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8th ieee symposium on large-Scale data analysis and visualization (LDAV)

作者： Hui, Yawei Park, Byung Hoon Engelmann, Christian Oak Ridge Natl Lab Comp Sci & Math Div Oak Ridge TN 37830 USA

ISBN: (纸本)9781538668733

It remains a major challenge to effectively summarize and visualize in a comprehensive form the status of a complex computer system, such as the Titan supercomputer at the Oak Ridge Leadership Computing Facility (OLCF). In the ongoing research highlighted in this poster, we present system information entropy (SIE), a newly developed system metric that leverages the powers of traditional machine learning techniques and information theory. By compressing the multi-variant multi-dimensional event information recorded during the operation of the targeted system into a single time series of SIE, we demonstrate that the historical system status can be sensitively summarized in form of SIE and visualized concisely and comprehensively.

关键词： Human-centered computing Visual analytics General and reference Metrics Mathematics of computing Time series analysis

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Scalable visualization of Discrete Velocity Decompositions Using Spatially Organized Histograms 5

Scalable Visualization of Discrete Velocity Decompositions U...

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ieee 5th symposium on large data analysis and visualization (LDAV)

作者： Neuroth, Tyson Sauer, Franz Wang, Weixing Ethier, Stephane Ma, Kwan-Liu Univ Calif Davis Davis CA 95616 USA Princeton Plasma Phys Lab Princeton NJ 08543 USA

ISBN: (纸本)9781467385176

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.

关键词： 2D histogram velocity decomposition particle data movement data large-scale data

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Distributed Path Compression for Piecewise Linear Morse-Smale Segmentations and Connected Components 14

Distributed Path Compression for Piecewise Linear Morse-Smal...

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14th symposium on large data analysis and visualization

作者： Will, Michael Lukasczyk, Jonas Tierny, Julien Garth, Christoph RPTU Kaiserslautern Landau Kaiserslautern Germany CNRS Paris France Sorbonne Univ Paris France

ISBN: (纸本)9798331516932;9798331516925

This paper describes the adaptation to a distributed computational setting of a well-scaling parallel algorithm for computing Morse-Smale segmentations based on path compression. Additionally, we extend the algorithm to efficiently compute connected components in distributed structured and unstructured grids, based either on the connectivity of the underlying mesh or a feature mask. Our implementation is seamlessly integrated with the distributed extension of the Topology ToolKit (TTK), ensuring robust performance and scalability. To demonstrate the practicality and efficiency of our algorithms, we conducted a series of scaling experiments on large-scale datasets, with sizes of up to 40963 vertices on up to 64 nodes and 768 cores.

关键词： Distributed algorithms Scientific visualization

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data Parallel Hypersweeps for in Situ Topological analysis 10

Data Parallel Hypersweeps for in Situ Topological Analysis

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10th ieee symposium on large data analysis and visualization (LDAV)

作者： Hristov, Petar Weber, Gunther H. Carr, Hamish A. Rubel, Oliver Ahrens, James P. Univ Leeds Leeds W Yorkshire England Lawrence Berkeley Natl Lab Berkeley CA USA Univ Calif Davis Davis CA 95616 USA Los Alamos Natl Lab Los Alamos NM USA

ISBN: (纸本)9781728184685

The contour tree is a tool for understanding the topological structure of a scalar field. Recent work has built efficient contour tree algorithms for shared memory parallel computation, driven by the need to analyze large data sets in situ while the simulation is running. Unfortunately, methods for using the contour tree for practical data analysis are still primarily serial, including single isocontour extraction, branch decomposition and simplification. We report data parallel methods for these tasks using a data structure called the hyperstructure and a general purpose approach called a hypersweep. We implement and integrate these methods with a Cinema database that stores features as depth images and with a web server that reconstructs the features for direct visualization.

关键词： branch decomposition contour tree geometric measures in situ scalar field

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Exploring Tradeoffs Between Power and Performance for a Scientific visualization Algorithm 5

Exploring Tradeoffs Between Power and Performance for a Scie...

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ieee 5th symposium on large data analysis and visualization (LDAV)

作者： Labasan, Stephanie Larsen, Matthew Childs, Hank Univ Oregon Eugene OR 97403 USA Lawrence Berkeley Natl Lab Berkeley CA USA

ISBN: (纸本)9781467385176

Power is becoming a major design constraint in the world of high-performance computing (HPC). This constraint affects the hardware being considered for future architectures, the ways it will run software, and the design of the software itself. Within this context, we explore tradeoffs between power and performance. visualization algorithms themselves merit special consideration, since they are more data-intensive in nature than traditional HPC programs like simulation codes. This data-intensive property enables different approaches for optimizing power usage. Our study focuses on the isosurfacing algorithm, and explores changes in power and performance as clock frequency changes, as power usage is highly dependent on clock frequency. We vary many of the factors seen in the HPC context-programming model (MPI vs. OpenMP), implementation (generalized vs. optimized), concurrency, architecture, and data set-and measure how these changes affect power-performance properties. The result is a study that informs the best approaches for optimizing energy usage for a representative visualization algorithm.

关键词： Energy sources High Performance Computing tradeoffs clock frequency energy use Power (Psychology) power Power data visualisation design constraints computer software Software design Power

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In Situ Video Encoding of Floating-Point Volume data Using Special-Purpose Hardware for a Posteriori Rendering and analysis 7

In Situ Video Encoding of Floating-Point Volume Data Using S...

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ieee 7th symposium on large data analysis and visualization (LDAV)

作者： Leaf, Nick Miller, Bob Ma, Kwan-Liu Univ Calif Davis Davis CA 95616 USA

ISBN: (纸本)9781538606179

Scientific simulations typically store only a small fraction of computed timesteps due to storage and I/O bandwidth limitations. Previous work has demonstrated the compressibility of floating-point volume data, but such compression often comes with a tradeoff between computational complexity and the achievable compression ratio. This work demonstrates the use of special-purpose video encoding hardware on the GPU which is present but (to the best of our knowledge) completely unused in current GPU-equipped super computers such as Titan. We show that lossy encoding allows the output of far more data at sufficient quality for a posteriori rendering and analysis. We also show that the encoding can be computed in parallel to general-purpose computation due to the special-purpose hardware. Finally, we demonstrate such encoded volumes are inexpensive to decode in memory during analysis, making it unnecessary to ever store the decompressed volumes on disk.

关键词： Volume compression floating-point compression GPU video encoding

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visualization and analysis of large data collections: a case study applied to confocal microscopy data

Visualization and analysis of large data collections: a case...

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ieee visualization Conference (Vis 2006)/ieee symposium on Information visualization (InfoVis 2006)

作者： de Leeuw, Wim Verschure, Pernette J. van Liere, Robert CWI Ctr Math & Comp Sci NL-1009 AB Amsterdam Netherlands

In this paper we propose an approach in which interactive visualization and analysis are combined with batch tools for the processing of large data collections. large and heterogeneous data collections are difficult to analyze and pose specific problems to interactive visualization. Application of the traditional interactive processing and visualization approaches as well as batch processing encounter considerable drawbacks for such large and heterogeneous data collections due to the amount and type of data. Computing resources are not sufficient for interactive exploration of the data and automated analysis has the disadvantage that the user has only limited control and feedback on the analysis process. In our approach, an analysis procedure with features and attributes of interest for the analysis is defined interactively. This procedure is used for off-line processing of large collections of data sets. The results of the batch process along with "visual summaries" are used for further analysis. visualization is not only used for the presentation of the result, but also as a tool to monitor the validity and quality of the operations performed during the batch process. Operations such as feature extraction and attribute calculation of the collected data sets are validated by visual inspection. This approach is illustrated by an extensive case study, in which a collection of confocal microscopy data sets is analyzed.

关键词： biomedical visualization features in volume data sets large data set visualization

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RESEARCH ISSUES IN VECTOR AND TENSOR FIELD visualization

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ieee COMPUTER GRAPHICS AND APPLICATIONS 1994年第2期14卷 76-79页

作者： HESSELINK, L POST, FH VANWIJK, JJ DELFT UNIV TECHNOL DELFTNETHERLANDS NETHERLANDS ENERGY RES FDN PETTENNETHERLANDS

Today, computational fluid dynamics (CFD) research is almost impossible without computer-generated visualizations of the very large amounts of data resulting from numerical simulations. Although good techniques now exist for analysis of scalar data, most existing techniques for the visualization of vector fields-the predominant data type in CFD-meet only part of what is required. Common techniques such as arrow plots, streamlines, and particles work well for 2D applications, but for 3D data sets they often lead to cluttered displays. For tensors, which are much more complex and abstract entities the problem is even more severe. There is a real need for visualization, but there are no simple solutions. Many researchers have recognized this challenge and developed new techniques. We restrict ourselves to open research issues. We proceed in three ways. First, we propose a classification of existing vector and tensor field visualization techniques based on work by Delmarcelle and Hesselink (1994) and point out research gaps in this classification scheme. Second, we discuss feature-based visualization, which shows higher level descriptions derived from elementary data. Third, we consider the role of visualization in the research process, again revealing gaps in our current know-how concerning visualization of vector and tensor fields.

关键词： TENSOR FIELDS visualization data types Tensor Cloning Vectors classification scheme

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SeekAView: An Intelligent Dimensionality Reduction Strategy for Navigating High-Dimensional data Spaces 6

<i>SeekAView</i>: An Intelligent Dimensionality Reduction St...

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6th ieee symposium on large data analysis and visualization (LDAV)

作者： Krause, Josua Dasgupta, Aritra Fekete, Jean-Daniel Bertini, Enrico NYU New York NY 10003 USA Pacific Northwest Natl Lab Richland WA 99352 USA INRIA Le Chesnay France

ISBN: (纸本)9781509056590

Dealing with the curse of dimensionality is a key challenge in high-dimensional data visualization. We present SeekAView to address three main gaps in the existing research literature. First, automated methods like dimensionality reduction or clustering suffer from a lack of transparency in letting analysts interact with their outputs in real-time to suit their exploration strategies. The results often suffer from a lack of interpretability, especially for domain experts not trained in statistics and machine learning. Second, exploratory visualization techniques like scatter plots or parallel coordinates suffer from a lack of visual scalability: it is difficult to present a coherent overview of interesting combinations of dimensions. Third, the existing techniques do not provide a flexible workflow that allows for multiple perspectives into the analysis process by automatically detecting and suggesting potentially interesting subspaces. In SeekAView we address these issues using suggestion based visual exploration of interesting patterns for building and refining multidimensional subspaces. Compared to the state-of-the-art in subspace search and visualization methods, we achieve higher transparency in showing not only the results of the algorithms, but also interesting dimensions calibrated against different metrics. We integrate a visually scalable design space with an iterative workflow guiding the analysts by choosing the starting points and letting them slice and dice through the data to find interesting subspaces and detect correlations, clusters, and outliers. We present two usage scenarios for demonstrating how SeekAView can be applied in real-world data analysis scenarios.

关键词： High-Dimensional data Subspace Exploration Guided visualization

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Maximal Clique Enumeration with data-Parallel Primitives 7

Maximal Clique Enumeration with Data-Parallel Primitives

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ieee 7th symposium on large data analysis and visualization (LDAV)

作者： Lessley, Brenton Perciano, Talita Mathai, Manish Childs, Hank Bethel, E. Wes Univ Oregon Eugene OR 97403 USA Lawrence Berkeley Natl Lab Berkeley CA USA

ISBN: (纸本)9781538606179

The enumeration of all maximal cliques in an undirected graph is a fundamental problem arising in several research areas. We consider maximal clique enumeration on shared-memory, multi-core architectures and introduce an approach consisting entirely of data-parallel operations, in an effort to achieve efficient and portable performance across different architectures. We study the performance of the algorithm via experiments varying over benchmark graphs and architectures. Overall, we observe that our algorithm achieves up to a 33-time speedup and 9-time speedup over state-of-the-art distributed and serial algorithms, respectively, for graphs with higher ratios of maximal cliques to total cliques. Further, we attain additional speedups on a GPU architecture, demonstrating the portable performance of our data-parallel design.

关键词： Arrays Algorithm design and analysis Vegetation data visualization Graphics processing units

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