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CHI Conference on Human Factors in Computing Sytems (CHI)

作者： Chen, Juntong Huang, Haiwen Ye, Huayuan Peng, Zhong Li, Chenhui Wang, Changbo East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China East China Normal Univ State Key Lab Estuarine & Coastal Res Shanghai Peoples R China

ISBN: (纸本)9798400703300

The voluminous nature of geospatial temporal data from physical monitors and simulation models poses challenges to efficient data access, often resulting in cumbersome temporal selection experiences in web-based data portals. Thus, selecting a subset of time steps for prioritized visualization and pre-loading is highly desirable. Addressing this issue, this paper establishes a multifaceted definition of salient time steps via extensive need-finding studies with domain experts to understand their workflows. Building on this, we propose a novel approach that leverages autoencoders and dynamic programming to facilitate user-driven temporal selections. Structural features, statistical variations, and distance penalties are incorporated to make more flexible selections. User-specified priorities, spatial regions, and aggregations are used to combine different perspectives. We design and implement a web-based interface to enable efficient and context-aware selection of time steps and evaluate its efficacy and usability through case studies, quantitative evaluations, and expert interviews.

关键词： Key Time Selection Geospatial data visualization Design large-scale data visualization Need-finding Study

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An accelerated rendering scheme for massively large point cloud data

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JOURNAL OF SUPERCOMPUTING 2020年第10期76卷 8313-8323页

作者： Baek, Nakhoon Yoo, Kwan-Hee Kyungpook Natl Univ Sch Comp Sci & Engn Daegu 41566 South Korea Chungbuk Natl Univ Dept Comp Sci Cheongju 28644 Chungbuk South Korea

In the field of large-scale data visualization, the graphics rendering speed is one of the most important factors for its application development. Since the large-scale data visualization usually requires three-dimensional representations, the three-dimensional graphics libraries such as OpenGL and DirectX have been widely used. In this paper, we suggest a new way of accelerated rendering, through directly using the direct rendering manager packets. Current three-dimensional graphics features are focused on the efficiency of general purpose rendering pipelines. In contrast, we concentrated on the speed-up of the special-purpose rendering pipeline, for point cloud rendering. Our result shows that we achieved our purpose effectively.

关键词： large-scale data visualization Direct rendering manager Graphics acceleration Point rendering

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RSATree: Distribution-Aware data Representation of large-scale Tabular datasets for Flexible Visual Query

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2020年第1期26卷 1161-1171页

作者： Mei, Honghui Chen, Wei Yating, Wei Hu, Yuanzhe Zhou, Shuyue Lin, Bingru Ying, Zhao Xia, Jiazhi Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China

Analysts commonly investigate the data distributions derived from statistical aggregations of data that are represented by charts, such as histograms and binned scatterplots, to visualize and analyze a large-scale dataset. Aggregate queries are implicitly executed through such a process. datasets are constantly extremely large;thus, the response time should be accelerated by calculating predefined data cubes. However, the queries are limited to the predefined binning schema of preprocessed data cubes. Such limitation hinders analysts' flexible adjustment of visual specifications to investigate the implicit patterns in the data effectively. Particularly, RSATree enables arbitrary queries and flexible binning strategies by leveraging three schemes, namely, an R-tree-based space partitioning scheme to catch the data distribution, a locality-sensitive hashing technique to achieve locality-preserving random access to data items, and a summed area table scheme to support interactive query of aggregated values with a linear computational complexity. This study presents and implements a web-based visual query system that supports visual specification, query, and exploration of large-scale tabular data with user-adjustable granularities. We demonstrate the efficiency and utility of our approach by performing various experiments on real-world datasets and analyzing time and space complexity.

关键词： Aggregate query visual query large-scale data visualization R-tree summed area table hashing

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Hybrid pipelining approach to image alignment for largescale brain image data

Hybrid pipelining approach to image alignment for largescale...

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7th IASTED International Conference on Computer Graphics and Imaging

作者： Sengupta, R Meyer, J Zhang, ZH Univ Calif Irvine Irvine CA 92697 USA

ISBN: (纸本)0889864187

Creating thin sections of frozen tissue in the order of a few microns and then manually mounting the resulting slices on glass plates is a common technique in brain imaging. large collections of such manually mounted cryosections are available for digital archiving. Scanning these slices at high resolution helps preserving them for future generations. The obtained data can also be used to restore the original shape of the specimen, i.e., to create a three-dimensional model. This task usually requires time-consuming individual alignment of the slices. We present a framework that uses a pipelining approach to aid in the alignment process of a large data set, and to automate most of the steps.

关键词： image registration large-scale data visualization histograms thresholding feature extraction

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Parallel visualization of gigabyte datasets in GeoFEM

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2002年第6-7期14卷 521-530页

作者： Fujishiro, I Chen, L Takeshima, Y Nakamura, H Suzuki, Y Res Org Informat Sci & Technol Tokyo Japan Ochanomizu Univ Tokyo 112 Japan Tohoku Univ Sendai Miyagi 980 Japan

An initial overview of parallel visualization in the GeoFEM software system is provided. Our visualization subsystem offers many kinds of parallel visualization methods for the users to visualize their huge finite-element analysis datasets for scalar, vector and/or tensor fields at a reasonable cost. A polygonal simplification scheme is developed to make the transmission and rendition of output graphic primitives more efficient. A salient feature of the subsystem lies in its capability in the automatic setting of visualization parameter values based on the analysis of scalar/flow field topology and volumetric coherence, to improve the quality of visualization results with a minimized number of batch re-executions. Representative experimental results illustrate the effectiveness of our subsystem. Copyright (C) 2002 John Wiley Sons, Ltd.

关键词： scientific visualization parallel visualization large-scale data visualization volume visualization flow visualization polygonal simplification feature analysis

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Adaptive fused visualization for large-scale blood flow dataset with particle-based rendering

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JOURNAL OF visualization 2015年第2期18卷 133-145页

作者： Zhao, Kun Sakamoto, Naohisa Koyamada, Koji Kyoto Univ Grad Sch Engn Kyoto Japan Kyoto Univ Inst Liberal Arts & Sci Kyoto Japan

The visualization for the large-scale volume dataset obtained from blood flow simulations needs to be performed to help researchers to confirm the detailed positional information of the red blood cells, platelets and vascular walls. However, such a large-scale dataset is difficult to be visualized with an interactive frame rate on a normal computer. Nevertheless, the fusion of different objects, such as red blood cell volumes, platelet volumes and vascular wall surfaces, is also needed in the visualization, which makes it even more challenging. To solve this problem, we propose a system for visualizing such data by applying the particle-based rendering (PBR) (Sakamoto et al. Comput Graph 34(1):34-42, 2010);(Sakamoto and Koyamada Ultra Vis 176-185, 2012) method. This rendering method does not require any visibility sorting;thus, it can handle large-scale volume dataset, and the fusion of different objects is also easy to be implemented. To achieve an interactive frame rate analysis while maintaining good image quality, we combine two types of PBR in the system: object-space PBR (O-PBR) (Sakamoto et al. Comput Graph 34(1):34-42, 2010) and image-space PBR (I-PBR) (Sakamoto and Koyamada Ultra Vis 176-185, 2012). O-PBR method can handle large-scale data at high rendering speed but the image quality is not adequate when the view is enlarged. I-PBR method can provide high-quality rendering but have a limitation for the renderable data size. To obtain a best performance for blood flow visualization, our system adaptively switches O-PBR and I-PBR based on the data size within the view frustum and the computer resources. With this proposed adaptive visualization approach, the large-scale blood flow dataset can be efficiently visualized with the fusion of different objects while maintaining an interactive frame rate and good image quality.

关键词： Adaptive visualization Fused visualization large-scale data visualization Blood flow Particle-based rendering Scientific application

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Two-stage compression for fast volume rendering of time-varying scalar data 06

Two-stage compression for fast volume rendering of time-vary...

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Proceedings of the 4th international conference on Computer graphics and interactive techniques in Australasia and Southeast Asia

作者： Daisuke Nagayasu Fumihiko Ino Kenichi Hagihara Osaka University

ISBN: (纸本)9781595935649

This paper presents a two-stage compression method for accelerating GPU-based volume rendering of time-varying scalar data. Our method aims at reducing transfer time by compressing not only the data transferred from disk to main memory but also that from main memory to video memory. In order to achieve this reduction, the proposed method uses packed volume texture compression (PVTC) and Lempel-Ziv-Oberhumer (LZO) compression as a lossy compression method on the GPU and a lossless compression method on the CPU, respectively. This combination realizes efficient compression exploiting both temporal and spatial coherence in time-varying data. We also present experimental results using scientific and medical datasets. In the best case, our method produces 56% more frames per second, as compared with a single-stage (GPU-based) compression method. With regard to the quality of images, we obtain permissible results ranging from approximately 30 to 50 dB in terms of PSNR (peak signal-to-noise ratio).

关键词： graphics hardware large-scale data visualization temporal coherence data compression spatial coherence

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Hybrid image composition mechanism for enhancing volume graphics clusters

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2005年第11期E88D卷 2582-2590页

作者： Nonaka, J Kukimoto, N Ebara, Y Ogata, M Iwashita, T Kanazawa, M Koyamada, K Kyoto Univ Grad Sch Informat Kyoto 6068501 Japan Kyoto Univ Grad Sch Engn Kyoto 6068501 Japan Tohwa Univ Dept Informat Fukuoka 8158510 Japan Kyoto Univ Acad Ctr Comp & Media Studies Kyoto 6068501 Japan Mitsubishi Precis Co Ltd Tokyo 1080073 Japan Kyoto Univ Ctr Promot Excellence High Educ Kyoto 6068501 Japan

Volume Graphics Clusters (VG Clusters) have proven to be efficient in a wide range of visualization applications and have also shown promise in some other applications where the image composition device could be fully utilized. The main differentiating feature from other graphics clusters is a specialized image composition device, commercially available as the MPC Image Compositor, which enables the building of do-it-yourself VG Clusters. Although this device is highly scalable, the unidirectional composition flow limits the data subdivision to the quantity of physically available rendering nodes. In addition, the limited buffer memory limits the maximum capable image composition size, therefore limiting its use in large-scale data visualization and high-resolution visualization. To overcome these limitations, we propose and evaluate an image composition mechanism in which additional hardware is used for assisting the image composition process. Because of the synergistic use of two distinct image composition hardware devices we named it "Hybrid Image Composition". Some encouraging results were obtained showing the effectiveness of this solution in improving the VG Cluster 's potential. A low-cost parallel port based hardware barrier is also presented as an efficient method for further enhancing this kind of small-scale VG Cluster. Moreover, this solution has proven to be especially useful in clusters built using low-speed networks, such as Fast Ethernet, which are still in common use.

关键词： volume graphics cluster image composition parallel rendering large-scale data visualization high-resolution visualization

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NVIDIA IndeX accelerated computing for visualizing Cholla's galactic winds

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PARALLEL COMPUTING 2021年 107卷

作者： Schneider, Evan Robertson, Brant Kuhn, Alexander Lux, Christopher Nienhaus, Marc Univ Pittsburgh Pittsburgh PA 15260 USA Univ Calif Santa Cruz Santa Cruz CA 95064 USA NVIDIA Santa Clara CA 95051 USA

Galactic winds - outflows of gas driven out of galaxies by the combined effects of thousands of supernovae - are a crucial feature of galaxy evolution. Despite their importance, a complete theoretical picture of these winds has been elusive. Simulating the complicated interaction between the hot, high pressure gas created by supernovae and the cooler, high density gas in the galaxy disk requires massive computational resources and sophisticated software. For this purpose, Computational Hydrodynamics On Parallel Architectures (Cholla) has been demonstrated to be a scalable and efficient tool that operates in large, distributed multi-GPU environments at high levels of performance. This additional performance results in unprecedented resolution for this type of simulation and massive amounts of generated data. This raises the requirements for analysis tools that can cope with scale and complexity of the simulated physical processes. To address those challenges, we utilize NVIDIA IndeX as a scalable framework to visualize the simulation output: NVIDIA IndeX features a streaming-based architecture to interactively explore simulation results in large-scale, multi-GPU environments. We utilize customized sampling programs for multi-volume and surface rendering to address analysis questions of galactic wind simulations. This combination of massively parallel simulation and analysis allows us to utilize recent supercomputer capabilities and to speed up the exploration of galactic wind simulations.

关键词： Scientific visualization large-scale data visualization Astrophysics Supercomputing

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