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Spatio-Temporal Visual Analysis of Turbulent Superstructures in Unsteady Flow

ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2024年第7期30卷 3346-3358页

作者： Ghaffari, Behdad Gatti, Davide Westermann, Rudiger Tech Univ Munich D-80333 Munich Germany Karlsruhe Inst Technol D-76131 Karlsruhe Germany

The large-scale motions in 3D turbulent channel flows, known as Turbulent Superstructures (TSS), play an essential role in the dynamics of small-scale structures within the turbulent boundary layer. However, as of today, there is no common agreement on the spatial and temporal relationships between these multiscale structures. We propose a novel space-time visualization technique for analyzing the temporal evolution of these multiscale structures in their spatial context and, thus, to further shed light on the conceptually different explanations of their dynamics. Since the temporal dynamics of TSS are believed to influence the structures in the turbulent boundary layer, we propose a combination of a 2D space-time velocity plot with an orthogonal 2D plot of projected 3D flow structures, which can interactively span the time and the space axis. Besides flow structures indicating the fluid motion, we propose showing the variations in derived fields as an additional source of explanation. The relationships between the structures in different spatial and temporal scales can be more effectively resolved by using various filtering operations and image registration algorithms. To reduce the information loss due to the non-injective nature of projection, spatial information is encoded into transparency or color. Since the proposed visualization is heavily demanding computational resources and memory bandwidth to stream unsteady flow fields and instantly compute derived 3D flow structures, the implementation exploits data compression, parallel computation capabilities, and high memory bandwidth on recent GPUs via the CUDA compute library.

关键词： Three-dimensional displays visualization graphics processing units Periodic structures data visualization Feature extraction Bandwidth Flow visualization large-scale data techniques animation and motion-related techniques

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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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Proceedings - ieee symposium on Volume visualization and graphics 2004. VolVis 2004

Proceedings - IEEE Symposium on Volume Visualization and Gra...

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Proceedings - ieee symposium on Volume visualization and graphics 2004. VolVis 2004

ISBN: (纸本)0780387813

The proceedings contain 15 papers from the ieee symposium on Volume visualization and graphics 2004. VolVis 2004. The topics discussed include: memory efficient acceleration structures and techniques for CPU-based volume raycasting of large data;a fast high accuracy volume renderer for unstructured data;hybrid hardware-accelerated image composition for sort-last parallel rendering on graphics clusters with commodity image compositor;transfer function based adaptive decompression for volume rendering of large medical data sets;interactive transfer function control for Monte Carlo volume rendering;feature preserving distance fields;volume interval segmentation and rendering;and texture-encoded tetrahedral strips.

关键词： visualization

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OpenGL Volumizer: A toolkit for high quality volume rendering of large data sets

OpenGL Volumizer: A toolkit for high quality volume renderin...

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7th symposium on Volume visualization and graphics

作者： Bhainramka, P Demange, Y SGI United States

ISBN: (纸本)0780376412

We present the OpenGL Volumizer API for interactive, high-quality, scalable visualization of large volumetric data sets. Volumizer provides a high-level interface to OpenGL hardware to allow application writers and researchers to visualize multiple gigabytes of volumetric data. Use of multiple graphics pipes scales rendering performance and system resources including pixel-fill rate and texture memory size. Volume roaming and multi-resolution volume rendering provide alternatives for interactive visualization of volume data. We combine the concepts of roaming and multi-resolution to introduce 3D Clip-Textures, an efficient technique to visualize arbitrarily large volume data by judiciously organizing and paging them to local graphics resources from storage peripherals. Volumetric shaders provide an interface for high quality volume rendering along with implementing new visualization techniques. This paper gives an overview of the API along with a discussion of large data visualization techniques used by Volumizer.

关键词： volume visualization large data texture mapping clip-textures

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Distributed shared memory for roaming large volumes

Distributed shared memory for roaming large volumes

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

作者： Castanie, Laurent Mion, Christophe Cavin, Xavier Levy, Bruno INRIA Earth Desis Nancy France INRIA ALICE Grp Nancy France

We present a cluster-based volume rendering system for roaming very large volumes. This system allows to move a gigabyte-sized probe inside a total volume of several tens or hundreds of gigabytes in real-time. While the size of the probe is limited by the total amount of texture memory on the cluster, the size of the total data set has no theoretical limit. The cluster is used as a distributed graphics processing unit that both aggregates graphics power and graphics memory. A hardware-accelerated volume renderer runs in parallel on the cluster nodes and the final image compositing is implemented using a pipelined sort-last rendering algorithm. Meanwhile, volume bricking and volume paging allow efficient data caching. On each rendering node, a distributed hierarchical cache system implements a global software-based distributed shared memory on the cluster. In case of a cache miss, this system first checks page residency on the other cluster nodes instead of directly accessing local disks. Using two Gigabit Ethernet network interfaces per node, we accelerate data fetching by a factor of 4 compared to directly accessing local disks. The system also implements asynchronous disk access and texture loading, which makes it possible to overlap data loading, volume slicing and rendering for optimal volume roaming.

关键词： large volumes volume roaming out-of-core hierarchical caching distributed shared memory hardware-accelerated volume visualization graphics hardware parallel rendering graphics cluster

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ieee symposium on large data Analysis and visualization 2013, LDAV 2013 - Proceedings

IEEE Symposium on Large Data Analysis and Visualization 2013...

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2013 3rd ieee symposium on large-Scale data Analysis and visualization, LDAV 2013

ISBN: (纸本)9781479916597

The proceedings contain 23 papers. The topics discussed include: distributed parallel particle advection using work requesting;a scalable algorithm for single-linkage hierarchical clustering on distributed-memory architectures;portable data-parallel visualization and analysis in distributed memory environments;efficient parallel volume rendering of large-scale adaptive mesh refinement data;visualizing large-scale atomistic simulations in ultra-resolution immersive environments;a provably-robust sampling method for generating colormaps of large data;trelliscope: a system for detailed visualization in the deep analysis of large complex data;filtering edge for exploration of large graphs;proper orthogonal decomposition based parallel compression for visualizing big data on the K Computer;and visual analysis on online display advertising data.

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A unified infrastructure for parallel out-of-core isosurface extraction and volume rendering of unstructured grids

A unified infrastructure for parallel out-of-core isosurface...

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ieee symposium on parallel and large-data visualization and graphics

作者： Chiang, YJ Farias, R Silva, CT Wei, B Polytech Univ Dept Comp & Informat Sci Brooklyn NY 11201 USA

ISBN: (纸本)0780372239

In this paper, we present a unified infrastructure for parallel out-of-ore isosurface extraction and volume rendering of large unstructured grids on distributed-memory parallel machines. We parallelize the out-of-core isosurface extraction algorithm of [9] and the out-of-core ZSweep technique [17] for direct volume rendering, using the meta-cell technique as a unified underlying building block. Our one-time preprocessing first partitions the dataset into meta-ells that are stored in disk. From the meta-cells, we build a BBIO tree in disk, which can be used to speed up isosurface extraction, and a bounding-box file in disk, which is used for direct volume rendering. At run-time, we use a simple self-scheduling scheme [39] to achieve load balancing among the processors, We perform several experiments on a sixteen-node cluster of PCs connected by a gigabit ethernet, using datasets as large as 6.6 million cells. For the larger datasets, we have found that both our isosurface extraction and direct volume rendering approaches are perfectly scalable up to sixteen nodes.

关键词： isosurface extraction volume rendering parallel computation out-of-core techniques scientific visualization

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visualization of vessel movements

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COMPUTER graphics FORUM 2009年第3期28卷 959-966页

作者： Willems, Niels van de Wetering, Huub van Wijk, Jarke J. Eindhoven Univ Technol Dept Math & Comp Sci NL-5600 MB Eindhoven Netherlands

We propose a geographical visualization to support operators of coastal surveillance systems and decision making analysts to get insights in vessel movements. For a possibly unknown area, they want to know where significant maritime areas, like highways and anchoring zones, are located. We show these features as an overlay, on a map. As source data we use A IS data: Many vessels are currently equipped with advanced GPS devices that frequently sample the state of the vessels and broadcast them. Our visualization is based on density fields that are derived from convolution of the dynamic vessel positions with a kernel. The density fields are shown as illuminated height maps. Combination of two fields, with a large and small kernel provides overview and detail. A large kernel provides an overview of area usage revealing vessel highways. Details of speed variations of individual vessels are shown with a small kernel, highlighting anchoring zones where multiple vessels stop. Besides for maritime applications we expect that this approach is useful for the visualization of moving object data in general.

关键词： Computer graphics [I.3.3]: Line and Curve Generation

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parallel visualization on large clusters using MapReduce

Parallel visualization on large clusters using MapReduce

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1st ieee symposium on large-Scale data Analysis and visualization 2011, LDAV 2011

作者： Vo, Huy T. Bronson, Jonathan Summa, Brian Comba, João L.D. Freire, Juliana Howe, Bill Pascucci, Valerio Silva, Cláudio T. Polytechnic Institute New York University United States SCI Institute University of Utah United States Instituto de Informática UFRGS Brazil eScience Institute University of Washington United States

ISBN: (纸本)9781467301541

large-scale visualization systems are typically designed to efficiently "push" datasets through the graphics hardware. However, exploratory visualization systems are increasingly expected to support scalable data manipulation, restructuring, and querying capabilities in addition to core visualization algorithms. We posit that new emerging abstractions for parallel data processing, in particular computing clouds, can be leveraged to support large-scale data exploration through visualization. In this paper, we take a first step in evaluating the suitability of the MapReduce framework to implement large-scale visualization techniques. MapReduce is a lightweight, scalable, general-purpose parallel data processing framework increasingly popular in the context of cloud computing. Specifically, we implement and evaluate a representative suite of visualization tasks (mesh rendering, isosurface extraction, and mesh simplification) as MapReduce programs, and report quantitative performance results applying these algorithms to realistic datasets. For example, we perform isosurface extraction of up to l6 isovalues for volumes composed of 27 billion voxels, simplification of meshes with 30GBs of data and subsequent rendering with image resolutions up to 800002 pixels. Our results indicate that the parallel scalability, ease of use, ease of access to computing resources, and fault-tolerance of MapReduce offer a promising foundation for a combined data manipulation and data visualization system deployed in a public cloud or a local commodity cluster. © 2011 ieee.

关键词： MapReduce

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Traveler: Navigating Task parallel Traces for Performance Analysis

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2023年第1期29卷 788-797页

作者： Sakin, Sayef Azad Bigelow, Alex Tohid, R. Scully-Allison, Connor Scheidegger, Carlos Brandt, Steven R. Taylor, Christopher Huck, Kevin A. Kaiser, Hartmut Isaacs, Katherine E. Univ Arizona Tucson AZ 85721 USA Louisiana State Univ Baton Rouge LA 70803 USA RStudio Boston MA USA Tact Comp Labs Muenster TX USA Univ Utah Salt Lake City UT 84112 USA

Understanding the behavior of software in execution is a key step in identifying and fixing performance issues. This is especially important in high performance computing contexts where even minor performance tweaks can translate into large savings in terms of computational resource use. To aid performance analysis, developers may collect an execution trace-a chronological log of program activity during execution. As traces represent the full history, developers can discover a wide array of possibly previously unknown performance issues, making them an important artifact for exploratory performance analysis. However, interactive trace visualization is difficult due to issues of data size and complexity of meaning. Traces represent nanosecond-level events across many parallel processes, meaning the collected data is often large and difficult to explore. The rise of asynchronous task parallel programming paradigms complicates the relation between events and their probable cause. To address these challenges, we conduct a continuing design study in collaboration with high performance computing researchers. We develop diverse and hierarchical ways to navigate and represent execution trace data in support of their trace analysis tasks. Through an iterative design process, we developed Traveler, an integrated visualization platform for task parallel traces. Traveler provides multiple linked interfaces to help navigate trace data from multiple contexts. We evaluate the utility of Traveler through feedback from users and a case study, finding that integrating multiple modes of navigation in our design supported performance analysis tasks and led to the discovery of previously unknown behavior in a distributed array library.

关键词： software visualization parallel computing traces performance analysis event sequence visualization

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