2022 IEEE 12th Symposium on Large Data Analysis and Visualization (LDAV)最新文献

Title1 Title1

2022 IEEE 12th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2022-10-16 DOI: 10.17817/2016.01.21.108

H. Dong

引用次数: 0

Hybrid Image-/Data-Parallel Rendering Using Island Parallelism 混合图像/数据并行渲染使用孤岛并行

2022 IEEE 12th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2022-10-16 DOI: 10.1109/LDAV57265.2022.9966396

Stefan Zellmann, J. Barbosa, Serkan Dermici, Alper Sahistan

引用次数: 2

New Triggers for Automatic Camera Placement Over Time 随着时间的推移，自动相机放置的新触发器

2022 IEEE 12th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2022-10-16 DOI: 10.1109/LDAV57265.2022.9966427

Meghanto Majumder, Nicole Marsaglia, H. Childs

引用次数: 0

LDAV 2022 Committees

2022 IEEE 12th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2022-10-16 DOI: 10.1109/ldav57265.2022.9966398

引用次数: 0

High-Quality Progressive Alignment of Large 3D Microscopy Data 大3D显微镜数据的高质量渐进校准

2022 IEEE 12th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2022-10-16 DOI: 10.1109/LDAV57265.2022.9966406

A. Venkat, D. Hoang, A. Gyulassy, P. Bremer, F. Federer, A. Angelucci, Valerio Pascucci

{"title":"High-Quality Progressive Alignment of Large 3D Microscopy Data","authors":"A. Venkat, D. Hoang, A. Gyulassy, P. Bremer, F. Federer, A. Angelucci, Valerio Pascucci","doi":"10.1109/LDAV57265.2022.9966406","DOIUrl":"https://doi.org/10.1109/LDAV57265.2022.9966406","url":null,"abstract":"Large-scale three-dimensional (3D) microscopy acquisitions fre-quently create terabytes of image data at high resolution and magni-fication. Imaging large specimens at high magnifications requires acquiring 3D overlapping image stacks as tiles arranged on a two-dimensional (2D) grid that must subsequently be aligned and fused into a single 3D volume. Due to their sheer size, aligning many overlapping gigabyte-sized 3D tiles in parallel and at full resolution is memory intensive and often I/O bound. Current techniques trade accuracy for scalability, perform alignment on subsampled images, and require additional postprocess algorithms to refine the alignment quality, usually with high computational requirements. One common solution to the memory problem is to subdivide the overlap region into smaller chunks (sub-blocks) and align the sub-block pairs in parallel, choosing the pair with the most reliable alignment to determine the global transformation. Yet aligning all sub-block pairs at full resolution remains computationally expensive. The key to quickly developing a fast, high-quality, low-memory solution is to identify a single or a small set of sub-blocks that give good alignment at full resolution without touching all the overlapping data. In this paper, we present a new iterative approach that leverages coarse resolution alignments to progressively refine and align only the promising candidates at finer resolutions, thereby aligning only a small user-defined number of sub-blocks at full resolution to determine the lowest error transformation between pairwise overlapping tiles. Our progressive approach is 2.6x faster than the state of the art, requires less than 450MB of peak RAM (per parallel thread), and offers a higher quality alignment without the need for additional postprocessing refinement steps to correct for alignment errors.","PeriodicalId":300023,"journal":{"name":"2022 IEEE 12th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133881504","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

A Prototype for Pipeline-Composable Task-Based Visualization Algorithms 一种基于管道组合任务的可视化算法原型

2022 IEEE 12th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2022-10-16 DOI: 10.1109/LDAV57265.2022.9966395

Marvin Petersen, Kilian Werner, Andrea Schnorr, T. Kuhlen, C. Garth

{"title":"A Prototype for Pipeline-Composable Task-Based Visualization Algorithms","authors":"Marvin Petersen, Kilian Werner, Andrea Schnorr, T. Kuhlen, C. Garth","doi":"10.1109/LDAV57265.2022.9966395","DOIUrl":"https://doi.org/10.1109/LDAV57265.2022.9966395","url":null,"abstract":"For next generation platforms, the paradigm of task-based parallelism has the potential to overcome some of the accompanying challenges. This paradigm has already been applied by the visualization community to specific algorithms and problems. However, one advantage of the task-based paradigm-the interleaving of work-should lead to better utilization of resources and ultimately lower execution times if the paradigm is applied to whole pipelines. In order to investigate this potential, we build a prototype framework for composable task-based parallel visualization algorithms. With this we explore the combination of a strictly task-based approach with the addition of a pipeline layer for visualization algorithms. This additional layer eases the composition of larger task-based parallel visualization applications without the need to explicitly define the exact connection in a task graph between algorithms. In this manner, task-based visualization algorithms can be designed towards a common interface, be easily combined, and still benefit from the advantages of the task-based paradigm across algorithm boundaries, such as latency hiding. We explore the design implications of this combination and show initial results of the scalability and the impact of task interleaving on the runtime of exemplary pipelines.","PeriodicalId":300023,"journal":{"name":"2022 IEEE 12th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128214975","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

Distributed Hierarchical Contour Trees 分布式分层等高线树

2022 IEEE 12th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2022-10-16 DOI: 10.1109/LDAV57265.2022.9966394

H. Carr, O. Rübel, G. Weber

引用次数: 2

Angular-based Edge Bundled Parallel Coordinates Plot for the Visual Analysis of Large Ensemble Simulation Data 基于角度的边缘捆绑平行坐标图用于大集合仿真数据的可视化分析

2022 IEEE 12th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2022-09-22 DOI: 10.1109/LDAV57265.2022.9966393

Keita Watanabe, Naohisa Sakamoto, J. Nonaka, Y. Maejima

{"title":"Angular-based Edge Bundled Parallel Coordinates Plot for the Visual Analysis of Large Ensemble Simulation Data","authors":"Keita Watanabe, Naohisa Sakamoto, J. Nonaka, Y. Maejima","doi":"10.1109/LDAV57265.2022.9966393","DOIUrl":"https://doi.org/10.1109/LDAV57265.2022.9966393","url":null,"abstract":"With the continuous increase in the computational power and resources of modern high-performance computing (HPC) systems, large-scale ensemble simulations have become widely used in various fields of science and engineering, and especially in meteoro-logical and climate science. It is widely known that the simulation outputs are large time-varying, multivariate, and multivalued datasets which pose a particular challenge to the visualization and analysis tasks. In this work, we focused on the widely used Parallel Coordinates Plot (PCP) to analyze the interrelations between different parameters, such as variables, among the members. However, PCP may suffer from visual cluttering and drawing performance with the increase on the data size to be analyzed, that is, the number of polylines. To overcome this problem, we present an extension to the PCP by adding Bézier curves connecting the angular distribution plots representing the mean and variance of the inclination of the line segments between parallel axes. The proposed Angular-based Parallel Coordinates Plot (APCP) is capable of presenting a simplified overview of the entire ensemble data set while maintaining the correlation information between the adjacent variables. To verify its effectiveness, we developed a visual analytics prototype system and evaluated by using a meteorological ensemble simulation output from the supercomputer Fugaku.","PeriodicalId":300023,"journal":{"name":"2022 IEEE 12th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130420970","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

Topological Analysis of Ensembles of Hydrodynamic Turbulent Flows An Experimental Study 流体动力湍流系综的拓扑分析——实验研究

2022 IEEE 12th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2022-07-28 DOI: 10.1109/LDAV57265.2022.9966403

F. Nauleau, F. Vivodtzev, T. Bridel-Bertomeu, H. Beaugendre, Julien Tierny

{"title":"Topological Analysis of Ensembles of Hydrodynamic Turbulent Flows An Experimental Study","authors":"F. Nauleau, F. Vivodtzev, T. Bridel-Bertomeu, H. Beaugendre, Julien Tierny","doi":"10.1109/LDAV57265.2022.9966403","DOIUrl":"https://doi.org/10.1109/LDAV57265.2022.9966403","url":null,"abstract":"This application paper presents a comprehensive experimental eval-uation of the suitability of Topological Data Analysis (TDA) for the quantitative comparison of turbulent flows. Specifically, our study documents the usage of the persistence diagram of the max-ima of flow enstrophy (an established vorticity indicator), for the topological representation of 180 ensemble members, generated by a coarse sampling of the parameter space of five numerical solvers. We document five main hypotheses reported by domain experts, describing their expectations regarding the variability of the flows generated by the distinct solver configurations. We contribute three evaluation protocols to assess the validation of the above hypothe-ses by two comparison measures: (i) a standard distance used in scientific imaging (the $L_{2}$ norm) and (ii) an established topological distance between persistence diagrams (the $L_{2}$ -Wasserstein metric). Extensive experiments on the input ensemble demonstrate the supe-riority of the topological distance (ii) to report as close to each other flows which are expected to be similar by domain experts, due to the configuration of their vortices. Overall, the insights reported by our study bring an experimental evidence of the suitability of TDA for representing and comparing turbulent flows, thereby providing to the fluid dynamics community confidence for its usage in future work. Also, our flow data and evaluation protocols provide to the TDA community an application-approved benchmark for the evaluation and design of further topological distances.","PeriodicalId":300023,"journal":{"name":"2022 IEEE 12th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128921123","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