2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)最新文献

Statistical projections for multi-dimensional visual data exploration 多维可视化数据探索的统计投影

2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2016-10-23 DOI: 10.1109/LDAV.2016.7874338

H. Nguyen, D. Stone, E. W. Bethel

引用次数: 0

Contour forests: Fast multi-threaded augmented contour trees 轮廓森林:快速多线程增强轮廓树

2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2016-10-23 DOI: 10.1109/LDAV.2016.7874333

Charles Gueunet, P. Fortin, J. Jomier

引用次数: 41

A study of scientific visualization on heterogeneous processors using Legion 基于Legion的异构处理器科学可视化研究

2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2016-10-01 DOI: 10.1109/LDAV.2016.7874341

Lina Yu, Hongfeng Yu

引用次数: 1

Correlating sub-phenomena in performance data in the frequency domain 频域性能数据中子现象的关联

2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2016-10-01 DOI: 10.1109/LDAV.2016.7874340

Tom Vierjahn, Marc-André Hermanns, B. Mohr, Matthias S. Müller, T. Kuhlen, B. Hentschel

引用次数: 1

Interactive visual exploration of a trillion particles 一万亿粒子的交互式视觉探索

2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2016-10-01 DOI: 10.1109/LDAV.2016.7874310

Karsten Schatz, C. Müller, M. Krone, J. Schneider, G. Reina, T. Ertl

{"title":"Interactive visual exploration of a trillion particles","authors":"Karsten Schatz, C. Müller, M. Krone, J. Schneider, G. Reina, T. Ertl","doi":"10.1109/LDAV.2016.7874310","DOIUrl":"https://doi.org/10.1109/LDAV.2016.7874310","url":null,"abstract":"We present a method for the interactive exploration of tera-scale particle data sets. Such data sets arise from molecular dynamics, particle-based fluid simulation, and astrophysics. Our visualization technique provides a focus+context view of the data that runs interactively on commodity hardware. The method is based on a hybrid multi-scale rendering architecture, which renders the context as a hierarchical density volume. Fine details in the focus are visualized using direct particle rendering. In addition, clusters like dark matter halos can be visualized as semi-transparent spheres enclosing the particles. Since the detail data is too large to be stored in main memory, our approach uses an out-of-core technique that streams data on demand. Our technique is designed to take advantage of a dual-GPU configuration, in which the workload is split between the GPUs based on the type of data. Structural features in the data are visually enhanced using advanced rendering and shading techniques. To allow users to easily identify interesting locations even in overviews, both the focus and context view use color tables to show data attributes on the respective scale. We demonstrate that our technique achieves interactive performance on a one trillionpar-ticle data set from the DarkSky simulation.","PeriodicalId":148570,"journal":{"name":"2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134439336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18

Parallel peak pruning for scalable SMP contour tree computation 并行峰值修剪可扩展SMP轮廓树计算

2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2016-10-01 DOI: 10.1109/LDAV.2016.7874312

H. Carr, G. Weber, Christopher M. Sewell, J. Ahrens

{"title":"Parallel peak pruning for scalable SMP contour tree computation","authors":"H. Carr, G. Weber, Christopher M. Sewell, J. Ahrens","doi":"10.1109/LDAV.2016.7874312","DOIUrl":"https://doi.org/10.1109/LDAV.2016.7874312","url":null,"abstract":"As data sets grow to exascale, automated data analysis and visualisation are increasingly important, to intermediate human understanding and to reduce demands on disk storage via in situ analysis. Trends in architecture of high performance computing systems necessitate analysis algorithms to make effective use of combinations of massively multicore and distributed systems. One of the principal analytic tools is the contour tree, which analyses relationships between contours to identify features of more than local importance. Unfortunately, the predominant algorithms for computing the contour tree are explicitly serial, and founded on serial metaphors, which has limited the scalability of this form of analysis. While there is some work on distributed contour tree computation, and separately on hybrid GPU-CPU computation, there is no efficient algorithm with strong formal guarantees on performance allied with fast practical performance. We report the first shared SMP algorithm for fully parallel contour tree computation, withfor-mal guarantees of O(lgnlgt) parallel steps and O(n lgn) work, and implementations with up to 10x parallel speed up in OpenMP and up to 50x speed up in NVIDIA Thrust.","PeriodicalId":148570,"journal":{"name":"2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"5 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114059161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 45

Parallel distributed, GPU-accelerated, advanced lighting calculations for large-scale volume visualization 并行分布式，gpu加速，先进的照明计算大规模体积可视化

2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2016-10-01 DOI: 10.1109/LDAV.2016.7874309

Min Shih, S. Rizzi, J. Insley, T. Uram, V. Vishwanath, M. Hereld, M. Papka, K. Ma

引用次数: 12

In situ generated probability distribution functions for interactive post hoc visualization and analysis 现场生成的概率分布函数用于交互式事后可视化和分析

2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2016-10-01 DOI: 10.1109/LDAV.2016.7874311

Yucong Ye, Tyson Neuroth, F. Sauer, K. Ma, G. Borghesi, Aditya Konduri, H. Kolla, Jacqueline H. Chen

{"title":"In situ generated probability distribution functions for interactive post hoc visualization and analysis","authors":"Yucong Ye, Tyson Neuroth, F. Sauer, K. Ma, G. Borghesi, Aditya Konduri, H. Kolla, Jacqueline H. Chen","doi":"10.1109/LDAV.2016.7874311","DOIUrl":"https://doi.org/10.1109/LDAV.2016.7874311","url":null,"abstract":"The growing power and capacity of supercomputers enable scientific simulations at extreme scale, leading to not only more accurate modeling and greater predictive ability but also massive quantities of data to analyze. New approaches to data analysis and visualization are this needed to support interactive exploration through selective data access for gaining insights into terabytes and petabytes of data. In this paper, we present an in situ data processing method for both generating probability distribution functions (PDFs) from field data and reorganizing particle data using a single spatial organization scheme. This coupling between PDFs and particles allows for the interactive post hoc exploration of both data types simultaneously. Scientists can explore trends in large-scale data through the PDFs and subsequently extract desired particle subsets for further analysis. We evaluate the usability of our in situ method using a petascale combustion simulation and demonstrate the increases in task efficiency and accuracy that the resulting workflow provides to scientists.","PeriodicalId":148570,"journal":{"name":"2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116075024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18

Segmented time series visualization tool for additive manufacturing 用于增材制造的分段时间序列可视化工具

2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2016-10-01 DOI: 10.1109/LDAV.2016.7874336

William Halsey, C. Steed, R. Dehoff, V. Paquit, Sean L. Yoder

引用次数: 0

SeekAView: An intelligent dimensionality reduction strategy for navigating high-dimensional data spaces SeekAView:用于导航高维数据空间的智能降维策略

2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2016-10-01 DOI: 10.1109/LDAV.2016.7874305

Josua Krause, Aritra Dasgupta, Jean-Daniel Fekete, E. Bertini

{"title":"SeekAView: An intelligent dimensionality reduction strategy for navigating high-dimensional data spaces","authors":"Josua Krause, Aritra Dasgupta, Jean-Daniel Fekete, E. Bertini","doi":"10.1109/LDAV.2016.7874305","DOIUrl":"https://doi.org/10.1109/LDAV.2016.7874305","url":null,"abstract":"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.","PeriodicalId":148570,"journal":{"name":"2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131219847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 28