conference on Parallel Processing, Minneapolis, Minnesota, USA, September, 2014.
A. Chaudhuri, T.-Y. Lee, H.-W. Shen, R. Wenger.
“Exploring Flow Fields Using Space-filling Analysis of Streamlines,” In IEEE Transactions on Visualization and Computer Graphics (TVCG), In IEEE, No. 99, 2014.
DOI: 10.1109/TVCG.2014.2312009
Large scale scientific simulations frequently use streamline based techniques to visualize flow fields. As the shape of a streamline is often related to some underlying property of the field, it is important to identify streamlines (or their parts) with unique geometric features. In this paper, we introduce a metric, called the box counting ratio, which measures the geometric complexity of streamlines by measuring their space-filling capacity at different scales.We propose a novel interactive visualization framework which utilizes this metric to extract, organize and visualize features of varying density and complexity hidden in large numbers of streamlines. The proposed framework extracts complex regions of varying density from the streamlines, and organizes and presents them on an interactive 2D information space, allowing user selection and visualization of streamlines. We also extend this framework to support exploration using an ensemble of measures including box counting ratio. Our framework allows the user to easily visualize and interact with features otherwise hidden in large vector field data. We strengthen our claims with case studies using combustion and climate simulation data sets.
A. Chaudhuri, T.-H. Wei, T.-Y. Lee, H.-W. Shen, T. Peterka.
“Efficient Range Distribution Query for Visualizing Scientific Data,” In Proceedings of the 2014 IEEE Pacific Visualization Symposium (PacificVis), 2014.
B. Chapman, H. Calandra, S. Crivelli, J. Dongarra, J. Hittinger, C.R. Johnson, S.A. Lathrop, V. Sarkar, E. Stahlberg, J.S. Vetter, D. Williams.
“ASCAC Workforce Subcommittee Letter,” Subtitled “DOE ASCAC Committee Report,” 2014.
Simulation and computing are essential to much of the research conducted at the DOE national laboratories. Experts in the ASCR-relevant Computing Sciences, which encompass a range of disciplines including Computer Science, Applied Mathematics, Statistics and domain sciences, are an essential element of the workforce in nearly all of the DOE national laboratories. This report seeks to identify the gaps and challenges facing DOE with respect to this workforce.
The DOE laboratories provided the committee with information on disciplines in which they experienced workforce gaps. For the larger laboratories, the majority of the cited workforce gaps were in the Computing Sciences. Since this category spans multiple disciplines, it was difficult to obtain comprehensive information on workforce gaps in the available timeframe. Nevertheless, five multi-purpose laboratories provided additional relevant data on recent hiring and retention.
Data on academic coursework was reviewed. Studies on multidisciplinary education in Computational Science and Engineering (CS&E) revealed that, while the number of CS&E courses offered is growing, the overall availability is low and the coursework fails to provide skills for applying CS&E to real-world applications. The number of graduates in different fields within Computer Science (CS) and Computer Engineering (CE) was also reviewed, which confirmed that specialization in DOE areas of interest is less common than in many other areas.
Projections of industry needs and employment figures (mostly for CS and CE) were examined. They indicate a high and increasing demand for graduates in all areas of computing, with little unemployment. This situation will be exacerbated by large numbers of retirees in the coming decade. Further, relatively few US students study toward higher degrees in the Computing Sciences, and those who do are predominantly white and male. As a result of this demographic imbalance, foreign nationals are an increasing fraction of the graduate population and we fail to benefit from including women and underrepresented minorities.
There is already a program that supports graduate education that is tailored to the needs of the DOE laboratories. The Computational Science Graduate Fellowship (CSGF) enables graduates to pursue a multidisciplinary program of education that is coupled with practical experience at the laboratories. It has been demonstrated to be highly effective in both its educational goals and in its ability to supply talent to the laboratories. However, its current size and scope are too limited to solve the workforce problems identified. The committee felt strongly that this proven program should be extended to increase its ability to support the DOE mission.
Since no single program can eliminate the workforce gap, existing recruitment efforts by the laboratories were examined. It was found that the laboratories already make considerable effort to recruit in this area. Although some challenges, such as the inability to match industry compensation, cannot be directly addressed, DOE could develop a roadmap to increase the impact of individual laboratory efforts, to enhance the suitability of existing educational opportunities, to increase the attractiveness of the laboratories, and to attract and sustain a full spectrum of human talent, which includes women and underrepresented minorities.
Zhengzhang Chen, Seung Woo Son, William Hendrix, Ankit Agrawal, Wei-keng Liao, Alok Choudhary.
“NUMARCK: Machine Learning Algorithm for Resiliency and Checkpointing,” In the International Conference for High Performance Computing, Networking, Storage and Analysis, November, 2014.
Hsuan-Te Chiu, Jerry Chou, Venkat Vishwanath, Suren Byna,, Kesheng Wu,.
“Simplifying Index File Structure to Improve I/O Performance of Parallel Indexing,” In The 20th IEEE International Conference on Parallel and Distributed Systems (ICPADS 2014), 2014.
Hank Childs, Scott Biersdorff, David Poliakoff, David Camp, Allen D. Malony.
“Particle Advection Performance Over Varied Architectures and Workloads,” In 21th Annual International Conference on High Performance Computing, HiPC 2014, goa, india, dec, 2014.
Dong Dai, Robert B. Ross, Philip Carns, Dries Kimpe, Yong Chen.
“Using Property Graphs for Rich Metadata Management in HPC Systems,” In Proceedings of the 9th Parallel Data Storage Workshop, IEEE, 11, 2014.
Ciprian Docan, Fan Zhang, Tong Jin, Hoang Bui, Qian Sun, Julian Cummings, Norbert Podhorszki, Scott Klasky, Manish Parashar.
“ActiveSpaces: Exploring dynamic code deployment for extreme scale data processing,” In Concurrency and Computation: Practice and Experience, 2014.
Managing the large volumes of data produced by emerging scientific and engineering simulations running on leadership-class resources has become a critical challenge. The data have to be extracted off the computing nodes and transported to consumer nodes so that it can be processed, analyzed, visualized, archived, and so on. Several recent research efforts have addressed data-related challenges at different levels. One attractive approach is to offload expensive input/output operations to a smaller set of dedicated computing nodes known as a staging area. However, even using this approach, the data still have to be moved from the staging area to consumer nodes for processing, which continues to be a bottleneck. In this paper, we investigate an alternate approach, namely moving the data-processing code to the staging area instead of moving the data to the data-processing code. Specifically, we describe the ActiveSpaces framework, which provides (1) programming support for defining the data-processing routines to be downloaded to the staging area and (2) runtime mechanisms for transporting codes associated with these routines to the staging area, executing the routines on the nodes that are part of the staging area, and returning the results. We also present an experimental performance evaluation of ActiveSpaces using applications running on the Cray XT5 at Oak Ridge National Laboratory. Finally, we use a coupled fusion application workflow to explore the trade-offs between transporting data and transporting the code required for data processing during coupling, and we characterize sweet spots for each option.
Steffen Frey, Filip Sadlo, Kwan-Liu Ma,, Thomas Ertl.
“Interactive Progressive Visualization with Space-Time Error Control,” In Proceedings of IEEE SciVis 2014 (also IEEE TVCG 20(12)), November, 2014.
M.G. Genton, C.R. Johnson, K. Potter, G. Stenchikov, Y. Sun.
“Surface boxplots,” In Stat Journal, Vol. 3, No. 1, pp. 1--11. 2014.
In this paper, we introduce a surface boxplot as a tool for visualization and exploratory analysis of samples of images. First, we use the notion of volume depth to order the images viewed as surfaces. In particular, we define the median image. We use an exact and fast algorithm for the ranking of the images. This allows us to detect potential outlying images that often contain interesting features not present in most of the images. Second, we build a graphical tool to visualize the surface boxplot and its various characteristics. A graph and histogram of the volume depth values allow us to identify images of interest. The code is available in the supporting information of this paper. We apply our surface boxplot to a sample of brain images and to a sample of climate model outputs.
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.
C.D. Hansen, M. Chen, C.R. Johnson, A.E. Kaufman, H. Hagen (Eds.).
“Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization,” In Mathematics and Visualization, Springer, 2014.
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.
ISSN: 1386-7857
DOI: 10.1007/s10586-014-0358-z
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.
Ruoqian Liu,, Ankit Agrawal, Wei-keng Liao,, Alok Choudhary.
“Search Space Preprocessing in Solving Complex Optimization Problems,” In the Workshop on Complexity for Big Data held in conjunction with the IEEE International Conference on Big Data, Washington, D.C., U.S.A., October, 2014.
Kewei Lu, Han-Wei Shen, Tom Peterka.
“Scalable Computation of Stream Surfaces on Large Scale Vector Field,” In ACM/IEEE SC' 14, 2014.
D. Maljovec, Bei Wang, J. Moeller, V. Pascucci.
“Topology-Based Active Learning,” SCI Technical Report, No. UUSCI-2014-001, SCI Institute, University of Utah, 2014.
A common problem in simulation and experimental research involves obtaining time-consuming, expensive, or potentially hazardous samples from an arbitrary dimension parameter space. For example, many simulations modeled on supercomputers can take days or weeks to complete, so it is imperative to select samples in the most informative and interesting areas of the parameter space. In such environments, maximizing the potential gain of information is achieved through active learning (adaptive sampling). Though the topic of active learning is well-studied, this paper provides a new perspective on the problem. We consider topologybased batch selection strategies for active learning which are ideal for environments where parallel or concurrent experiments are able to be run, yet each has a heavy cost. These strategies utilize concepts derived from computational topology to choose a collection of locally distinct, optimal samples before updating the surrogate model. We demonstrate through experiments using a several different batch sizes that topology-based strategies have comparable and sometimes superior performance, compared to conventional approaches.
D. Maljovec, S. Liu, Bei Wang, V. Pascucci, P.-T. Bremer, D. Mandelli, C. Smith.
“Analyzing Simulation-Based PRA Data Through Clustering: a BWR Station Blackout Case Study,” In Proceedings of the Probabilistic Safety Assessment & Management conference (PSAM), 2014.
Dynamic probabilistic risk assessment (DPRA) methodologies couple system simulator codes (e.g., RELAP, MELCOR) with simulation controller codes (e.g., RAVEN, ADAPT). Whereas system simulator codes accurately model system dynamics deterministically, simulation controller codes introduce both deterministic (e.g., system control logic, operating procedures) and stochastic (e.g., component failures, parameter uncertainties) elements into the simulation. Typically, a DPRA is performed by 1) sampling values of a set of parameters from the uncertainty space of interest (using the simulation controller codes), and 2) simulating the system behavior for that specific set of parameter values (using the system simulator codes). For complex systems, one of the major challenges in using DPRA methodologies is to analyze the large amount of information (i.e., large number of scenarios ) generated, where clustering techniques are typically employed to allow users to better organize and interpret the data. In this paper, we focus on the analysis of a nuclear simulation dataset that is part of the risk-informed safety margin characterization (RISMC) boiling water reactor (BWR) station blackout (SBO) case study. We apply a software tool that provides the domain experts with an interactive analysis and visualization environment for understanding the structures of such high-dimensional nuclear simulation datasets. Our tool encodes traditional and topology-based clustering techniques, where the latter partitions the data points into clusters based on their uniform gradient flow behavior. We demonstrate through our case study that both types of clustering techniques complement each other in bringing enhanced structural understanding of the data.
Keywords: PRA, computational topology, clustering, high-dimensional analysis
D. Mandelli, C. Smith, T. Riley, J. Nielsen, J. Schroeder, C. Rabiti, A. Alfonsi, J. Cogliati, R. Kinoshita, V. Pascucci, Bei Wang, D. Maljovec.
“Overview of New Tools to Perform Safety Analysis: BWR Station Black Out Test Case,” In Proceedings of the Probabilistic Safety Assessment & Management conference (PSAM), 2014.
The existing fleet of nuclear power plants is in the process of extending its lifetime and increasing the power generated from these plants via power uprates. In order to evaluate the impacts of these two factors on the safety of the plant, the Risk Informed Safety Margin Characterization project aims to provide insights to decision makers through a series of simulations of the plant dynamics for different initial conditions (e.g., probabilistic analysis and uncertainty quantification). This paper focuses on the impacts of power uprate on the safety margin of a boiling water reactor for a station black-out event. Analysis is performed by using a combination of thermal-hydraulic codes and a stochastic analysis tool currently under development at the Idaho National Laboratory, i.e. RAVEN. We employed both classical statistical tools, i.e. Monte-Carlo, and more advanced machine learning based algorithms to perform uncertainty quantification in order to quantify changes in system performance and limitations as a consequence of power uprate. We also employed advanced data analysis and visualization tools that helped us to correlate simulation outcomes such as maximum core temperature with a set of input uncertain parameters. Results obtained give a detailed investigation of the issues associated with a plant power uprate including the effects of station black-out accident scenarios. We were able to quantify how the timing of specific events was impacted by a higher nominal reactor core power. Such safety insights can provide useful information to the decision makers to perform risk-informed margins management.
Robert Miller, Kenneth Moreland, Kwan-Liu Ma.
“Finely-Threaded History-Based Topology Computation,” In Eurographics Symposium on Parallel Graphics and Visualization, pp. 41--48. June, 2014.
DOI: 10.2312/pgv.20141083
H. Obermaier, K. I. Joy.
“Future Challenges for Ensemble Visualization,” In IEEE Computer Graphics and Applications, Vol. 34, No. 3, IEEE, pp. 8--11. May/June, 2014.
The simulation of complex events is a challenging task and often requires careful selection of simulation parameters. With the availability of vast computation resources, it has become possible to run several alternative parameter settings or simulation models in parallel, creating an ’ensemble’ of possible outcomes for a given event of interest. Recently, the visual analysis of such ensemble data has repeatedly come up as one of the most important new areas of visualization and it is expected to have a wide impact on the field of visualization in the next few years. The main challenge is to develop expressive visualizations of properties of this set of solutions, the ensemble, to support scientists in this challenging parameter-space exploration task. This paper presents and explores future challenges for ensemble visualization.
Keywords: ensemble visualization, computational steering
Diana Palsetia, Mostofa Patwary, William Hendrix, Ankit Agrawal,, Alok Choudhary.
“Clique Guided Community Detection,” In the IEEE International Conference on Big Data, Washington, DC, IEEE, pp. 500 - 509. October, 2014.
DOI: 10.1109/BigData.2014.7004267
V. Pascucci, P.-T. Bremer, H. Bhatia.
“The Natural Helmholtz-Hodge Decomposition For Open-Boundary Flow Analysis,” In IEEE Transactions on Visualization and Computer Graphics (TVCG), Vol. 99, pp. 1. 2014.
DOI: 10.1109/TVCG.2014.2312012
The Helmholtz-Hodge decomposition (HHD) describes a flow as the sum of an incompressible, an irrotational, and a harmonic flow, and is a fundamental tool for simulation and analysis. Unfortunately, for bounded domains, the HHD is not uniquely defined, and traditionally, boundary conditions are imposed to obtain a unique solution. However, in general, the boundary conditions used during the simulation may not be known and many simulations use open boundary conditions. In these cases, the flow imposed by traditional boundary conditions may not be compatible with the given data, which leads to sometimes drastic artifacts and distortions in all three components, hence producing unphysical results. Instead, this paper proposes the natural HHD, which is defined by separating the flow into internal and external components. Using a completely data-driven approach, the proposed technique obtains uniqueness without assuming boundary conditions a priori. As a result, it enables a reliable and artifact-free analysis for flows with open boundaries or unknown boundary conditions. Furthermore, our approach computes the HHD on a point-wise basis in contrast to the existing global techniques, and thus supports computing inexpensive local approximations for any subset of the domain. Finally, the technique is easy to implement for a variety of spatial discretizations and interpolated fields in both two and three dimensions.
Peterka, T., Morozov, D., Phillips, C..
“High-Performance Computation of Distributed-Memory Parallel 3D Voronoi and Delaunay Tessellation,” In Proceedings of SC14, New Orleans, LA, 2014.
N. Podhorszki, S. Klasky, Q. Liu, Y. Tian, M. Parashar, K. Schwan, M. Wolf, S. Lakshminarasimhan.
“ADIOS,” In High Performance Parallel I/O, Edited by Prabhat, Q. Koziol, Taylor and Francis, 2014.
David Pugmire, James Kress, Jeremy Meredith, Norbert Podhorszki, Jong Choi, Scott Klasky.
“Towards scalable visualization plugins for data staging workflows,” In Big Data Analytics: Challenges and Opportunities (BDAC-14) Workshop at Supercomputing Conference, Nov, 2014.
S. Rizzi, M. Hereld, J. Insley, M. Papka, T. Uram,, V. Vishwanath.
“Performance Modeling of vl3 Volume Rendering on GPU-Based Clusters,” In Proceedings of the 14th Eurographics Symposium on Parallel Graphics and Visualization (EGPGV), Swansea, Wales, UK, June 9-10, 2014.
S. Rizzi, M. Hereld, J. Insley, M. Papka, T. Uram,, V. Vishwanath.
“Large-Scale Parallel Visualization of Particle Datasets using Point Sprites,” In Proceedings of the IEEE/ACM International Conference for High Performance Computing, Networking, Storage and Analysis (SC 2014), New Orleans, LA [Poster]., November, 2014.
R. Ross, D. Kimpe.
“Storage Models: Past, Present, and Future,” In High Performance Parallel I/O, CRC Press, Taylor and Francis Group, October, 2014.
Oliver Ruebel, Cameron G.R. Geddes, Min Chen, Estelle Cormier-Michel, E. Wes Bethel.
“Feature-based Analysis of Plasma-based Particle Acceleration Data,” In IEEE Transactions on Visualization and Computer Graphics (TVCG), In IEEE Transactions on Visualization and Computer Graphics, July, Vol. 20, No. 2, February, 2014.
Plasma-based particle accelerators can produce and sustain thousands of times stronger acceleration fields than conventional particle accelerators, providing a potential solution to the problem of the growing size and cost of conventional particle accelerators. To facilitate scientific knowledge discovery from the ever growing collections of accelerator simulation data generated by accelerator physicists to investigate next-generation plasma-based particle accelerator designs, we describe a novel approach for automatic detection and classification of particle beams and beam substructures due to temporal differences in the acceleration process, here called acceleration features. The automatic feature detection in combination with a novel visualization tool for fast, intuitive, query-based exploration of acceleration features enables an effective top-down data exploration process, starting from a high-level, feature- based view down to the level of individual particles. We describe the application of our analysis in practice to analyze simulations of single pulse and dual and triple colliding pulse accelerator designs, and to study the formation and evolution of particle beams, to compare substructures of a beam and to investigate transverse particle loss.
A.R. Sanderson.
“An Alternative Formulation of Lyapunov Exponents for Computing Lagrangian Coherent Structures,” In Proceedings of the 2014 IEEE Pacific Visualization Symposium (PacificVis), Yokahama Japan, 2014.
Lagrangian coherent structures are time-evolving surfaces that highlight areas in flow fields where neighboring advected particles diverge or converge. The detection and understanding of such structures is an important part of many applications such as in oceanography where there is a need to predict the dispersion of oil and other materials in the ocean. One of the most widely used tools for revealing Lagrangian coherent structures has been to calculate the finite-time Lyapunov exponents, whose maximal values appear as ridgelines to reveal Lagrangian coherent structures. In this paper we explore an alternative formulation of Lyapunov exponents for computing Lagrangian coherent structures.
Franz Sauer, Hongfeng Yu,, Kwan-Liu Ma.
“Trajectory-based Flow Feature Tracking in Joint Particle/Volume Datasets,” In Proceedings of IEEE SciVis 2014 (also IEEE TVCG 20(12)), November, 2014.
Sohail Shafii, Harald Obermaier, Bernd Hamann, Kenneth I. Joy.
“Topological Features in Glyph-Based Corotation Visualization,” In Topological Methods in Data Analysis and Visualization III - Theory, Algorithms, and Applications, Mathematics and Visualization, Springer International Publishing, pp. 263--276. 2014.
ISBN: 978-3-319-04098-1
DOI: 10.1007/978-3-319-04099-8_17
We introduce a novel method for vortex detection in flow fields based on the corotation of line segments and glyph rendering. The corotation measure is defined as a point-symmetric scalar function on a sphere, suitable for direct representation in the form of a three-dimensional glyph. Appropriate placement of these glyphs in the domain of a flow field makes it possible to depict vortical features present in the flow. We demonstrate how topological analysis of this novel glyph-based representation of vortex features can reveal vortex characteristics that lie beyond the capabilities of visualization techniques that consider vortex direction and magnitude information only.
Min Shih, Yubo Zhang, Kwan-Liu Ma, Jayanarayanan Sitaraman,, Dimitri J. Mavriplis.
“Out-of-Core Visualization of Time-Varying Hybrid-Grid Volume Data,” In Proceedings of LDAV, November, 2014.
Solomon Lasluisa, Fan Zhang, Tong Jin, Ivan Rodero, Hoang Bui, Manish Parashar.
“In-situ feature-based objects tracking for data-intensive scientific and enterprise analytics workflows,” In Cluster Computing, 2014.
Seung Woo Son, Zhengzhang Chen, William Hendrix, Ankit Agrawal, Wei-keng Liao, Alok Choudhary.
“Data Compression for the Exascale Computing Era - Survey,” In Supercomputing frontiers and innovations, Vol. 1, No. 2, pp. 76-88. September, 2014.
ISSN: 2313-8734
DOI: 10.14529/jsfi140205
Qian Sun, Fan Zhang, Tong Jin, Hoang Bui, Kesheng Wu, Arie Shoshani, Hemanth Kolla, Scott Klasky, Jacqueline Chen, Manish Parashar.
“Scalable Run-time Data Indexing and Querying for Scientific Simulations,” In Big Data Analytics: Challenges and Opportunities (BDAC-14) Workshop at Supercomputing Conference, November, 2014.
Houjun Tang, Xiaocheng Zou, John Jenkins, David A. Boyuka II, Stephen Ranshous, Dries Kimpe, Scott Klasky,, Nagiza F. Samatova.
“Improving Read Performance with Online Access Pattern Analysis and Prefetching,” In Proc. of 20th International European Conference on Parallel Processing, 2014.
Among the major challenges of transitioning to exascale in HPC is the ubiquitous I/O bottleneck. For analysis and visualization applications in particular, this bottleneck is exacerbated by the write-once-read-many property of most scientific datasets combined with typically complex access patterns. One promising way to alleviate this problem is to recognize the application’s access patterns and utilize them to prefetch data, thereby overlapping computation and I/O. However, current research methods for analyzing access patterns are either offline-only and/or lack the support for complex access patterns, such as high-dimensional strided or composition-based unstructured access patterns.Therefore, we propose an online analyzer capable of detecting both simple and complex access patterns with low computational and memory overhead and high accuracy. By combining our pattern detection with prefetching, we consistently observe run-time reductions, up to 26%, across 18 configurations of PIO-Bench and 4 configurations of a micro- benchmark with both structured and unstructured access patterns.
V. Vishwanath, H. Bui, M. Hereld, M. Papka.
“GLEAN,” In High Performance Parallel I/O, CRC Press, Taylor and Francis Group, October, 2014.
Gunther H. Weber, Hans Johansen, Daniel T. Graves, Terry J. Ligocki.
“Simulating Urban Environments for Energy Analysis,” In Proceedings Visualization in Environmental Sciences 2014 (EnvirVis 2014), 2014.
Weber, G. H., Hauser, H..
“Interactive visual exploration and analysis,” In Scientific Visualization: Uncertainty, Multifield, Bio-Medical and Scalable Visualization, Mathematics and Visualization (Hansen, C. D., Chen, M., Johnson, C. R., Kaufman, A. E., and Hagen, H., eds.), Springer-Verlag, pp. 161–174. 2014.
Wathsala Widanagamaachchi, Peer-Timo Bremer, Christopher Sewell, Li-ta Lo, James Ahrens,, Valerio Pascucci.
“Data-Parallel Halo Finding with Variable Linking Lengths,” In Proceedings of the IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV), Paris, France, November, 2014.
Pak Chung Wong, Han-Wei Shen, Ruby Leung, Samson Hagos, Teng-Yok Lee, Xin Tong, Kewei Lu.
“Visual Analytics of Large-Scale Climate Model Data,” In IEEE Syposium on Large Data Analysis and Visualization (LDAV 2014), October, 2014.
Yanwei Zhang, Qing Liu, Scott Klasky, Matthew Wolf, Karsten Schwan, Gregg Eisenhauer, Jong Choi, Norbert Podhorszki.
“Active Workflow System for Near Real-Time Extreme-Scale Science,” In PPoPP Workshop on Parallel Programming for Analytics Applications , 2014.
L. Zhang, Q. Deng, R. Machiraju, A. Rangaragjan, D. Thompson, D. K. Walters, Han-Wei Shen.
“Boosting Techniques for Physics-Based Vortex Detection,,” In Computer Graphics Forum, Vol. 33, Issue 1, pp. 282-293. February, 2014.
L. Zhou, C.D. Hansen.
“GuideME: Slice-guided Semiautomatic Multivariate Exploration of Volumes,” In Proceedings of the Eurographics Conference on Visualization (EuroVis) 2014, Vol. 33, No. 3, 2014.
Multivariate volume visualization is important for many applications including petroleum exploration and medicine. State-of-the-art tools allow users to interactively explore volumes with multiple linked parameter-space views. However, interactions in the parameter space using trial-and-error may be unintuitive and time consuming. Furthermore, switching between different views may be distracting. In this paper, we propose GuideME: a novel slice-guided semiautomatic multivariate volume exploration approach. Specifically, the approach comprises four stages: attribute inspection, guided uncertainty-aware lasso creation, automated feature extraction and optional spatial fine tuning and visualization. Throughout the exploration process, the user does not need to interact with the parameter views at all and examples of complex real-world data demonstrate the usefulness, efficiency and ease-of-use of our method.
Xiaocheng Zou, Sriram Lakshminarasimhan , David A. Boyuka II, Stephen Ranshous, Houjun Tang, Scott Klasky ,, Nagiza F. Samatova.
“Fast Set Intersection through Run-time Bitmap Construction over PForDelta-compressed Indexes,” In Proc. of 20th International European Conference on Parallel Processing (EuroPar), 2014.
Set intersection is a fundamental operation for evaluating conjunctive queries in the context of scientific data analysis. The state-of-the-art approach in performing set intersection, compressed bitmap indexing, achieves high computa- tional efficiency because of cheap bitwise operations; however, overall efficiency is often nullified by the HPC I/O bottleneck, because compressed bitmap indexes typically exhibit a heavy storage footprint. Conversely, the recently-presented PForDelta-compressed index has been demonstrated to be storage-lightweight, but has limited performance for set intersection. Thus, a more effective set inter- section approach should be efficient in both computation and I/O. Therefore, we propose a fast set intersection approach that couples the storage light-weight PForDelta indexing format with computationally-efficient bitmaps through a specialized on-the-fly conversion. The resultant challenge is to ensure this conversion process is fast enough to maintain the performance gains from both PForDelta and the bitmaps. To this end, we contribute two key enhancements to PForDelta, BitRun and BitExp, which improve bitmap conversion through bulk bit-setting and a more streamlined PForDelta decoding process, respectively. Our experimental results show that our integrated PForDelta-bitmap method speeds up conjunctive queries by up to 7.7x versus the state-of-the-art approach, while using indexes that require 15%-60% less storage in most cases.
2013
A. Agranovsky, H. Obermaier, K. I. Joy.
“A Framework for the Visualization of Finite-Time Continuum Mechanics Effects in Time-Varying Flow,” In 9th International Symposium on Visual Computing (ISVC 2013), 2013.
S. Ahern, E. Brugger, B. Whitlock, J. S. Meredith, K. Biagas, M. C. Miller, H. Childs.
“VisIt: Experiences with Sustainable Software,” In Workshop on Sustainable Software for Science: Practice and Experiences, IEEE Supercomputing 2013, Nov, 2013.
B. Behzad, H. Luu, J. Huchette, S. Byna, Prabhat, R. Aydt, Q. Koziol,, M. Snir.
“Taming Parallel I/O Complexity with Auto-Tuning,” In SC13, Nov, 2013.
E. Wes Bethel, Prabhat, Suren Byna, Oliver Rbel, K. John Wu, Michael Wehner.
“Why High Performance Visual Data Analytics is both Relevant and Difficult,” In Proceedings of Visualization and Data Analysis 2013, IS&T/SPIE Electronic Imaging, San Francisco, CA, USA, 2013.
H. Bhatia, G. Norgard, V. Pascucci,, P.-T. Bremer.
“The Helmholtz-Hodge decomposition- a survey,” In IEEE Transactions on Visualization and Computer Graphics (TVCG), Vol. 19, No. 8, pp. 1386--1404. 2013.
H. Bhatia, G. Norgard, V. Pascucci, P.-T. Bremer.
“Comments on the Meshless Helmholtz-Hodge decomposition,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 19, No. 3, pp. 527--528. March, 2013.
DOI: 10.1109/TVCG.2012.62
Harsh Bhatia, Attila Gyulassy, Hao Wang, Peer-Timo Bremer, Valerio Pascucci.
“Robust Detection of Singularities in Vector Fields,” In Topological Methods in Data Analysis and Visualizatuion (TopoInVis) , 2013.
H. Bhatia, B. Wang, G. Norgard, V. Pascucci, P.-T. Bremer.
“Local, Smooth, and Consistent Jacobi Set Simplification,” In Cornell University Library, No. arXiv:1307.7752, 2013.
A. Biswas, S. Dutta, H.-W. Shen, J. Woodring.
“An information-aware framework for exploring multivariate data sets,” In IEEE Transactions on Visualization and Computer Graphics (TVCG), Vol. 19, No. 12, pp. 2683--2692. 2013.
C. Brownlee, T. Ize, C.D. Hansen.
“Image-parallel Ray Tracing using OpenGL Interception,” In Proceedings of the Eurographics Symposium on Parallel Graphics and Visualization (2013), pp. 65--72. 2013.
S. Byna, A. Uselton, Prabhat, D. Knaak,, Y. He.
“Trillion Particles, 120,000 cores, and 350 TBs: Lessons Learned from a Hero I/O Run on Hopper,” Cray User Group meeting, 2013.
David Camp, E. Wes Bethel, Hank Childs.
“Transitioning Data Flow-Based Visualization Software to Multi-Core Hybrid Parallelism,” In 3rd International Workshop on Data-Flow Execution Models for Extreme Scale Computing (DFM 2013), September, 2013.
P. Carns, Y. Yao, K. Harms, R. Latham, R. Ross, K. Antypas.
“Production I/O Characterization on the Cray XE6,” In Proceedings of the Cray User Group meeting 2013 (CUG 2013), May, 2013.
Yu-Hsuan Chan, Carlos Correa,, Kwan-Liu Ma.
“The Generalized Sensitivity Scatterplot,” In IEEE Transactions on Visualization and Computer Graphics, 2013.
J. Chandler, H. Obermaier, K. I. Joy.
“Illustrative Rendering of Particle Systems,” In Proc. of Vision, Modeling, and Visualization (VMV 2013), pp. 177-185. 2013.
J. Chen, A. Choudhary, S. Feldman, B. Hendrickson, C.R. Johnson, R. Mount, V. Sarkar, V. White, D. Williams.
“Synergistic Challenges in Data-Intensive Science and Exascale Computing,” DOE ASCAC Data Subcommittee Report, Department of Energy Office of Science, March, 2013.
Chun-Ming Shen, Han-Wei Shen.
“Graph-based Seed Scheduling for Out-of-core FTLE and Pathline Computation ,” In IEEE 2013 Large Data Analysis and Visualization , Atlanta, GA, IEEE, 2013.
C. Chen, H.-W. Shen.
“Graph-based seed scheduling for out-of-core FTLE and pathline computation,” In Proceedings of the 2013 IEEE LDAV Symposium, Note: Received Honorable Mention, pp. 15--32. October, 2013.
F. Chen, H. Obermaier, H. Hagen, B. Hamann, J. Tierny, V. Pascucci.
“Topology analysis of time-dependent multi-fluid data using the Reeb graph,” In Computer Aided Geometric Design, Vol. 30, No. 6, pp. 557--566. 2013.
DOI: 10.1109/TVCG.2012.62
Liquid–liquid extraction is a typical multi-fluid problem in chemical engineering where two types of immiscible fluids are mixed together. Mixing of two-phase fluids results in a time-varying fluid density distribution, quantitatively indicating the presence of liquid phases. For engineers who design extraction devices, it is crucial to understand the density distribution of each fluid, particularly flow regions that have a high concentration of the dispersed phase. The propagation of regions of high density can be studied by examining the topology of isosurfaces of the density data. We present a topology-based approach to track the splitting and merging events of these regions using the Reeb graphs. Time is used as the third dimension in addition to two-dimensional (2D) point-based simulation data. Due to low time resolution of the input data set, a physics-based interpolation scheme is required in order to improve the accuracy of the proposed topology tracking method. The model used for interpolation produces a smooth time-dependent density field by applying Lagrangian-based advection to the given simulated point cloud data, conforming to the physical laws of flow evolution. Using the Reeb graph, the spatial and temporal locations of bifurcation and merging events can be readily identified supporting in-depth analysis of the extraction process.
Hank Childs, Berk Geveci, Will Schroeder, Jeremy Meredith, Kenneth Moreland, Christopher Sewell, Torsten Kuhlen, E. Wes Bethel.
“Research Challenges for Visualization Software,” In IEEE Computer, Vol. 46, No. 5, pp. 34--42. May, 2013.
DOI: 10.1109/MC.2013.179
M. Gamell, I. Rodero, M. Parashar, J.C. Bennett, H. Kolla, J. Chen, P. Bremer, A.G. Landge, A. Gyulassy, P. McCormick, S. Pakin, V. Pascucci.
“Exploring Power Behaviors and Tradeoffs of In-situ Data Analytics,” In ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), Denver, Colorado, U.S.A, No. 77, November, 2013.
ISBN: 978-1-4503-2378-9
DOI: 10.1145/2503210.2503303
Marc Gamell, Ivan Rodero, Manish Parashar, Stephen W. Poole.
“Exploring Energy and Performance Behaviors of Data-Intensive Scientific Workflows on Systems with Deep Memory Hierarchies,” In IEEE International Conference on High Performance Computing (HiPC), December, 2013.
S. Gerber, O. Reubel, P.-T. Bremer, V. Pascucci, R.T. Whitaker.
“Morse-Smale Regression,” In Journal of Computational and Graphical Statistics, Vol. 22, No. 1, pp. 193-214. 2013.
Z. Gong, D. A. Boyuka II, X. Zou, Q. Liu, N. Podhorszki, S. Klasky, X. Ma, N. F. Samatova.
“PARLO: PArallel Run-time Layout Optimization for Scientific Data Explorations with Heterogeneous Access Patterns,” 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), Delft, The Netherlands, May, 2013.
L. Gosink, K. Bensema, T. Pulsipher, H. Obermaier, M. Henry, H. Childs, K. I. Joy.
“Characterizing and Visualizing Predictive Uncertainty in Numerical Ensembles Through Bayesian Model Averaging,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 19, No. 12, pp. 2703-2712. 2013.
A. Grosset, M. Schott, G.-P. Bonneau, C.D. Hansen.
“Evaluation of Depth of Field for Depth Perception in DVR,” In Proceedings of the 2013 IEEE Pacific Visualization Symposium (PacificVis), pp. 81--88. 2013.
S. Habib, V. Morozov, N. Frontiere, H. Finkel, A. Pope, K. Heitmann, K. Kumaran, V. Vishwanath, T. Peterka, J. Insley, D. Daniel, P. Fasel, Z. Lukic.
“HACC: Extreme Scaling and Performance Across Diverse Architectures,” In the Proceedings of the IEEE/ACM International Conference for High Performance Computing, Networking, Storage and Analysis (SC 2013), Denver, Colorado, USA, November 2013, Note: Gordon Bell Finalist, 2013.
M. Hall, R.M. Kirby, F. Li, M.D. Meyer, V. Pascucci, J.M. Phillips, R. Ricci, J. Van der Merwe, S. Venkatasubramanian.
“Rethinking Abstractions for Big Data: Why, Where, How, and What,” In Cornell University Library, No. arXiv:1306.3295, 2013.
W. Hendrix, D. Palsetia, M. M. A. Patwary, A. Agrawal, W.-k. Liao, A. Choudhary.
“A Scalable Algorithm for Single-Linkage Hierarchical Clustering on Distributed Memory Architectures,” In Proceedings of 3rd IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV), 2013.
S Herbein, M Matheny, Matthew Wezowicz, J Krogel, Jeremy Logan, J Kim, Scott Klasky, Michela Taufer.
“Performance Impact of I/O on QMCPack Simulations at the Petascale and Beyond,” In Computational Science and Engineering (CSE), 2013.
T. Hollt, A. Magdy, G. Chen, G. Gopalakrishnan, I. Hoteit, C.D. Hansen, M. Hadwiger.
“Visual Analysis of Uncertainties in Ocean Forecasts for Planning and Operation of Off-Shore Structures,” In Proceedings of IEEE Pacific Visualization 2013, Note: Received Honerable Mention, 2013.
M. Hummel, H. Obermaier, C. Garth, K. I. Joy.
“Comparative Visual Analysis of Lagrangian Transport in CFD Ensembles,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 19, No. 12, Note: Recieved Best Paper award, pp. 2743-2752. 2013.
J. Jenkins, I. Arkatkar, S. Lakshminarasimhan, D. A. Boyuka II, E. R. Schendel, N. Shah, S. Ethier, C.S. Chang, J. Chen, H. Kolla, S. Klasky, R. Ross,, N. F. Samatova.
“ALACRITY: Analytics-driven Lossless Data Compression for Rapid In-situ Indexing, Storing, and Querying,” In LNCS Transactions on Large-Scale Data- and Knowledge-Centered Systems (TLDKS), 2013.
T. Jin, F. Zhang, Q. Sun, H. Bui, M. Parashar, H. Yu, S. Klasky, N. Podhorszki, H. Abbasi.
“Using Cross-Layer Adaptations for Dynamic Data Management in Large Scale Coupled Scientific Workflows,” In ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), Denver, Colorado, U.S.A, November, 2013.
C. Jin, M. M. A. Patwary, A. Agrawal, W. Hendrix, W.-k. Liao, A. Choudhary.
“DiSC: A Distributed Single-Linkage Hierarchical Clustering Algorithm using MapReduce,” In Proceedings of the 4th International SC Workshop on Data Intensive Computing in the Clouds (DataCloud), 2013.
Karimabadi, H., Loring, B., O'Leary, P., Majumdar, A., Tatineni, M., Geveci, B..
“In-situ Visualization for Global Hybrid Simulations,” In Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery, XSEDE '13, pp. 1-8. 2013.
Aaron Knoll, Kah Chun Lau, Bin Liu, Maria K.Y. Chan, Aslihan Sumer, Jeffrey Greeley, Larry Curtiss, Julius Jellinek, Mark Hereld, Michael E. Papka.
“Uncertainty Classification and Visualization of Molecular Interfaces,” In International Journal of Uncertainty Quantification, 3(2), 2013.
Sidharth Kumar, Avishek Saha, Venkatram Vishwanath, Philip Carns, John A. Schmidt, Giorgio Scorzelli, Hemanth Kolla, Ray Grout, Robert Latham, Robert Ross, Michael E. Papka, Jacqueline Chen, Valeri Pascucci.
“Characterization and Modeling of PIDX Parallel I/O for Performance Optimization,” In Proceedings of the ACM/IEEE Conference on High Performance Computing, Networking, Storage and Analysis (SC), IEEE Computer Society Press, pp. 67. November, 2013.
S. Lakshminarasimhan, D. A. Boyuka II, S. V. Pendse, X. Zou, J. Jenkins, V. Vishwanath, M. E. Papka,, N. F. Samatova.
“Scalable in situ scientific data encoding for analytical query processing,” In Proceedings of the 21st ACM Symposium on High-Performance Parallel and Distributed Computing (HPDC ), Note: Best Paper Award, 2013.
S. Lasluisa, F. Zhang, T. Jin, I. Rodero, H. Bui, M. Parashar.
“In-situ Feature-based Objects Tracking for Data-Intensive Scientific and Enterprise Analytics Workflows,” In Submitted ot Journal of Cluster Computing, 2013.
Nick Leaf, Venkatram Vishwanath, Joseph Insley, Mark Hereld, Michael E. Papka, Kwan-Liu Ma.
“Efficient Parallel Volume Visualization of Large-Scale Adaptive Mesh Refinement Data,” In Proceedings of IEEE Symposium on Large Data Analysis and Visualization (LDAV), Note: Best Paper Award, pp. 35--42. 2013.
DOI: 10.1109/LDAV.2013.6675156
Adaptive Mesh Refinement is a popular approach for allocating scarce computing resources to the most important portions of the simulation domain. This approach implies spatial compression and the large simulation sizes which necessitate it. We present a novel, cluster- and GPU-parallel rendering scheme for AMR data, which is built on previous work in the GPU ray casting of AMR data. Our approach utilizes the existing AMR structure to subdivide the problem into convexly-bounded chunks and perform static load-balancing. We take advantage of data locality within chunks to interpolate directly between blocks without the need to store ghost cells on the interior boundaries. We also present a novel block decomposition method, and analyze its performance against two alternative methods. Finally, we examine the interactivity of our renderer for multiple datasets, and consider its scalability across a large number of GPUs.
Teng-Yok Lee, Xin Tong, Han-Wei Shen, Pak Chung Wong, Samson Hagos, L. Ruby Leun.
“Feature Tracking and Visualization of Madden-Julian Oscillation in Climate Simulation,” In IEEE Computer Graphics and Applications , IEEE, July, 2013.
T.-Y. Lee, H.-W. Shen.
“Efficient local statistical analysis via integral histograms with discrete wavelet transform,” In IEEE Transactions on Visualization and Computer Graphics (TVCG), Vol. 19, No. 12, pp. 2693--2702. 2013.
Matthieu Lefebvre, Ebru Bozdag, Henri Calandra, Judy Hill, Wenjie Lei, Daniel Peter, Norbert Podhorszki, Dave Pugmire, Herurisa Rusmanugroho, James Smith, Jeroen Tromp.
“A Data Centric View of Large-Scale Seismic Imaging Workflows,” In 4th SC Workshop on Petascale (Big) Data Analytics: Challenges and Opportunities, 2013.
K-W Lin, J. Chou, S. Byna,, K. Wu.
“Optimizing FastQuery Performance on Luster File System,” 25th International Conference on Scientific and Statistical Database Management (SSDBM) , 2013.
Qing Liu, Norbert Podhorszki, Jeremy Logan, Scott Klasky.
“Runtime I/O Re-Routing + Throttling on HPC Storage,” In USENIX HotStorage, 2013.
Qing Liu, Jeremy Logan, Yuan Tian, Hasan Abbasi, Norbert Podhorszki, Jong Youl Choi, Scott Klasky, Roselyne Tchoua, Jay Lofstead, Ron Oldfield, Manish Parashar, Nagiza Samatova, Karsten Schwan, Arie Shoshani, Matthew Wolf, Kesheng Wu, Weikuan Yu.
“Hello ADIOS: the challenges and lessons of developing leadership class I/O frameworks,” In Concurrency and Computation: Practice and Experience, 2013.
J. Lofstead, R. Ross.
“Insights for exascale IO APIs from building a petascale IO API,” In Proceedings of SC13: International Conference for High Performance Computing, Networking, Storage and Analysis, ACM, November, 2013.
Kewei Lu, Abon Chaudhuri, Teng-Yok Lee, Han-Wei Shen, Pak Chung Wong.
“Exploring Vector Fields with Distribution-based Streamline Analysis,” In IEEE Pacific Visualization 2013, IEEE, 2013.
D. Maljovec, B. Wang, A. Kupresanin, G. Johannesson, V. Pascucci,, P.-T. Bremer.
“Adaptive sampling with topological scores,” In International Journal for Uncertainty Quantification, Vol. 3, No. 2, pp. 119--141. 2013.
Dan Maljovec, Avishek Saha, Peter Lindstrom, Peer-Timo Bremer, Bei Wang, Carlos Correa, Valerio Pascucci.
“A Comparative Study of Morse Complex Approximation Using Different Neighborhood Graphs,” Topological Methods in Data Analysis and Visualizatuion (TopoInVis), 2013.
D. Maljovec, B. Wang, V. Pascucci, P.-T. Bremer, M. Pernice, D. Mandelli, R. Nourgaliev.
“Exploration of high-dimensional scalar function for nuclear reactor safety analysis and visualization,” In Proceedings of International Conference on Mathematics and Computational Methods Applied to Nuclear Science & Engineering, pp. 712--723. 2013.
D. Maljovec, B. Wang, D. Mandelli, P.-T. Bremer, V. Pascucci.
“Adaptive Sampling Algorithms for Probabilistic Risk Assessment of Nuclear Simulations,” In Proceedings of the 2013 International Topical Meeting on Probabilistic Safety Assessment and Analysis (PSA 2013), 2013.
D. Maljovec, B. Wang, D. Mandelli, P.-T. Bremer, V. Pascucci.
“Analyzing Dynamic Probabilistic Risk Assessment Data through Clustering,” In Proceedings of the 2013 International Topical Meeting on Probabilistic Safety Assessment and Analysis (PSA 2013), 2013.
Steve Martin, Han-Wei Shen.
“Transformations for Volumetric Range Distribution Queries,” In IEEE Pacific Visualization 2013, IEEE, 2013.
S. Martin, H.-W. Shen.
“Transformations for volumetric range distribution queries,” In Proceedings of the 2013 IEEE Pacific Visualization Symposium (PacificVis), pp. 89--96. 2013.
Robert Maynard, Kenneth Moreland, Utkarsh Ayachit, Berk Geveci, Kwan-Liu Ma.
“Optimizing Threshold for Extreme Scale Analysis,” In Proceedings SPIE Visualization and Data Analysis, February, 2013.
DOI: 10.1117/12.2007320
Kenneth Moreland.
“A Survey of Visualization Pipelines,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 19, No. 3, pp. 367-378. March, 2013.
DOI: 10.1109/TVCG.2012.133
Kenneth Moreland, Berk Geveci, Kwan-Liu Ma, Robert Maynard.
“A Classification of Scientific Visualization Algorithms for Massive Threading,” In Proceedings of the 8th International Workshop on Ultrascale Visualization (UltraVis '13), November, 2013.
DOI: 10.1145/2535571.2535591
H. Obermaier, K. I. Joy.
“Local Data Models for Probabilistic Transfer Function Design,” In Eurographics Conference on Visualization (EuroVis 2013) - Short Papers, pp. 43-47. 2013.
M. M. A. Patwary, D. Palsetia, A. Agrawal, W.-k. Liao, F. Manne, A. Choudhary.
“Scalable Parallel OPTICS Data Clustering Using Graph Algorithmic Techniques,” In Proceedings of 25th International Conference on High Performance Computing, Networking, Storage and Analysis (Supercomputing, SC), 2013.
S. Philip, B. Summa, J. Tierny, P.-T. Bremer V. Pascucci.
“Scalable Seams for Gigapixel Panoramas,” In Proceedings of the 2013 Eurographics Symposium on Parallel Graphics and Visualization, Note: Awarded Best Paper!, pp. (to appear). 2013.
Iuri Prilepov, Harald Obermaier, Eduard Deines, Christoph Garth, Kenneth I. Joy.
“Cubic Gradient-Based Material Interfaces,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 19, No. 10, pp. 1687-1699. 2013.
Khairi Reda, Aaron Knoll, Ken-ichi Nomura, Michael E. Papka, Andrew E. Johnson,, Jason Leigh.
“ Visualizing Large-Scale Atomistic Simulations in Ultra-Resolution Immersive Environments,” In Proc. IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV), pp. 59-65. 2013.
A. Romosan, A. Shoshani, K. Wu, V. Markowitz,, K. Mavrommatis.
“Accelerating Gene Context Analysis Using Bitmaps,” In 25th International Conference on Scientific and Statistical Database Management (SSDBM), 2013.
A. Rungta, B. Summa, D. Demir, P.-T. Bremer, V. Pascucci.
“ManyVis: Multiple Applications in an Integrated Visualization Environment,” Vol. 19, No. 12, pp. 2878--2885. 2013.
F. Sauer, Y. Zhang, S. Ethier, W. Wang,, K.-L. Ma.
“Visualizing Velocity Fields from Fusion Simulations using Advected Particle Trajectories,” In The 23rd International Conference on Numerical Simulation of Plasmas (ICNSP), September 14-16, 2013.
M. Schott, T. Martin, A.V.P. Grosset, S.T. Smith, C.D. Hansen.
“Ambient Occlusion Effects for Combined Volumes and Tubular Geometry,” In IEEE Transactions on Visualization and Computer Graphics (TVCG), Vol. 19, No. 6, Note: Selected as Spotlight paper for June 2013 issue, pp. 913--926. 2013.
DOI: 10.1109/TVCG.2012.306
E. Schendel, S. Harenberg, H. Tang, V. Vishwanath, M.E. Papka, N. Samatova.
“A Generic High-performance Method for Deinterleaving Scientific Data,” In 19th International European Conference on Parallel and Distributed Computing (EuroPar), Aachen, Germany, August, 2013.
Christopher Sewell, Li-ta Lo,, James Ahrens.
“Portable Data-Parallel Visualization and Analysis in Distributed Memory Environments,” In Proceedings of the IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV), October, 2013.
Sohail Shafii, Harald Obermaier, Rodman Linn, Eunmo Koo, Mario Hlawitschka, Christoph Garth, Bernd Hamann, Kenneth I. Joy.
“Visualization and Analysis of Vortex-Turbine Intersections in Wind Farms,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 19, No. 9, pp. 1579-1591. 2013.
S. Shafii, H. Obermaier, V. Kolar, M. Hlawitschka, C. Garth, B. Hamann, K. I. Joy.
“Illustrative Rendering of Vortex Cores,” In Eurographics Conference on Visualization (EuroVis 2013) - Short Papers, pp. 61-65. 2013.
Carmen Sigovan, Chris Muelder,, Kwan-Liu Ma.
“Visualizing Large-scale Parallel Communication Traces Using a Particle Animation Technique,” Computer Graphics Forum, 32(3), also Proceedings of EuroVis, 2013.
C. Sigovan, C. Muelder, K.-L. Ma, J. Cope, K. Iskra,, R. Ross.
“A Visual Network Analysis Methodology for Large Scale Parallel I/O Systems,” Proceedings of IEEE International Parallel & Distributed Processing Symposium, 2013.
R. Sisneros, J. Huang, G. Ostrouchov, S. Ahern, B. D. Semeraro.
“Contrasting Climate Ensembles: A Model-Based Visualization Approach for Analyzing Extreme Events,” In International Conference on Computational Sciences, Procedia Computer Science, Vol. 18, pp. 2347-2356. 2013.
ISSN: 1877-0509
DOI: 10.1016/j.procs.2013.05.406
Magdalena Slawinska, Tanja Bode, Jeremy Logan, Michael Clark, Hongbo Zou, Matthew Kinsey, Matthew Wolf, Pablo Laguna,, Scott Klasky.
“A Maya use case: adaptable scientific workflows with ADIOS for general relativistic astrophysics,” In Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery Article No. 54, ACM SIGAPP, July 22-25, 2013. San Diego, CA, USA, 2013.
Smith, J. A.; Bozdag, E.; Krischer, L.; Lefebvre, M.; Lei, W.; Podhorszki, N.; Tromp, J. .
“An Adaptable Seismic Data Format for Modern Scientific Workflows,” In American Geophysical Union, Fall Meeting, 2013.
B. Summa, A. Gyulassy, P.-T. Bremer, V. Pascucci.
“Interactive Data Exploration,” In Data Intensive Science. Chapman & Hall/Crc Computational Science, Note: Book chapter, 2013.
Q. Sun, F. Zhang, T. Jin, H. Bui, M. Parashar, K. Wu, A. Shoshani, H. Kolla, S. Klasky, J. Chen.
“Scalable In-Memory Data Indexing and Querying for Scientific Simulation Workflows,” In Submitted to IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 2013.
A. F. Szczepanski, Jian Huang, T. Baer, Y. C. Mack, S. Ahern.
“Data Analysis and Visualization in High-Performance Computing,” In Computer, Vol. 46, No. 5, IEEE Computer Society, pp. 84-92. May, 2013.
ISSN: 0018-9162
DOI: 10.1109/MC.2012.192
Y. Tian, Z, Liu, S. Klasky, B. Wang, H. Abbasi, S. Zhou, N. Podhorszki, T. Clune, J. Logan, W. Yu.
“A Lightweight I/O Scheme to Facilitate Spatial and Temporal Queries of Scientific Data Analytics,” In MSST, 2013.
Ying Tu, Han-Wei Shen .
“GraphCharter: Combining Browsing with Query to Explore Large Semantic Graphs,” In IEEE Pacific Visualization 2012 , IEEE, 2013.
Ranga Raju Vatsavai.
“Gaussian multiple instance learning approach for mapping the slums of the world using very high resolution imagery,” In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1419--1426. 2013.
DOI: 10.1145/2487575.2488210
In this paper, we present a computationally efficient algorithm based on multiple instance learning for mapping informal settlements (slums) using very high-resolution remote sensing imagery. From remote sensing perspective, informal settlements share unique spatial characteristics that distinguish them from other urban structures like industrial, commercial, and formal residential settlements. However, regular pattern recognition and machine learning methods, which are predominantly single-instance or per-pixel classifiers, often fail to accurately map the informal settlements as they do not capture the complex spatial patterns. To overcome these limitations we employed a multiple instance based machine learning approach, where groups of contiguous pixels (image patches) are modeled as generated by a Gaussian distribution. We have conducted several experiments on very high-resolution satellite imagery, representing four unique geographic regions across the world. Our method showed consistent improvement in accurately identifying informal settlements.
Yang Wang, Hongfeng Yu,, Kwan-Liu Ma.
“ Scalable Parallel Feature Extraction and Tracking for Large Time-varying 3D Volume Data,” Proceedings of Eurographics Symposium on Parallel Graphics and Visualization, 2013.
Bei Wang, Paul Rosen, Primoz Skraba, Harsh Bhatia, Valerio Pascucci.
“Visualizing Robustness of Critical Points for 2D Time-Varying Vector Fields,” In Computer Graphics Forum (EuroVis), Vol. 32, No. 3, 2013.
Y. Wan, H. Otsuna, C.D. Hansen.
“Synthetic Brainbows,” In Computer Graphics Forum, Vol. 32, No. 3pt4, pp. 471--480. 2013.
G.H. Weber, K. Beketayev, P.-T. Bremer, B. Hamann, M. Haranczyk, M. Hlawitschka, V. Pascucci.
“Comprehensible Presentation of Topological Information,” No. LBNL-5693E, Lawrence Berkeley National Laboratory, 2013.
Topological information has proven very valuable in the analysis of scientific data. An important challenge that remains is presenting this highly abstract information in a way that it is comprehensible even if one does not have an in-depth background in topology. Furthermore, it is often desirable to combine the structural insight gained by topological analysis with complementary information, such as geometric information. We present an overview over methods that use metaphors to make topological information more accessible to non-expert users, and we demonstrate their applicability to a range of scientific data sets. With the increasingly complex output of exascale simulations, the importance of having effective means of providing a comprehensible, abstract overview over data will grow. The techniques that we present will serve as an important foundation for this purpose.
Tzu-Hsuan Wei, Teng-Yok Lee,, Han-Wei Shen.
“Evaluating Isosurfaces with Level-est-based Information Maps,” In EuroVis 2013, Eurographics, 2013.
W. Widanagamaachchi, P. Rosen, V. Pascucci.
“A Flexible Framework for Fusing Image Collections into Panoramas,” In Proceedings of the 2013 SIBGRAPI Conference on Graphics, Patterns, and Images, Note: Awarded Best Paper!, pp. 195-202. 2013.
DOI: 10.1109/SIBGRAPI.2013.35
Panoramas create summary views of multiple images, which make them a valuable means of analyzing huge quantities of image and video data. This paper introduces the Ray Graph - a general framework for panorama construction. With rays as its vertices, the Ray Graph uses its edges to specify a set of coherency relationships among all input rays. Consequently, by using a set of simple graph traversal rules, a diverse set of panorama structures can be enumerated, which can be used to efficiently and robustly generate panoramic images from image collections. To demonstrate this framework, we first use it to recreate both 360° and street panoramas. We further introduce two new panorama models, the centipede panorama - a hybrid of the 360° and street panoramas, and the storytelling panorama - a time encoding panorama. Finally, we demonstrate the flexibility of this framework by enabling interactive brushing of panoramic regions for removal of undesired features such as occlusions and moving objects.
D. Williams, T. Bremer, C. Doutriaux, J. Patchett, G. Shipman, B. Haugen, R. Miller, B. Smith, C. Steed, W. Bethel, H. Childs, H. Krishnan, M. Wehner, C. T. Silva, E. Santos, D. Koop, T. Ellqvist, H. T. Vo, J. Poco, B. Geveci, A. Chaudhary, A. Bauer, A. Pletzer, D. Kindig, G. L. Potter, T. P. Maxwell.
“The Ultra-scale Visualization Climate Data Analysis Tools: Data Analysis and Visualization for Geoscience Data ,” In IEEE Special Issue: Cutting-Edge Research in Visualization , 2013.
Y. Wu, X. Liu, S. Liu,, K.-L. Ma.
“ViSizer: A Visualization Resizing Framework,” IEEE Transactions on Visualization and Computer Graphics, 19(2):278-290 , 2013.
Jinrong Xie, Hongfeng Yu,, Kwan-Liu Ma.
“Interactive Ray Casting of Geodesic Grids,” Computer Graphics Forum, 32(3), also Proceedings of EuroVis, 2013.
Yucong Ye, Robert Miller,, Kwan-Liu Ma.
“In-Situ Pathtube Visualization with Explorable Images,” Proceedings of Eurographics Symposium on Parallel Graphics and Visualization, 2013.
F. Zhang, C. Docan, H. Bui, M. Parashar, S. Klasky.
“XpressSpace: A Programming Framework for Coupling PGAS Simulation Codes,” In Journal of Concurrency and Computation: Practice and Experience, Note: (Accepted for publication), 2013.
DOI: 10.1002/cpe.3025
Yubo Zhang, Kwan-Liu Ma.
“Fast Global Illumination for Interactive Volume Visualization,” Proceedings of ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (I3D '13), 2013.
Yubo Zhang, Zhao Dong,, Kwan-Liu Ma.
“Real-time Volume Rendering in Dynamic Lighting Environments using Precomputed Photon Mapping,” IEEE Transactions on Visualization and Computer Graphics, 2013.
F. Zheng, H. Zou, G. Eisnhauer, K. Schwan, M. Wolf, J. Dayal, T. A. Nguyen, J. Cao, H. Abbasi, S. Klasky, N. Podhorszki, H. Yu.
“FlexIO: I/O middleware for Location-Flexible Scientific Data Analytics,” submitted to IPDPS 2013, 2013.
L. Zheng, Y. Wu,, K.-L. Ma.
“Perceptually Based Depth-Ordering Enhancement for Direct Volume Rendering,” IEEE Transactions on Visualization and Computer Graphics, 19(3):446-459, 2013.
Fang Zheng, Hongfeng Yu, Can Hantas, Matthew Wolf, Greg Eisenhauer, Karsten Schwan, Hasan Abbasi, Scott Klasky.
“GoldRush: resource efficient in situ scientific data analytics using fine-grained interference aware execution,” In Proceedings of SC13: International Conference for High Performance Computing, Networking, Storage and Analysis, pp. Article 78. 2013.
DOI: 10.1145/2503210.2503279
Severe I/O bottlenecks on High End Computing platforms call for running data analytics in situ. Demonstrating that there exist considerable resources in compute nodes un-used by typical high end scientific simulations, we leverage this fact by creating an agile runtime, termed GoldRush, that can harvest those otherwise wasted, idle resources to efficiently run in situ data analytics. GoldRush uses fine-grained scheduling to "steal" idle resources, in ways that minimize interference between the simulation and in situ analytics. This involves recognizing the potential causes of on-node resource contention and then using scheduling methods that prevent them. Experiments with representative science applications at large scales show that resources harvested on compute nodes can be leveraged to perform useful analytics, significantly improving resource efficiency, reducing data movement costs incurred by alternate solutions, and posing negligible impact on scientific simulations.
L. Zhou, C.D. Hansen.
“Transfer Function Design based on User Selected Samples for Intuitive Multivariate Volume Exploration,” In Proceedings of the 2013 IEEE Pacific Visualization Symposium (PacificVis), pp. 73--80. 2013.
2012
Sean Ahern.
“The Road to Exascale,” In High Performance Visualization: Enabling Extreme-Scale Scientific Insight, Chapman & Hall/CRC Computational Sciencet, Edited by E. Wes Bethel; Hank Childs, Lawrence Berkeley National Laboratory, Berkeley, California, USA; Charles Hansen, University of Utah, Salt Lake City, USA, Chapman & Hall/CRC Computational Science, October, 2012.
J. C. Bennett, H. Abbasi, P. Bremer, R. W. Grout, A. Gyulassy, T. Jin, S. Klasky, H. Kolla, M. Parashar, V. Pascucci, P. Pbay, D. Thompson, H. Yu, F. Zhang, J. Chen.
“Combining In-Situ and In-Transit Processing to Enable Extreme-Scale Scientific Analysis ,” In ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), Salt Lake City, Utah, U.S.A., November, 2012.
A. Bhatele, T. Gamblin, S.H. Langer, P.-T. Bremer, E.W. Draeger, B. Hamann, K.E. Isaacs, A.G. Landge, J.A. Levine, V. Pascucci, M. Schulz, C.H. Still.
“Mapping applications with collectives over sub-communicators on torus networks,” In Proceedings of Supercomputing 2012, pp. 1-11. 2012.
DOI: 10.1109/SC.2012.75
C. Brownlee, T. Fogal, C.D. Hansen.
“GLuRay: Ray Tracing in Scientific Visualization Applications using OpenGL Interception,” In Proceedings of the Eurographics Symposium on Parallel Graphics and Visualization (2012), Edited by H. Childs, T. Kuhlen, and F. Marton, pp. 41--50. 2012.
DOI: 10.2312/EGPGV/EGPGV12/041-050
C. Brownlee, J. Patchett, L.-T. Lo, D. DeMarle, C. Mitchell, J. Ahrens, C.D. Hansen.
“A Study of Ray Tracing Large-scale Scientific Data in Parallel Visualization Applications,” In Proceedings of the Eurographics Symposium on Parallel Graphics and Visualization (2012), Edited by H. Childs, T. Kuhlen, and F. Marton, 2012.
Surendra Byna, Jerry Chou, Oliver Ruebel, Prabhat, Homa Karimabadi, William S. Daughton, Vadim Roytershteyn, E. Wes Bethel, Mark Howison, Ke-Jou Hsu, Kuan-Wu Lin, Arie Shoshani, Andrew Uselton,, Kesheng Wu.
“Parallel I/O, Analysis, and Visualization of a Trillion Particle Simulation,” In Proceedings of the international conference on Supercomputing, 2012.
S. Byna, J. Chou, O. Rbel, Prabhat, H. Karimabadi, W. S. Daughton, V. Roytershteyn, E. W. Bethel, M. Howison, K-J. Hsu, K-W. Lin, A. Shoshani, A. Uselton,, K. Wu,.
“Parallel Data, Analysis, and Visualization of a Trillion Particles,” Lightning Talk and Poster Presentation at the 6th Extremely Large Databases Conference (XLDB), 2012.
David Camp, Hank Childs, Christoph Garth, Dave Pugmire, Kenneth I. Joy.
“Parallel Stream Surface Computation for Large Data Sets,” In Proceedings of the IEEE Symposium on Large-Scale Data Anaysis and Visualization, Seattle WA, USA, Note: to appear, 2012.
Abon Chaudhuri, Han-Wei Shen.
“A Self-Adaptive Technique for Visualizing Geospatial Data in 3D with Minimum Occlusion,” In Visualization and Data Analysis Conference in IS&/SPIE Symposium on Electronic Imaging, Note: Best Paper Award, 2012.
Abon Chauduhri, Teng-Yok Lee, Cong Wang, Bo Zhou, Tian-Tian Xu, Han-Wei Shen, Tom Peterka, Yi-Jen Chiang.
“Scalable Computation of Distributions from Large Scale Data Sets,” In IEEE 2012 Large Data Analysis and Visualization , 2012 , 2012.
Abon Chaudhuri, Teng-Yok Lee, Han-Wei Shen.
“Exploring Flow Fields Using Fractal Analysis of Field Lines,” In IEEE Visualization 2012 Poster Proceedings (Best Poster Award) , 2012.
Zhengzhang Chen, William Hendrix, Hang Guan, Isaac K. Tetteh, Alok Choudhary, Fredrick Semazzi, Nagiza F. Samatova.
“Discovery of extreme events-related communities in contrasting groups of physical system networks,” In Data Mining and Knowledge Discovery, 2012.
DOI: 10.1007/s10618-012-0289-3
F. Chen, H. Obermaier, H. Hagen, B. Hamann, J. Tierny, V. Pascucci..
“Topology Analysis of Time-Dependent Multi-Fluid Data Using the Reeb Graph,” In Computer Aided Geometric Design, Note: Published Online Apr. 24, Elsevier, 2012.
DOI: 10.1016/j.cagd.2012.03.019
H. Childs, E. Brugger, B. Whitlock, J. Meredith, S. Ahern, K. Bonnell, M. Miller, G. H. Weber, C. Harrison, D. Pugmire, T. Fogal, C. Garth, A. Sanderson, E. W. Bethel, M. Durant, D. Camp, J. M. Favre, O. Rbel, Paul Navrtil.
“VisIt: An End-User Tool For Visualizing and Analyzing Very Large Data,” In High Performance Visualization: Enabling Extreme-Scale Scientific Insight, Edited by E. Wes Bethel; Hank Childs, Lawrence Berkeley National Laboratory, Berkeley, California, USA; Charles Hansen, University of Utah, Salt Lake City, USA, Chapman & Hall/CRC Computational Science, October, 2012.
H. Childs, D. Pugmire, S. Ahern, B. Whitlock, M. Howison, Prabhat, G. Weber, E. W. Bethel.
“Visualization at Extreme Scale Concurrency,” In High Performance Visualization: Enabling Extreme-Scale Scientific Insight, Edited by E. Wes Bethel; Hank Childs, Lawrence Berkeley National Laboratory, Berkeley, California, USA; Charles Hansen, University of Utah, Salt Lake City, USA, Chapman & Hall/CRC Computational Science, October, 2012.
A.N.M. Imroz Choudhury, B. Wang, P. Rosen, V. Pascucci.
“Topological Analysis and Visualization of Cyclical Behavior in Memory Reference Traces,” In Proceedings of the IEEE Pacific Visualization Symposium (PacificVis 2012), pp. 9--16. 2012.
DOI: 10.1109/PacificVis.2012.6183557
Chun-Ming Chen, Lijie Xu, Teng-Yok Lee, Han-Wei Shen.
“A Flow Guided Layout for Out-of-Core Streamline Computation,” In Proceedings of IEEE Pacific Visualization, 2012.
C. Correa, T. Crnovrsanin,, K.-L. Ma.
“ Visual Reasoning about Social Networks using Centrality Sensitivities,” IEEE Transactions on Visualization and Computer Graphics, 18(1): 106-120, 2012.
Nathaniel Fout, Kwan-Liu Ma.
“Fuzzy Volume Rendering,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 18, No. 12, Note: (Also a Visualization 2012 Conference paper), pp. 2335-2344. 2012.
Nathaniel Fout, Kwan-Liu Ma.
“An Adaptive PRediction-Based Approach to Lossless Compression of Floating-Point Volume Data,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 18, No. 12, Note: (Also a Visualization 2012 Conference paper), pp. 2295-2304. 2012.
K. Gaither, H. Childs, K. Schulz, C. Harrison, W. Barth, D. Donzis, P. Yeung.
“Visual Analytics for Finding Critical Structures in Massive Time-Varying Turbulent-Flow Simulations,” In Computer Graphcs and Applications, Vol. 32, No. 4, IEEE, pp. 34-45. July-Aug, 2012.
S. Gerber, O. Reubel, P.-T. Bremer, V. Pascucci, R.T. Whitaker.
“Morse-Smale Regression,” In Journal of Computational and Graphical Statistics, Vol. 22, No. 1, pp. 193--214. 2012.
DOI: 10.1080/10618600.2012.657132
Z. Gong, S. Lakshminarasimhan, J. Jenkins, H. Kolla, S. Ethier, J. Chen, R. Ross, S. Klasky, N. Samatova.
“Multi-level layout optimizations for efficient spatio-temporal queries of ISABELA-compressed data,” In The 26th IEEE International Parallel & Distributed Processing Symposium (IPDPS), May, 2012.
Zhenhuan Gong, Terry Rogers, John Jenkins, Hemanth Kolla, Stephane Ethier, Jackie Chen, Robert Ross, Scott Klasky, Nagiza F. Samatova.
“MLOC: Multi-level Layout Optimization Framework for Compressed Scientific Data Exploration with Heterogeneous Access Patterns,” In International Conference on Parallel Processing (ICPP), Pittsburgh, PA, September, 2012.
R. Grout, A. Gruber, H. Kolla, P.-T. Bremer, J. Bennett, A. Gyulassy, J. Chen.
“A direct numerical simulation study of turbulence and flame structure in transverse jets analysed in jet-trajectory based coordinates,” In Journal of Fluid Mechanics 706 , pp. 351-383 . 2012.
A. Gyulassy, V. Pascucci, T. Peterka, R. Ross.
“The Parallel Computation of Morse-Smale Complexes,” In Parallel & Distributed Processing Symposium (IPDPS), pp. 484--495. 2012.
DOI: 10.1109/IPDPS.2012.52
A. Gyulassy, P.-T. Bremer, V. Pascucci.
“Computing Morse-Smale Complexes with Accurate Geometry,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 18, No. 12, pp. 2014--2022. 2012.
DOI: 10.1109/TVCG.2011.272
A. Gyulassy, N. Kotava, M. Kim, C. Hansen, H. Hagen,, V. Pascucci.
“Direct Feature Visualization Using Morse-Smale Complexes,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 18, No. 9, pp. 1549--1562. Sept., 2012.
DOI: 10.1109/TVCG.2011.272
Habib, S., Morozov, V., Finkel, H., Pope, A., Heitmann, K., Kumaran, K., Peterka, T., Insley, J., Daniel, D., Fasel, P., Frontiere, N., Lukic, Z..
“The Universe at Extreme Scale: Multi-Petaflop Sky Simulation on the BG/Q,” In Proceedings of SC12, 2012.
C. Harrison, H. Krishnan.
“Python's role in VisIt,” In the Proceedings of the eleventh annual Scientific Computing with Python Conference, (SciPy 2012), July, 2012.
C. Harrison, P. A. Navrtil, M. Moussalem, M. Jiang, H. Childs.
“Efficient Dynamic Derived Field Generation on Many-Core Architectures,” In Proceedings of Workshop on Python for High Performance and Scientific Computing, (PyHPC 2012), November, 2012.
William Hendrix, Md. Mostofa Ali Patwary, Ankit Agrawal, Wei-keng Liao, Alok Choudhary.
“Parallel Hierarchical Clustering on Shared Memory Platforms,” In 19th Annual International Conference on High Performance Computing, Pune, India, 2012.
Wei-Hsien Hsu, Chun-Fu Wang, Kwan-Liu Ma, Hongfeng Yu,, Jacqueline H. Chen.
“A Job Scheduling Design for Visualization Services using GPU Clusters,” In Proceedings of IEEE Cluster 2012, pp. 523-533. September, 2012.
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 Topological Methods in Data Analysis and Visualization II. Mathematics and Visualization, No. 3, pp. 141--159. 2012.
DOI: 10.1007/978-3-642-23175-9 10
John Jenkins, Isha Arkatkar, Sriram Lakshminarasimhan, Neil Shah, Eric R. Schendel, Stephane Ethier, CS Chang, Jackie Chen, Hemanth Kolla, Scott Klasky, Robert Ross,, Nagiza F. Samatova.
“Analytics-driven Lossless Data Compression for Rapid In-situ Indexing, Storing, and Querying,” In Proceedings of the 23rd International Conference on Database and Expert Systems Applications (DEXA), September, 2012.
John Jenkins, James Dinan, Pavan Balaji, Nagiza F. Samatova, Rajeev Thakur.
“Enabling Fast, Noncontiguous GPU Data Movement in Hybrid MPI+GPU Environments,” In IEEE International Conference on Cluster Computing (Cluster), Beijing, China, September, 2012.
John Jenkins, Eric Schendel, Sriram Lakshminarasimhan, David A. Boyuka III, Terry Rogers, Stephane Ethier, Robert Ross, Scott Klasky, Nagiza, F. Samatova.
“Byte-precision Level of Detail Processing for Variable Precision Analysis,” In ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), Salt Lake City, Utah, U.S.A., November, 2012.
F. Jiao, J.M. Phillips, Y. Gur, C.R. Johnson.
“Uncertainty Visualization in HARDI based on Ensembles of ODFs,” In Proceedings of the 5th IEEE Pacific Visualization Symposium (PacificVis 2012), pp. 193--200. 2012.
T. Jin, F. Zhang, M. Parashar, S. Klasky, N. Podhorszki, H. Abbasi.
“A Scalable Messaging System for Accelerating Discovery from Large Scale Scientific Simulations,” In IEEE International Conference on High Performance Computing (HiPC), December, 2012.
C.R. Johnson.
“Biomedical Visual Computing: Case Studies and Challenges,” In IEEE Computing in Science and Engineering, Vol. 14, No. 1, pp. 12--21. 2012.
Thomas Kerwin, D. Stredney, G. Wiet, Han-Wei Shen.
“Virtual Mastoidectomy performance evaluation through multi-volume analysis,” In Computer Assisted Radiology and Surgery (CARS), 2012.
M. Kim, G. Chen, C.D. Hansen.
“Dynamic particle system for mesh extraction on the GPU,” In Proceedings of the 5th Annual Workshop on General Purpose Processing with Graphics Processing Units, London, England, pp. 38--46. 2012.
DOI: 10.1145/2159430.215943
J. Knezevic, R.-P. Mundani, E. Rank, A. Khan, C.R. Johnson.
“Extending the SCIRun Problem Solving Environment to Large-Scale Applications,” In Applied Computing, Madrid, Spain, IADIS, pp. 171--178. 2012.
Hari Krishnan, Christoph Garth, Jens Guhring, M. Akif Gulsun, Andreas Greiser,, Kenneth I. Joy.
“Analysis of Time-Dependent Flow-Sensitive PC-MRI Data,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 18, No. 6, pp. 966--977. June, 2012.
S. Kumar, V. Vishwanath, P. Carns, J.A. Levine, R. Latham, G. Scorzelli, H. Kolla, R. Grout, R. Ross, M.E. Papka.
“Efficient data restructuring and aggregation for I/O acceleration in PIDX,” In Proceedings of the ACM/IEEE Conference on High Performance Computing, Networking, Storage and Analysis (SC), IEEE Computer Society Press, pp. 50:1--50:11. 2012.
ISBN: 978-1-4673-0804-5
Sriram Lakshminarasimhan, Prabhat Kumar, Wei-keng Liao, Alok Choudhary, Vipin Kumar,, Nagiza F. Samatova.
“On the Path to Sustainable, Scalable, and Energy-efficient Data Analytics: Challenges, Promises, and Future Directions,” In Proceedings of the 2012 International Green Computing Conference (IGCC), San Jose, CA, U.S.A., Note: Invited paper, June, 2012.
Sriram Lakshminarasimhan, Neil Shah, Stephane Ethier, Seung-Hoe Ku, C.S. Chang, Scott Klasky, Rob Latham, Rob Ross, Nagiza F. Samatova.
“ISABELA for Effective In-situ Compression of Scientific Data,” In Concurrency and Computation: Practice and Experience, Note: Invited paper, 2012.
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 Symposium on Information Visualization (INFOVIS'12), Seattle, WA, Vol. 18, No. 12, pp. 2467--2476. 2012.
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R. Latham, C. Daley, W. Liao, K. Gao, R. Ross, A. Dubey, A. Choudhary.
“A case study for scientific I/O: improving the FLASH astrophysics code,” In Computational Science & Discovery, Vol. 5, No. 1, pp. 015001. 2012.
Z. Li, S. Wang, J. Yu,, K.-L. Ma.
“Restoration of Brick and Stone Relief from Single Rubbing Images,” IEEE Transactions on Visualization and Computer Graphics. 18(2):177-187, 2012.
Shusen Liu, Joshua A. Levine, Peer Timo Bremer, Valerio Pascucci.
“Gaussian Mixture Model Based Volume Rendering ,” 2012 IEEE Symposium on Large Data Analysis and Visualization (LDAV), Note: Best Paper Honorable Mention, pp. 73--77. 2012.
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Li-ta Lo, Christopher Sewell,, James Ahrens.
“PISTON: A Portable Cross-Platform Framework for Data-Parallel Visualization Operators,” In EGPGV.12: Proceedings of the Eurographics Symposium on Parallel Graphics and Visualization 2012, May, 2012.
S.-J. Luo, C.-L. Liu, B.-Y. Chen,, K.-L. Ma.
“Ambiguity-Free Edge-Bundling for Interactive Graph Visualization,” IEEE Transactions on Visualization and Computer Graphics, 18(5):810-821, 2012.
J. Ma, I. Liao, J. Frazier,, K.-L. Ma.
“Living Liquid: Design and Evaluation of an Exploratory Visualization Tool for Museum Visitors,” IEEE Transactions on Visualization and Computer Graphics, 18(12):2799-2808, 2012.
K.-L. Ma et al..
“Scientific Storytelling using Visualization,” IEEE Computer Graphics and Applications, 32(1):12-19, Jan/Feb, 2012.
Joyce Ma, Isaac Liao, Jennifer Frazier, Kwan-Liu Ma.
“Living Liquid: Design and Evaluation of an Exploratory Visualization Tool for Museum Visitors,” Vol. 18, No. 12, Note: (Also an InfoVis 2012 Conference paper), pp. 2799-2808. 2012.
Steve Martin, Han-Wei Shen .
“Interactive Transfer Function Design on Large Multiresolution Volumes,” In IEEE 2012 Large Data Analysis and Visualization , IEEE, 2012.
T. Martin, G. Chen, S. Musuvathy, E. Cohen, C.D. Hansen.
“Generalized Swept Mid-structure for Polygonal Models,” In Proceedings of Eurographics 2012, Vol. 31, No. 2, pp. 805--814. 2012.
DOI: 10.1111/j.1467-8659.2012.03061.x
W. C. McLendon, G. Bansal, P.-T. Bremer, J. Chen, H. Kolla, J. Bennett.
“On The Use of Graph Search Techniques for The Analysis of Extreme-scale Combustionc Simulation Data,” Proceedings of IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV), 2012.
Jeremy S. Meredith, Sean Ahern, David Pugmire, Robert Sisneros.
“EAVL: The Extreme-scale Analysis and Visualization Library,” In Proceedings of the Eurographics Symposium on Parallel Graphics and Visualization (EGPGV), 2012.
J. Meredith, R. Sisneros, D. Pugmire, S. Ahern.
“A Distributed Data-Parallel Framework for Analysis and Visualization Algorithm Development,” In Fifth Workshop on General Purpose Processing on Graphics Processing Units (GPGPU), ACM Architectural Support for Programming Languages and Operating Systems, March, 2012.
Kenneth Moreland.
“Redirecting Research in Large-Format Displays for Visualization,” In Proceedings of the IEEE Symposium on Large-Scale Data Anaysis and Visualization, pp. 91--95. October, 2012.
Kenneth Moreland, Brad King, Robert Maynard, Kwan-Liu Ma.
“Flexible Analysis Software for Emerging Architectures,” In Petascale Data Analytics: Challenges and Opportunities (PDAC-12), November, 2012.
Kenneth Moreland, Jeremy Meredith, Berk Geveci.
“Enabling Production-Quality Scientific-Discovery Tools with Data and Execution Models,” No. SAND 2012-10796P, Sandia National Laboratories, December, 2012.
Kenneth Moreland.
“Oh, $#*@! Exascale! The Effect of Emerging Architectures on Scientific Discovery,” In 2012 SC Companion (Proceedings of the Ultrascale Visualization Workshop), pp. 224--231. November, 2012.
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P. Navratil, B. Barth,, H. Childs.
“Virtual Rheoscopic Fluids for Dense, Large- Scale Fluid Flow Visualizations.,” In Proceedings of IEEE Symposium on Large Data Analysis and Visualization (LDAV),, pp. 79-84. October, 2012.
B. Nouanesengsy, T.-Y. Lee, K. Lu, H.-W. Shen,, T. Peterka.
“Parallel Particle Advection and FTLE Computation for Time-Varying Flow Fields,” SC 12: ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, 2012, 2012.
Harald Obermaier, Kenneth I. Joy.
“Derived Metric Tensors for Flow Surface Visualization,” In IEEE Transactions on Visualization and Computer Graphics (TVCG), Vol. 18, No. 12, pp. 2149-2158. Oct, 2012.
Harald Obermaier, Kenneth I. Joy.
“Function Field Analysis for the Visualization of Flow Similiarity in Time-Varying Vector Fields,” In LNCS 7432, Part II (Proc of ISVC 2012), Springer Heidelberg, pp. 253-264. 2012.
Harald Obermaier, Fang Chen, Hans Hagen, Kenneth I. Joy.
“Visualization of Material Interface Stability,” In Proceedings of Pacific Visualization Conference, Songdo, Korea, pp. 225--232. 2012.
Harald Obermaier, Magali I. Billen, Kenneth I. Joy, Hans Hagen, Martin Hering-Bertram..
“Visualization and multivariate clustering of scattered moment tensors,” In Information Visualization, Vol. 11, No. 1, Sage, pp. 43-59. 2012.
Sedat Ozer, Jishang Wei, Deborah Silver, Kwan-Liu Ma,, Pino Martin.
“Group Dynamics in Scientific Visualization,” Proceedings of IEEE Symposium on Large Data Analysis and Visualization (LDAV '12), pp. 97-104. October, 2012.
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 & Hall/CRC Computational Science, Ch. 19, Edited by E. Wes Bethel; Hank Childs, Lawrence Berkeley National Laboratory, Berkeley, California, USA; Charles Hansen, University of Utah, Salt Lake City, USA, Chapman & Hall/CRC Computational Science, 2012.
Md. Mostofa Ali Patwary, Diana Palsetia, Ankit Agrawal, Wei-keng Liao, Fredrik Manne, Alok Choudhary.
“A New Scalable Parallel DBSCAN Algorithm Using the Disjoint Set Data Structure,” In ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), Salt Lake City, Utah, U.S.A., 2012.
Tom Peterka, Robert Ross.
“Versatile Communication Algorithms for Data Analysis,” In EuroMPI Special Session on Improving MPI User and Developer Interaction IMUDI'12, Vienna, Austria, 2012.
Peterka, T., Kwan, J., Pope, A., Finkel, H., Heitmann, K., Habib, S., Wang, J., Zagaris, G..
“Meshing the Universe: Integrating Analysis in Cosmological Simulations,” In Proceedings of the SC12 Ultrascale Visualization Workshop, Salt Lake City, UT, 2012.
Peterka, T., Ma, K.-L.
“Parallel Image Compositing Methods, book chapter in High Performance Visualization,” Edited by Bethel, E. W., Childs, H., Hansen, C., 2012.
K. Potter, R.M. Kirby, D. Xiu, C.R. Johnson.
“Interactive visualization of probability and cumulative density functions,” In International Journal of Uncertainty Quantification, Vol. 2, No. 4, pp. 397--412. 2012.
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K. Potter, P. Rosen, C.R. Johnson.
“From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches,” In Uncertainty Quantification in Scientific Computing, IFIP Advances in Information and Communication Technology Series, Vol. 377, Edited by Andrew Dienstfrey and Ronald Boisvert, Springer, pp. 226--249. 2012.
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Elaheh Pourabbas, Arie Shoshani,, Kesheng Wu.
“Minimizing Index Size by Reordering Rows and Columns,” In International Conference on Scientific and Statistical Database Management (SSDBM), June, 2012.
Michel Rasquin, Patrick Marion, Venkatram Vishwanath, Ray Loy, Andrew Bauer, Benjamin Matthews, Min Zhou, Onkar Sahni, Jing Fu, Ning Liu, Christopher Carothers, Mark S. Shephard, Mark Hereld, Michael E. Papka, Kalyan Kumaran, Berk Geveci,, Kenneth Jansen.
“Interactive visualization from a live unstructured grid CFD simulation at 160k cores,” 2012 SIAM Conference on Parallel Processing for Scientific Computing. SIAM, 2012, 2012.
Shawn Recker, Mauricio Hess-Flores, Mark A. Duchaineau, Kenneth I. Joy.
“Visualization of Scene Structure Uncertainity in a Multi-View Reconstruction Pipeline,” In Proceedings of Vision, Modeling and Visualization Conference, Note: to appear, 2012.
A. Sallaberry, C. Muelder,, K.-L. Ma.
“Clustering, Visualizing, and Navigating for Large Dynamic Graphs,” Proceedings of Graph Drawing 2012, pp. 487-498. September 19-21,, 2012.
A.R. Sanderson, B. Whitlock, O. Ruebel, H. Childs, G.H. Weber, Prabhat, K. Wu.
“A System for Query Based Analysis and Visualization,” In Proceedings of the Third International Eurovis Workshop on Visual Analytics EuroVA 2012, pp. 25--29. June, 2012.
E.R. Schendel, S.V. Pendse, J. Jenkins, D.A. Boyuka II, Z. Gong, S. Lakshminarasimhan, Q. Liu, S. Klasky, R. Ross, N.F. Samatova.
“ISOBAR Hybrid Compression-I/O Interleaving for Large-scale Parallel I/O Optimization,” In 21st International ACM Symposium on High Performance Parallel and Distributed Computing, June, 2012.
E.R. Schendel, Y. Jin, N. Shah, J. Chen, C.S. Chang, S.-H. Ku, S. Ethier, S. Klasky, R. Latham, R. Ross, N. Samatova.
“ISOBAR Preconditioner for Effective and High-throughput Lossless Data Compression,” In 28th annual IEEE International Conference on Data Engineering (ICDE), 2012.
Simon Schroeder, John A. Peterson, Harald Obermaier, Louise H. Kellogg, Kenneth I. Joy, Hans Hagen.
“Visualization of Flow Behavior in Earth Mantle Convection,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 18, No. 12, pp. 2198-2207. Oct, 2012.
M. Schott, T. Martin, A.V.P. Grosset, C. Brownlee, Thomas Hollt, B.P. Brown, S.T. Smith, C.D. Hansen.
“Combined Surface and Volumetric Occlusion Shading,” In Proceedings of Pacific Vis 2012, pp. 169--176. 2012.
DOI: 10.1109/PacificVis.2012.6183588
Christopher Sewell, Jeremy Meredith, Kenneth Moreland, Tom Peterka, Dave DeMarle, Li-ta Lo, James Ahrens, Robert Maynard,, Berk Geveci.
“The SDAV Software Frameworks for Visualization and Analysis on Next-Generation Multi-Core and Many-Core Architectures,” In 2012 SC Companion (Proceedings of the Workshop on Ultrascale Visualization), Salt Lake City, UT, pp. 206--214. November, 2012.
DOI: 10.1109/SC.Companion.2012.36
Neil Shah, Eric R. Schendel, Sriram Lakshminarasimhan, Saurabh V. Pendse, Terry Rogers, Nagiza F. Samatova.
“Improving I/O Throughput with PRIMACY: Preconditioning ID-Mapper for Compressing Incompressibility,” In IEEE International Conference on Cluster Computing (Cluster), Beijing, China, September, 2012.
Zeqian Shen, Jishang Wei, Neel Sundaresan,, Kwan-Liu Ma.
“Visual Analysis of Massive Web Session Data,” Proceedings of IEEE Symposium on Large Data Analysis and Visualization (LDAV), pp. 65-72. October, 2012.
Brian Summa, Julien Tierny, Valerio Pascucci.
“Panorama weaving: fast and flexible seam processing,” In ACM Trans. Graph, Vol. 31, No. 4, Note: ACM ID:2335434, pp. 83:1--83:11. July, 2012.
ISSN: 0730-0301
DOI: 10.1145/2185520.2185579
Yuzuru Tanahashi, Kwan-Liu Ma.
“Design Considerations for Optimizing Storyline Visualizations,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 18, No. 12, Note: (Also an InfoVis 2012 Conference paper), pp. 2679-2688. 2012.
Wei Tang, Narayan Desai, Venkatram Vishwanath, Daniel Buettner, Zhilin Lan.
“Multi-domain job coscheduling for leadership computing systems,” The Journal of Supercomputing, Springer, 2012.
ISSN: 0920-8542, 2012
DOI: 10.1007/s11227-012-0741-6
Y. Tananhashi, K.-L. Ma.
“Design Considerations for Optimizing Storyline Visualizations,” IEEE Transactions on Visualization and Computer Graphics 18(12):2679-2688 (InfoVis), 2012.
Yuzuru Tanahashi, James R. Rowland, Stephen North, Kwan-Liu Ma.
“Inferring Human Mobility Patterns from Anonymized Mobile Communication Usage,” Proceedings of MoMM 2012, pp. 151-160. December 3-5, 2012.
Y. Tian, S. Klasky, W. Yu, H. Abbasi, B. Wang, N. Podhorszki, R. Grout, M. Wolf.
“A System-Aware Optimized Data Organization for Efficient Scientific Analytics,” In 20th IEEE International Symposium on Modelling, Analysis, and Simulation of Computer and Telecommunication Systems (Mascots), 2012.
Y. Tian, S. Klasky, W. Yu, H. Abbasi, B. Wang, N. Podhorszki, R. Grout, M. Wolf.
“Two-Level Data Organization for Efficient Scientific Analytics,” In 21st International ACM Symposium on High-Performance Parallel and Distributed Computing (HPDC), 2012.
Xin Tong, Teng-Yok Lee, Han-Wei Shen.
“ Salient Time Steps Selection from Large Scale Time-Varying Data Sets with Dynamic Time Warping,” In IEEE 2012 Large Data Analysis and Visualization , IEEE, 2012.
Daniela M. Ushizima, Dmitriy Morozov, Gunther H. Weber, Andrea G. C. Bianchi, James A. Sethian, E. Wes Bethel, Member.
“Augmented Topological Descriptors of Pore Networks for Material Science,” In IEEE Transactions on Visualization and Computer Graphcis, Vol. 18, No. 12, pp. 2041-2050. 2012.
Ranga Raju Vatsavai.
“Scalable Multi-Instance Learning Approach for Mapping the Slums of the World,” Proceedings of the SC workshop on Petascale Data Analytics: Challenges and Opportunities, IEEE, 2012.
Ranga Raju Vatsavai, Auroop Ganguly, Varun Chandola, Anthony Stefanidis, Scott Klasky, Shashi Shekhar.
“Spatiotemporal data mining in the era of big spatial data: algorithms and applications,” Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data (BigSaptial12), ACM, 2012.
Ranga Raju Vatsavai, Budhendra Bhaduri.
“Big Spatiotemporal Data Analytics: Recent Advances and Future Challenges. (white paper) ,” CRA/CCC/NSF Sponsored: From GPS and Virtual Globes to Spatial Computing-2020, 2012.
Y. Wan, H. Otsuna, C.-B. Chien, C.D. Hansen.
“FluoRender: An Application of 2D Image Space Methods for 3D and 4D Confocal Microscopy Data Visualization in Neurobiology Research,” In Proceedings of Pacific Vis 2012, Incheon, Korea, pp. 201--208. 2012.
DOI: 10.1109/PacificVis.2012.6183592
Y. Wan, H. Otsuna, C.-B. Chien, C.D. Hansen.
“Interactive Extraction of Neural Structures with User-Guided Morphological Diffusion,” In Proceedings of the IEEE Symposium on Biological Data Visualization, pp. 1--8. 2012.
DOI: 10.1109/BioVis.2012.6378577
Y. Wan, A.K. Lewis, M. Colasanto, M. van Langeveld, G. Kardon, C.D. Hansen.
“A Practical Workflow for Making Anatomical Atlases in Biological Research,” In IEEE Computer Graphics and Applications, Vol. 32, No. 5, pp. 70--80. 2012.
DOI: 10.1109/MCG.2012.64
Gunther H. Weber, Hank Childs, Jeremy S. Meredith.
“Efficient Parallel Extraction of Crack-free Isosurfaces from Adaptive Mesh Refinement (AMR) Data,” In Proceedings of the IEEE Symposium on Large-Scale Data Anaysis and Visualization, Seattle WA, USA, 2012.
J. Wei, Z. Shen, N. Sundaresan,, K-L. Ma.
“Visual Cluster Exploration of Web Clickstream Data,” In Proceedings of the Visual Analytics Science and Technology Conference (VAST 2012), pp. 3-12. October, 2012.
J. Wei, H. Yu, R. W. Grout, J. Chen,, K.-L. Ma.
“Visual Analysis of Particle Behaviors to Understand Combustion Simulations,” IEEE Computer Graphics and Applications, 32(1):12-19, 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), Note: Best Paper Award, pp. 9--17. 2012.
DOI: 10.1109/LDAV.2012.6378962
P.C. Wong, H.-W. Shen, C.R. Johnson, C. Chen, R.B. Ross.
“The Top 10 Challenges in Extreme-Scale Visual Analytics,” In IEEE Computer Graphics and Applications, Visualization Viewpoints, Edited by Theresa-Marie Rhyne, pp. 63--67. July-August, 2012.
DOI: 10.1109/MCG.2012.87
Pak Chung Wong, Han-Wei Shen, Valerio Pascucci.
“Extreme Scale Visual Analytics,” In IEEE Computer Graphics and Applications, IEEE, July, 2012.
Yingcai Wu, Guo-Xun Yuan, Kwan-Liu Ma.
“Visualizing Flow of Uncertainty through Analytical Processes,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 18, No. 12, Note: (Also an InfoVis 2012 Conference paper), pp. 2526-2535. 2012.
Guo-Xun Yuan, Kwan-Liu Ma.
“Scalable Traning of Sparse Linear SVM,” In Proceedings of IEEE ICDM 2012, Note: (Accepted for publication), December, 2012.
F. Zhang, C. Docan, M. Parashar, S. Klasky, N. Podhorszki, H. Abbasi.
“Enabling In-situ Execution of Coupled Scientific Workflow on Multi-core Platform,” In 26th IEEE International Parallel & Distributed Processing Symposium (IPDPS), May, 2012.
F. Zhang, S. Lasluisa, T. Jin, I. Rodero, H. Bui, M. Parashar.
“In-situ Feature-based Objects Tracking for Large-Scale Scientific Simulations,” In 1st International Workshop on Data-Intensive Scalable Computing Systems (DISCS), November, 2012.
Lin Zheng, Yingcai Wu, Kwan-Liu Ma.
“Perceptually Based Depth-Ordering Enhancement for Direct Volume Rendering,” In IEEE Transactions on Visualization and Computer Graphics, Note: (Accepted for publication), 2012.
ISSN: 1077-2626
DOI: http://dx.doi.org/10.1109/TVCG.2012.144
L. Zhou, M. Schott, C.D. Hansen.
“Transfer Function Combinations,” In Computers and Graphics, Vol. 36, No. 6, pp. 596--606. 2012.
DOI: 10.1016/j.cag.2012.02.007