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

作者： Morozov, Dmitriy Peterka, Tom Lawrence Berkeley Natl Lab Berkeley CA USA Argonne Natl Lab Argonne IL 60439 USA

ISBN: (纸本)9781509056590

DIY2 is a programming model and runtime for block-parallel analytics on distributed-memory machines. Its main abstraction is block-structured data parallelism: data are decomposed into blocks;blocks are assigned to processing elements (processes or threads);computation is described as iterations over these blocks, and communication between blocks is defined by reusable patterns. By expressing computation in this general form, the DIY2 runtime is free to optimize the movement of blocks between slow and fast memories (disk and flash vs. DRAM) and to concurrently execute blocks residing in memory with multiple threads. This enables the same program to execute in-core, out-of-core, serial, parallel, single-threaded, multithreaded, or combinations thereof. This paper describes the implementation of the main features of the DIY2 programming model and optimizations to improve performance. DIY2 is evaluated on complete analysis codes.

关键词： Computational modeling Runtime Programming Analytical models Message systems data models data analysis Runtime Computer based information systems data models Programming ANALYSIS MODELS Computational modeling data analysis Blocks memory machine

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EGPGV 2022 - 22nd Eurographics symposium on parallel graphics and visualization

EGPGV 2022 - 22nd Eurographics Symposium on Parallel Graphic...

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22nd Eurographics symposium on parallel graphics and visualization, EGPGV 2022

ISBN: (纸本)9783038681755

The proceedings contain 8 papers. The topics discussed include: rainbow: a rendering-aware index for high-quality spatial scatterplots with result-size budgets & profiling and visualizing GPU memory access and cache behavior of ray tracers;iterative discrete element solver for efficient snow simulation;massively parallel large scale inundation modelling & a flexible data streaming design for interactive visualization of large-scale volume data;automatic in situ camera placement for isosurfaces of large-scale scientific simulations;design and evaluation of a GPU streaming framework for visualizing time-varying AMR data;and GraphWaGu: GPU powered large scale graph layout computation and rendering for the web.

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Application of Stochastic Point-based Rendering to Transparent visualization of large-scale Laser-scanned data of 3D Cultural Assets

Application of Stochastic Point-based Rendering to Transpare...

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ieee Pacific visualization symposium (PacificVis)

作者： Tanaka, Satoshi Uemura, Makoto Hasegawa, Kyoko Kitagawa, Takehiko Yoshida, Takahiro Sugiyama, Asuka Tanaka, Hiromi T. Okamoto, Atsushi Sakamoto, Naohisa Koyamada, Koji Ritsumeikan Univ Kyoto Japan Otemae Univ Nishinomiya Hyogo Japan Kyoto Univ Kyoto 6068501 Japan

ISBN: (纸本)9781479928736

We propose a new application of stochastic point-based rendering, which was recently proposed for implicit surfaces, to large-scale laser-scanned 3D point data. Specifically, we propose a scheme to apply the rendering to transparent and fused visualization of recent large and complex laser-scanned data from cultural assets. Our scheme uses 3D points that are directly acquired using a laser scanner as the rendering primitives. For laser-scanned data that consist of more than 10(7) or 10(8) 3D points, the pre-processing stage takes only a few minutes, and the rendering stage is executable at interactive frame rates. We do not encounter rendering artifacts originating from the indefiniteness of depth-sorted orders of rendering primitives. Fused visualization with various visual assistants is also possible. We demonstrate the effectiveness of our scheme by visualizing a campus building and a culturally important festival float.

关键词： Computer graphics [I.3.m]: Miscellaneous Image Processing and Computer Vision [I.4.8]: Scene Analysis Range data

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Adaptive Collision Culling for Massive Simulations by a parallel and Context-Aware Sweep and Prune Algorithm

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2018年第7期24卷 2064-2077页

作者： Capannini, Gabriele Larsson, Thomas Malardalen Univ Sch Innovat Design & Engn S-72123 Vasteras Sweden

We present an improved parallel Sweep and Prune algorithm that solves the dynamic box intersection problem in three dimensions. It scales up to very large datasets, which makes it suitable for broad phase collision detection in complex moving body simulations. Our algorithm gracefully handles high-density scenarios, including challenging clustering behavior, by using a double-axis sweeping approach and a cache-friendly succinct data structure. The algorithm is realized by three parallel stages for sorting, candidate generation, and object pairing. By the use of temporal coherence, our sorting stage runs with close to optimal load balancing. Furthermore, our approach is characterized by a work-division strategy that relies on adaptive partitioning, which leads to almost ideal scalability. In addition, for scenarios that involves intense clustering along several axes simultaneously, we propose an enhancement that increases the context-awareness of the algorithm. By exploiting information gathered along three orthogonal axes, an efficient choice of what range query to perform can be made per object during run-time. Experimental results show high performance for up to millions of objects on modern multi-core CPUs.

关键词： Collision detection simulation parallel algorithms multicore processing multithreading tree data structures sorting

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Real-Time Visual Analysis of High-Volume Social Media Posts

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2022年第1期28卷 879-889页

作者： Knittel, Johannes Koch, Steffen Tang, Tan Chen, Wei Wu, Yingcai Liu, Shixia Ertl, Thomas Univ Stuttgart Stuttgart Germany Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China Tsinghua Univ Beijing Peoples R China

Breaking news and first-hand reports often trend on social media platforms before traditional news outlets cover them. The real-time analysis of posts on such platforms can reveal valuable and timely insights for journalists, politicians, business analysts, and first responders, but the high number and diversity of new posts pose a challenge. In this work, we present an interactive system that enables the visual analysis of streaming social media data on a large scale in real-time. We propose an efficient and explainable dynamic clustering algorithm that powers a continuously updated visualization of the current thematic landscape as well as detailed visual summaries of specific topics of interest. Our parallel clustering strategy provides an adaptive stream with a digestible but diverse selection of recent posts related to relevant topics. We also integrate familiar visual metaphors that are highly interlinked for enabling both explorative and more focused monitoring tasks. Analysts can gradually increase the resolution to dive deeper into particular topics. In contrast to previous work, our system also works with non-geolocated posts and avoids extensive preprocessing such as detecting events. We evaluated our dynamic clustering algorithm and discuss several use cases that show the utility of our system.

关键词： Social networking (online) Heuristic algorithms data visualization Clustering algorithms Real-time systems Text analysis Visual analytics Social media analysis dynamic clustering streaming data

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The perceptual scalability of visualization

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2006年第5期12卷 837-844页

作者： Yost, Beth North, Chris IEEE

larger, higher resolution displays can be used to increase the scalability of information visualizations. But just how much can scalability increase using larger displays before hitting human perceptual or cognitive limits? Are the same visualization techniques that are. good on a single monitor also the techniques that are best when they are scaled up using large, high-resolution displays? To answer these questions we performed a controlled experiment on user performance time, accuracy, and subjective workload when scaling up data quantity with different space-time-attribute visualizations using a large, tiled display. Twelve college students used small multiples, embedded bar matrices, and embedded time-series graphs either on a 2 megapixel (Mp) display or with data scaled up using a 32 Mp tiled display. Participants performed various overview and detail tasks on geospatially-referenced multidimensional time-series data. Results showed that current designs are perceptually scalable because they result in a decrease in task completion time when normalized per number of data attributes along with no decrease in accuracy. It appears that, for the visualizations selected for this study, the relative comparison between designs is generally consistent between display sizes. However, results also suggest that encoding is more important on a smaller display while spatial grouping is more important on a larger display. Some suggestions for designers are provided based on our experience designing visualizations for large displays.

关键词： information visualization large displays empirical evaluation

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Viz3D:: Effective exploratory visualization of large multidimensional data sets

<i>Viz3D</i>:: Effective exploratory visualization of large ...

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17th Brazilian symposium on Computer graphics and Image Processing/2nd Ibero-American symposium on Computer graphics

作者： Artero, AO de Oliveira, MCF Univ Sao Paulo Inst Ciencias Matemat & Computacao BR-13560970 Sao Carlos SP Brazil

ISBN: (纸本)0769522270

We propose a multidimensional visualization technique, named Viz3D, that creates a 3D representation of n-dimensional data that may be interactively manipulated by users to handle visual cluttering and object occlusion. The projection performed in Viz3D is comparable in quality with the 3D projections obtained with well-known dimensionality reduction techniques, at a lower complexity cost. nile a 3D projection conveys more information, giving the user more control of the visual representation and an additional dimension, as compared to 2D, visual cluttering and object occlusion are still a problem in handling large multidimensional data sets. To produce more effective visualizations, two strategies are introduced Dimensionality is handled with a similarity clustering of attributes prior to projection. data set size is handled with a new strategy of visualizing data densities, rather than individual data records. Both the direct and density Viz3D visualizations provide the basis for a user driven visual clustering approach applicable to high-dimensional data sets that is very simple, intuitive and effective.

关键词： visualization

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Hierarchical pixel bar charts

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2002年第3期8卷 255-269页

作者： Keim, DA Hao, MC Dayal, U AT&T Corp Res Florham Pk NJ USA Univ Constance D-7750 Constance Germany Hewlett Packard Res Labs Palo Alto CA USA

Simple presentation graphics are intuitive and easy-to-use, but only show highly aggregated data. Bar charts, for example, only show a rather small number of data values and x-y-plots often have a high degree of overlap. Presentation techniques are often chosen depending on the considered data type-bar charts, for example, are used for categorical data and x-y plots are used for numerical data. In this article, we propose a combination of traditional bar charts and x-y-plots, which allows the visualization of large amounts of data with categorical and numerical data. The categorical data dimensions are used for the partitioning into the bars and the numerical data dimensions are used for the ordering arrangement within the bars. The basic idea is to use the pixels within the bars to present the detailed information of the data records. Our so-called pixel bar charts retain the intuitiveness of traditional bar charts while applying the principle of x-y charts within the bars. In many applications, a natural hierarchy is defined on the categorical data dimensions such as time, region, or product type. In hierarchical pixel bar charts, the hierarchy is exploited to split the bars for selected portions of the hierarchy. Our application to a number of real-world e-business and Web services data sets shows the wide applicability and usefulness of our new idea.

关键词： information visualization multidimensional data visualization visual data exploration and data mining very large multiattributes data sets hierarchical visualization

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Reducing latency to volume visualization on PC cluster

Reducing latency to volume visualization on PC cluster

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14th Brazilian symposium on Computer graphics and Image Processing

作者： Bressan, PA Zuffo, MK Univ Sao Paulo Polytech Sch Lab Integrated Syst EPUSP Sao Paulo Brazil

ISBN: (纸本)0769513301

Volume visualization is an important tool in many scientific applications, requiring intensive processing and dealing with large amounts of data. Therefore the size of these data always exceeds the processing and visualization capacities of the conventional workstations. In this technical poster we present our working in progress about techniques of data prefetching and data prediction to volume visualization on low cost PC clusters. Our approach is based on the Distributed Shared Memory (DSM) paradigm [3], which one can greatly facilitate the parallel programming efforts on volume visualization applications.

关键词： parallel volume visualization PC cluster DSM data prefetching data prediction

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Correlating Sub-Phenomena in Performance datainthe Frequency Domain 6

Correlating Sub-Phenomena in Performance Datainthe Frequency...

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

作者： Vierjahn, Tom Hermanns, Marc-Andre Mohr, Bernd Mueller, Matthias S. Kuhlen, Torsten W. Hentschel, Bernd Rhein Westfal TH Aachen Visual Comp Inst Aachen Germany Rhein Westfal TH Aachen Chair High Performance Comp Aachen Germany Julich Supercomp Ctr Julich Germany JARA High Performance Comp Aachen Germany

ISBN: (纸本)9781509056590

Finding and understanding correlated performance behaviour of the individual functions of massively parallel high-performance computing (HPC) applications is a time-consuming task. In this poster, we propose filtered correlation analysis for automatically locating interdependencies in call-path performance profiles. Transforming the data into the frequency domain splits a performance phenomenon into sub-phenomena to be correlated separately. We provide the mathematical framework and an overview over the visualization, and we demonstrate the effectiveness of our technique.

关键词： D.2.8 [Software Engineering]: Metrics/Measurement-Performance measures I.5.4.m [Pattern Recognition]: Applications-Signal processing I.6.9 [Simulation Modeling and visualization]: visualization

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