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

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Formal evaluation strategies for feature tracking 特征跟踪的形式化评估策略
2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2016-10-01 DOI: 10.1109/LDAV.2016.7874339
Andrea Schnorr, Sebastian Freitag, D. Helmrich, T. Kuhlen, B. Hentschel
{"title":"Formal evaluation strategies for feature tracking","authors":"Andrea Schnorr, Sebastian Freitag, D. Helmrich, T. Kuhlen, B. Hentschel","doi":"10.1109/LDAV.2016.7874339","DOIUrl":"https://doi.org/10.1109/LDAV.2016.7874339","url":null,"abstract":"We present an approach for tracking space-filling features based on a two-step algorithm utilizing two graph optimization techniques. First, one-to-one assignments between successive time steps are found by a matching on a weighted, bi-partite graph. Second, events are detected by computing an independent set on potential event explanations. The main objective of this work is investigating options for formal evaluation of complex feature tracking algorithms in the absence of ground truth data.","PeriodicalId":148570,"journal":{"name":"2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"24 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":"115716478","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: 0
Quantitative exploration of large medieval manuscripts data for the codicological research 用于法典研究的大量中世纪手稿数据的定量探索
2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2016-10-01 DOI: 10.1109/LDAV.2016.7874306
Swati Chandna, F. Rindone, C. Dachsbacher, R. Stotzka
{"title":"Quantitative exploration of large medieval manuscripts data for the codicological research","authors":"Swati Chandna, F. Rindone, C. Dachsbacher, R. Stotzka","doi":"10.1109/LDAV.2016.7874306","DOIUrl":"https://doi.org/10.1109/LDAV.2016.7874306","url":null,"abstract":"Quantitative exploration is gaining in importance for the analysis of the digitized medieval manuscripts. While codicologists can collect such massive amounts of heterogeneous datasets digitized in high-resolution, they still lack efficient and intuitive means to explore data and answer domain-specific research questions. A new approach is needed to enable codicologists with the quantitative exploration of large of data. This paper presents a concept of a fully integrated system to enable a quantitative exploration of various layout features and their uncertainties over a large collection of medieval manuscripts. It is composed of three main components: data handling that ingests large amounts of data into a data repository, feature extraction that extracts various layout features of the manuscripts and quantitative exploration for visual analysis. In addition to this, we introduce new visualization approaches, i.e. the superimposition plot and the manuscript montage plot in combination with the parallel coordinate plot to explore structural layout features of 170,000 manuscript pages with more than 2.5 million measurements of these layout features. Our approach supports codicologists to see the overall structure of the manuscript at a single glimpse, explore heterogeneous layout features and convey uncertainties. We demonstrate typical use-case scenarios of our collaborators in codicological research where our system has enabled them to answer domain-specific questions for analysis of the medieval manuscripts data for the first time.","PeriodicalId":148570,"journal":{"name":"2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"41 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":"133471462","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: 7
Topology-aware image compositing using NVLink 使用NVLink的拓扑感知图像合成
2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2016-10-01 DOI: 10.1109/LDAV.2016.7874334
Cameron Christensen, T. Fogal, Nathan Luehr, Cliff Woolley
{"title":"Topology-aware image compositing using NVLink","authors":"Cameron Christensen, T. Fogal, Nathan Luehr, Cliff Woolley","doi":"10.1109/LDAV.2016.7874334","DOIUrl":"https://doi.org/10.1109/LDAV.2016.7874334","url":null,"abstract":"Compositing is a significant factor in distributed visualization performance at scale on high-performance computing (HPC) systems. For applications such as Para VieworVisIt, the common approach is “sort-last” rendering. For this approach, data are split up to be rendered such that each MPI rank has one or more portions of the over-all domain. After rendering its individual piece(s), each rank has one or more partial images that must be composited with the others to form the final image. The common approach for this step is to use a tree-like communication pattern to reduce the rendered images down to a single image to be displayed to the user. A variety of algorithms have been explored to perform this step efficiently in order to achieve interactive rendering on massive systems [7, 3, 8, 4].","PeriodicalId":148570,"journal":{"name":"2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"34 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":"125174294","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: 4
Scalable rendering of large SPH simulations using an RK-enhanced interpolation scheme on constrained datasets 在约束数据集上使用rk增强插值方案的大型SPH模拟的可扩展渲染
2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2016-10-01 DOI: 10.1109/LDAV.2016.7874335
K. Griffin, C. Raskin
{"title":"Scalable rendering of large SPH simulations using an RK-enhanced interpolation scheme on constrained datasets","authors":"K. Griffin, C. Raskin","doi":"10.1109/LDAV.2016.7874335","DOIUrl":"https://doi.org/10.1109/LDAV.2016.7874335","url":null,"abstract":"Smoothed particle hydrodynamics (SPH) is a Lagrangian alternative to mesh-based schemes for simulating fluid flows in a wide variety of physical applications. However, there are a number of challenges that arise when attempting to visualize the results of these simulations. This poster presents a Reproducing Kernel (RK) enhanced SPH Resample Operator we have developed, in VisIt [1], to run on high performance computing (HPC) platforms, scale across compute nodes, and work efficiently on constrained datasets. We define constrained datasets as data that is exported in a way as to not allow efficient processing within a visualization tool. For us, these constrained datasets are pre-partitioned spatially, which in most cases, is not ideal for good load balancing. Furthermore, they also lack metadata, like the identification of halo or ghost zone regions, needed for node independent processing.","PeriodicalId":148570,"journal":{"name":"2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"21 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":"124913094","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: 1
A lightweight H.264-based hardware accelerated image compression library 一个轻量级的基于h .264硬件加速图像压缩库
2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2016-10-01 DOI: 10.1109/LDAV.2016.7874337
Jie Jiang, T. Fogal, Cliff Woolley, P. Messmer
{"title":"A lightweight H.264-based hardware accelerated image compression library","authors":"Jie Jiang, T. Fogal, Cliff Woolley, P. Messmer","doi":"10.1109/LDAV.2016.7874337","DOIUrl":"https://doi.org/10.1109/LDAV.2016.7874337","url":null,"abstract":"Hardware video encoding can lower the perceived latency for remote visualization. We have created a lightweight library that simplifies the use of NVIDIA's video compression hardware. We achieve overall latencies below 15ms with compression ratios of approximately 85:1. To verify its applicability in real world scenarios, we integrated our library into ParaView. This offloads the encoding within ParaView to the GPU and provides a 25x bandwidth reduction compared to existing image compression methods available in the tool.","PeriodicalId":148570,"journal":{"name":"2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"25 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":"127597607","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: 3
Embedded domain-specific language and runtime system for progressive spatiotemporal data analysis and visualization 嵌入式领域特定语言和运行时系统，用于渐进式时空数据分析和可视化
2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2016-08-16 DOI: 10.1109/LDAV.2016.7874304
Cameron Christensen, Ji-Woo Lee, Shusen Liu, P. Bremer, G. Scorzelli, Valerio Pascucci
{"title":"Embedded domain-specific language and runtime system for progressive spatiotemporal data analysis and visualization","authors":"Cameron Christensen, Ji-Woo Lee, Shusen Liu, P. Bremer, G. Scorzelli, Valerio Pascucci","doi":"10.1109/LDAV.2016.7874304","DOIUrl":"https://doi.org/10.1109/LDAV.2016.7874304","url":null,"abstract":"As our ability to generate large and complex datasets grows, accessing and processing these massive data collections is increasingly the primary bottleneck in scientific analysis. Challenges include retrieving, converting, resampling, and combining remote and often disparately located data ensembles with only limited support from existing tools. In particular, existing solutions rely predominantly on extensive data transfers or large-scale remote computing resources, both of which are inherently offline processes with long delays and substantial repercussions for any mistakes. Such workflows severely limit the flexible exploration and rapid evaluation of new hypotheses that are crucial to the scientific process and thereby impede scientific discovery. Here we present an embedded domain-specific language (EDSL) specifically designed for the interactive exploration of large-scale, remote data. Our EDSL allows users to express a wide range of data analysis operations in a simple and abstract manner. The underlying runtime system transparently resolves issues such as remote data access and resampling while at the same time maintaining interactivity through progressive and interruptible computation. This system enables, for the first time, interactive remote exploration of massive datasets such as the 7km NASA GEOS-5 Nature Run simulation, which previously have been analyzed only offline or at reduced resolution.","PeriodicalId":148570,"journal":{"name":"2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116997809","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: 5
Optimizing multi-image sort-last parallel rendering 优化多图像排序后并行渲染
2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2016-08-01 DOI: 10.1109/LDAV.2016.7874308
Matthew Larsen, K. Moreland, Chris R. Johnson, H. Childs
{"title":"Optimizing multi-image sort-last parallel rendering","authors":"Matthew Larsen, K. Moreland, Chris R. Johnson, H. Childs","doi":"10.1109/LDAV.2016.7874308","DOIUrl":"https://doi.org/10.1109/LDAV.2016.7874308","url":null,"abstract":"Sort-last parallel rendering can be improved by considering the rendering of multiple images at a time. Most parallel rendering algorithms consider the generation of only a single image. This makes sense when performing interactive rendering where the parameters of each rendering are not known until the previous rendering completes. However, in situ visualization often generates multiple images that do not need to be created sequentially. In this paper we present a simple and effective approach to improving parallel image generation throughput by amortizing the load and overhead among multiple image renders. Additionally, we validate our approach by conducting a performance study exploring the achievable speed-ups in a variety of image-based in situ use cases and rendering workloads. On average, our approach shows a 1.5 to 3.7 fold improvement in performance, and in some cases, shows a 10 fold improvement.","PeriodicalId":148570,"journal":{"name":"2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128500825","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: 10
Block-parallel data analysis with DIY2 基于DIY2的块并行数据分析
2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2016-05-11 DOI: 10.1109/LDAV.2016.7874307
D. Morozov, T. Peterka
{"title":"Block-parallel data analysis with DIY2","authors":"D. Morozov, T. Peterka","doi":"10.1109/LDAV.2016.7874307","DOIUrl":"https://doi.org/10.1109/LDAV.2016.7874307","url":null,"abstract":"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.","PeriodicalId":148570,"journal":{"name":"2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132363485","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: 35
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