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

作者： Shih, Min Vishwanath, Venkatram Rizzi, Silvio Hereld, Mark Insley, Joseph Papka, Michael E. Uram, Thomas Ma, Kwan-Liu Univ Calif Davis Davis CA 95616 USA Argonne Natl Lab Argonne IL 60439 USA Northern Illinois Univ Argonne Natl Lab De Kalb IL 60115 USA

ISBN: (纸本)9781509056590

The benefits of applying advanced illumination models to volume visualization have been demonstrated by many researchers. For a parallel distributed, GPU computing environment, however, there is no efficient algorithm for scalable global illumination calculations. This paper presents a parallel, data-distributed and GPU-accelerated algorithm for volume rendering with advanced lighting. Our approach features tunable soft shadows for enhancing perception of complex spatial structures and relationships. For lighting calculations, our design effectively avoids data exchange among GPUs. Performance evaluation on a GPU cluster using up to 128 GPUs shows scalable rendering performance, with both the number of GPUs and volume data size.

关键词： Lighting Rendering (computer graphics) Scattering Computational modeling Graphics processing units Light sources Acceleration Lighting LIGHT SOURCES Graphics Processing Unit Computational modeling acceleration Scattering arithmetic visualization Parallel Lines efficient algorithm

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Narrative In Situ Visual analysis for large-Scale Ocean Eddy Evolution

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ieee COMPUTER GRAPHICS AND APPLICATIONS 2022年第3期42卷 65-73页

作者： Han, Xiaoyang Yu, Xiaomin Li, Guan Liu, Jun Zhao, Ying Shan, Guihua Univ Chinese Acad Sci Beijing 101408 Peoples R China Cent South Univ Sch Informat Sci & Engn Changsha 410017 Peoples R China

The rapid development of high-performance computing systems has led to a rapid increase in the speed of flow field simulation calculations. However, large-scale simulation output data lead to storage bottlenecks and inefficient data analysis. In this work, we used in situ visualization to process the simulation analysis of large-scale flow fields. Combined with narrative visual analysis, we designed a large-scale ocean flow field eddy evolution analysis system based on in situ visualization. Our system can generate high-precision eddy streamline structures in real time and supports eddy statistical analysis and tracking analysis at different ocean regional scales. Through the case data analysis of ocean simulation, we demonstrated the efficiency and effectiveness of the system.

关键词： Oceans visualization data visualization data models Computational modeling Analytical models Real-time systems

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Bandlimited OLAP Cubes for Interactive Big data visualization 5

Bandlimited OLAP Cubes for Interactive Big Data Visualizatio...

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

作者： Reach, Caleb North, Chris Virginia Tech Blacksburg VA 24061 USA

ISBN: (纸本)9781467385176

visualizations backed by data cubes can scale to massive datasets while remaining interactive. However, the use of data cubes introduces artifacts, causing these visualizations to appear noisy at best and deceptive at worst. Moreover, data cubes highly constrain the space of possible visualizations. For example, a histogram backed by a data cube is constrained to have a bin width that is a multiple of the data cube bin size. Similarly, for dynamic queries backed by data cubes, query extents must be aligned with bin boundaries. We present bandlimited OLAP (online analytical processing) cubes (BLOCs), a technique that uses established tools from digital signal processing to generate interactive visualizations of very large datasets. Based on kernel density plots and Gaussian filtering, BLOCs suppress the artifacts that occur in data cubes and allow for a continuous range of zoom/pan positions and continuous dynamic queries.

关键词： data cubes visualization Online analytical processing Cube bandlimited data visualisation Bin Interactive Digital signal processing big data

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Parallel Assemblies Plot, a visualization tool to explore categorical and quantitative data: application to digital mobility outcomes 16

Parallel Assemblies Plot, a visualization tool to explore ca...

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ieee 16th Pacific visualization symposium (ieee PacificVis)

作者： Cantu, Alma Mico-Amigo, Maria Encarna Del Din, Silvia Fernstad, Sara Johansson Newcastle Univ Sch Comp Newcastle Upon Tyne Tyne & Wear England Newcastle Univ Translat & Clin Res Inst Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9798350321241

This paper presents a tool enabling the visual analysis of multivariate heterogeneous data. large amounts of measured and contextual data are being gathered for a large number of applications, increasing connectivity across different data types. While measured data are often quantitative, contextual data tend to be categorical. This results in datasets containing multivariate data with heterogeneous properties. Difference in the natures of these properties raises challenges when combining them for analysis. This paper presents the design of a tool that enables the exploration of multivariate heterogeneous data by combining the strengths of Parallel Coordinates and Parallel Sets. The design relied on the application domain of real-life mobility monitoring that is particularly affected by the challenge mentioned above. To validate the suggested approach this paper presents the result of a usability evaluation, which confirms that the presented design is as efficient as other exiting tools while providing more features for correlation analysis.

关键词： Heterogeneous data Multivariate data visualization Technique Scientific visualization

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Statistical Projections for Multi-dimensional Visual data Exploration 6

Statistical Projections for Multi-dimensional Visual Data Ex...

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

作者： Nguyen, Hoa Stone, Daithi Bethel, E. Wes Univ Utah Salt Lake City UT 84112 USA Lawrence Berkeley Natl Lab Berkeley CA USA

ISBN: (纸本)9781509056590

When working with large, multidimensional and multivariate data, science users are frequently interested in understanding variation in data, as opposed to the actual data values. Our work focuses on exploring how a simple statistical metric, the Coefficient of Variation (or C-v), can be used in several different ways to facilitate understanding variation in large data. As a statistical measure, it offers a key advantage over more widely accepted measures like standard deviation, namely to its ability to capture local variation properties. As a multidimensional projection operator, C-v is an effective way of reducing data size while preserving the key variational signal. visualizations produced from C-v that target conveying variation in data are highly informative, especially compared to those produced with more widely known methods. We demonstrate these ideas within the context of a two-part application case study focusing on understanding long-term trends in the the changes in precipitation and winds in large-scale climate model ensemble output.

关键词： Climate models

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Tracking Space-Filling Structures in Turbulent Flows 5

Tracking Space-Filling Structures in Turbulent Flows

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

作者： Schnorr, Andrea Goebbert, Jens Henrik Kuhlen, Torsten W. Hentschel, Bernd Rhein Westfal TH Aachen Virtual Real Grp Aachen Germany Forschungszentrum Julich Julich Supercomp Ctr D-52425 Julich Germany JARA High Performance Comp Aachen Germany

ISBN: (纸本)9781467385176

We present a novel approach for tracking space-filling features, i.e. a set of features which covers the entire domain. In contrast to previous work, we determine the assignment between features from successive time steps by computing a globally optimal, maximum-weight, maximal matching on a weighted, bi-partite graph. We demonstrate the method's functionality by tracking dissipation elements (DEs), a space-filling structure definition from turbulent flow analysis. The ability to track DEs over time enables researchers from fluid mechanics to extend their analysis beyond the assessment of static flow fields to time-dependent settings.

关键词： Feature Tracking Weighted Bi-Partite Matching Flow visualization Dissipation Elements

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Out-of-Core visualization of Time-Varying Hybrid-Grid Volume data 4

Out-of-Core Visualization of Time-Varying Hybrid-Grid Volume...

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ieee symposium on large data analysis and visualization

作者： Shih, Min Zhang, Yubo Ma, Kwan-Liu Sitaraman, Jayanarayanan Mavriplis, Dimitri Univ Calif Davis Davis CA 95616 USA Univ Wyoming Laramie WY 82071 USA

ISBN: (纸本)9781479952151

Traditional computational fluid dynamics (CFD) solvers are usually written for a single gridding paradigm such as structured-Cartesian, structured-body-fitted, or unstructured grids. Each type of mesh paradigms has inherent advantages and disadvantages. Thus, the methods of coupling multiple mesh paradigms have been developed to facilitate the use of different solvers in different part of the computational domain. However, the complex hybrid gridding paradigm poses challenges to rendering calculations for visualizing the data. This paper describes a volume visualization system for time-varying adaptive moving-body CFD datasets, where the grid system consists of unstructured grids near the body surface, coupled with Structured Adaptive Mesh Refinement (SAMR) grid in the off-body domain. We present two approaches to the hybrid-grid volume ray casting: a KD-tree based single-pass algorithm, and a multi-pass algorithm using the depth peeling technique. The system has a three-level memory hierarchy: GPU memory, main memory, and a solid state drive (SSD). Through data caching and prefetching within the memory hierarchy, the latency of time-step swapping can be hidden. Experimental results show that our system allows interactive volume exploration on single-GPU commodity PCs.

关键词： Computational fluid dynamics

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Speculative Progressive Raycasting for Memory Constrained Isosurface visualization of Massive Volumes 13

Speculative Progressive Raycasting for Memory Constrained Is...

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13th ieee symposium on large data analysis and visualization (LDAV)

作者： Usher, Will Dyken, Landon Kumar, Sidharth Intel Luminary Cloud Mountain View CA 94035 USA Univ Illinois Chicago IL USA

ISBN: (纸本)9798350330182

New web technologies have enabled the deployment of powerful GPU-based computational pipelines that run entirely in the web browser, opening a new frontier for accessible scientific visualization applications. However, these new capabilities do not address the memory constraints of lightweight end-user devices encountered when attempting to visualize the massive data sets produced by today's simulations and data acquisition systems. In this paper, we propose a novel implicit isosurface rendering algorithm for interactive visualization of massive volumes within a small memory footprint. We achieve this by progressively traversing a wavefront of rays through the volume and decompressing blocks of the data on-demand to perform implicit ray-isosurface intersections. The progressively rendered surface is displayed after each pass to improve interactivity. Furthermore, to accelerate rendering and increase GPU utilization, we introduce speculative ray-block intersection into our algorithm, where additional blocks are traversed and intersected speculatively along rays as other rays terminate to exploit additional parallelism in the workload. Our entire pipeline is run in parallel on the GPU to leverage the parallel computing power that is available even on lightweight end-user devices. We compare our algorithm to the state of the art in low-overhead isosurface extraction and demonstrate that it achieves 1.7x-5.7x reductions in memory overhead and up to 8.4x reductions in data decompressed.

关键词： isosurface rendering raycasting volume rendering

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Bridging the Gap of Network Management and Anomaly Detection through Interactive visualization

Bridging the Gap of Network Management and Anomaly Detection...

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ieee Pacific visualization symposium (PacificVis)

作者： Zhang, Tao Liao, Qi Shi, Lei Cent Michigan Univ Dept Comp Sci Mt Pleasant MI 48859 USA Chinese Acad Sci State Key Lab Comp Sci Inst Software Beijing 100864 Peoples R China

ISBN: (纸本)9781479928736

large-scale networks have become increasingly challenging to manage. It is vital for a system administrator or network manager to be able to analyze the vast amount of log data in order to detect suspicious behaviors or patterns, possibly due to malicious users/applications or faulty devices. While an intrusion detection system (IDS) log can provide a large number of warnings, exactly which alarms are true while the others are false, and more importantly what are the underlying causes are still difficult to know. To bridge the gap between network log and anomaly discovery, we design and implement a visualization tool that combines multiple commodity visualizations with minimum learning curve. While each individual view is well understood, the effects of such views in analyzing network anomalies are not well studied. Since each visualization technique has advantages as well as limitations in addressing a particular task, we show that these views, when combined and linked together, may provide an effective and lightweight network anomaly analysis tool. The web-based open platform may simplify network administration as well as promote collaborative analysis among researchers.

关键词： Internet computer network management computer network security data analysis data visualisation IDS log Web-based open platform anomaly detection collaborative analysis faulty devices interactive visualization intrusion detection system log large-scale networks log data analysis malicious applications malicious users minimum learning curve multiple commodity visualizations network anomaly analysis tool network management suspicious behavior detection suspicious pattern detection data visualization Image color analysis Joining processes Market research Security Servers visualization Network Anomaly visualization

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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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