2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)最新文献_第2页

Parallel Partial Reduction for Large-Scale Data Analysis and Visualization 面向大规模数据分析和可视化的并行部分约简

2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2018-10-01 DOI: 10.1109/LDAV.2018.8739165

Wenbin He, Hanqi Guo, T. Peterka, S. Di, F. Cappello, Han-Wei Shen

{"title":"Parallel Partial Reduction for Large-Scale Data Analysis and Visualization","authors":"Wenbin He, Hanqi Guo, T. Peterka, S. Di, F. Cappello, Han-Wei Shen","doi":"10.1109/LDAV.2018.8739165","DOIUrl":"https://doi.org/10.1109/LDAV.2018.8739165","url":null,"abstract":"We present a novel partial reduction algorithm to aggregate sparsely distributed intermediate results that are generated by data-parallel analysis and visualization algorithms. Applications of partial reduction include flow trajectory analysis, big data online analytical processing, and volume rendering. Unlike traditional full parallel reduction that exchanges dense data across all processes, the purpose of partial reduction is to exchange only intermediate results that correspond to the same query, such as line segments of the same flow trajectory. To this end, we design a three-stage algorithm that minimizes the communication cost: (1) partitioning the result space into groups; (2) constructing and optimizing the reduction partners for each group; and (3) initiating collective reduction operations for all groups concurrently. Both theoretical and empirical analyses show that our algorithm outperforms the traditional methods when the intermediate results are sparsely distributed. We also demonstrate the effectiveness of our algorithm for flow visualization, big log data analysis, and volume rendering.","PeriodicalId":241591,"journal":{"name":"2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128864183","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 Large Data Visualization Framework for SPARC64 fx HPC Systems - Case Study: K Computer Operational Environment - sparc64fx HPC系统的大数据可视化框架-案例研究:K计算机操作环境-

2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2018-10-01 DOI: 10.1109/LDAV.2018.8739214

J. Nonaka, K. Ono, Naohisa Sakamoto, Kengo Hayashi, Motohiko Matsuda, F. Shoji, Kentaro Oku, M. Fujita, Kazuma Hatta

{"title":"A Large Data Visualization Framework for SPARC64 fx HPC Systems - Case Study: K Computer Operational Environment -","authors":"J. Nonaka, K. Ono, Naohisa Sakamoto, Kengo Hayashi, Motohiko Matsuda, F. Shoji, Kentaro Oku, M. Fujita, Kazuma Hatta","doi":"10.1109/LDAV.2018.8739214","DOIUrl":"https://doi.org/10.1109/LDAV.2018.8739214","url":null,"abstract":"Leading-edge supercomputer systems have been designed to achieve the highest computational performance possible for running a wide variety of large-scale simulations, and the pre- and post-processing are usually not considered in the main design feature. Although supercomputer systems may have peculiar CPU architecture, the auxiliary computational systems tend to use commodity based hardware and software in the form of servers and clusters. In the case of the K computer operational environment, at RIKEN R-CCS, the supercomputer itself is based on SPARC64 fx CPU architecture, and pre- and post-processing servers are based on traditional x86 CPU architecture. In this poster we present a large data visualization environment developed for this peculiar HPC operational environment, presenting some of the efforts made to meet the large data visualization needs. It is publicly known that the next-generation leading-edge Japanese supercomputer will abandon this CPU architecture in favor of another architecture, but we expect that some of the knowledge obtained in this development will also be useful for this future coming supercomputer system.","PeriodicalId":241591,"journal":{"name":"2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130374467","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

Method for Improving RadViz’s Navigation Function Based on Focusing and Filtering 基于聚焦滤波的RadViz导航功能改进方法

2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2018-10-01 DOI: 10.1109/LDAV.2018.8739161

Hyunwoo Han, Taerin Yoon, Hyoji Ha, Juwon Hong, Kyungwon Lee

引用次数: 0

Towards In-Situ Vortex Identification for Peta-Scale CFD Using Contour Trees 基于轮廓树的peta尺度CFD原位涡识别研究

2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2018-10-01 DOI: 10.1109/LDAV.2018.8739233

Marius K. Koch, P. Kelly, P. Vincent

引用次数: 0

Complexity Estimation for Feature Tracking Data 特征跟踪数据的复杂性估计

2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2018-10-01 DOI: 10.1109/LDAV.2018.8739231

D. Helmrich, Andrea Schnorr, T. Kuhlen, B. Hentschel

引用次数: 0

LDAV 2018 TOC

2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2018-10-01 DOI: 10.1109/ldav.2018.8739230

引用次数: 0

A Flow Visualization Using Parallel 3D Line Integral Convolution for Large Scale Unstructured Grid Data 基于并行三维线积分卷积的大规模非结构化网格数据流可视化

2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2018-10-01 DOI: 10.1109/LDAV.2018.8739209

Y. Liao, H. Matsui, O. Kreylos, L. Kellogg

{"title":"A Flow Visualization Using Parallel 3D Line Integral Convolution for Large Scale Unstructured Grid Data","authors":"Y. Liao, H. Matsui, O. Kreylos, L. Kellogg","doi":"10.1109/LDAV.2018.8739209","DOIUrl":"https://doi.org/10.1109/LDAV.2018.8739209","url":null,"abstract":"One of the greatest challenges facing scientists doing large computation of vector fields in a distributed parallel setting is the need for optimal parallel algorithms for flow visualization. To address this need, we present a new flow visualization method based on parallel 3D line integral convolution (LIC). Our approach uses the fact that 3D LIC only needs limited local information to design an embarrassingly parallel model with a trade-off between the additional memory cost of external cells and the time cost for communication. All data required for each process can be stored in either a local data block or the external cells which are sets of exterior data surrounding the local partition. One problem for parallel LIC is that equal domain size decomposition of the data cannot guarantee balanced parallel processes. To achieve a load-balanced visualization process, we repartition data using an estimate of the LIC computation time. In addition, to minimize the memory cost, we introduce a vector-driven external cell expansion method to reduce the required memory cost. We find that we can use fewer external cells with minimal loss of visual quality.We evaluate the performance of our visualization method by first comparing its parallel scalability with traditional integral field line visualization. Next, we compare our new partition method with other data partition methods to verify that the workload of our model is more balanced. Finally, we compare our external cell expansion method with a traditional layer-based external cell expansion method. Consequently, together with the new partition and external cell expansion methods, our parallel 3D LIC visualization proves to be an efficient and well-balanced parallel flow visualization with limited extra memory cost and a large saving of communication time.","PeriodicalId":241591,"journal":{"name":"2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116036563","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: 2

Adaptive Encoder Settings for Interactive Remote Visualisation on High-Resolution Displays 高分辨率显示器上交互式远程可视化的自适应编码器设置

2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2018-10-01 DOI: 10.1109/LDAV.2018.8739215

Florian Friess, Mathias Landwehr, Valentin Bruder, S. Frey, T. Ertl

引用次数: 1

Foundations of Multivariate Functional Approximation for Scientific Data 科学数据的多元泛函逼近基础

2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2018-10-01 DOI: 10.1109/LDAV.2018.8739195

T. Peterka, Y. Nashed, I. Grindeanu, V. Mahadevan, Raine Yeh, X. Tricoche

{"title":"Foundations of Multivariate Functional Approximation for Scientific Data","authors":"T. Peterka, Y. Nashed, I. Grindeanu, V. Mahadevan, Raine Yeh, X. Tricoche","doi":"10.1109/LDAV.2018.8739195","DOIUrl":"https://doi.org/10.1109/LDAV.2018.8739195","url":null,"abstract":"The size and complexity of scientific data are rapidly outpacing our ability to manage, analyze, visualize, and draw conclusions from the results. One way to streamline scientific data processing is to transform the data model to one that is not only more efficient but that facilitates analytical reasoning. To this end, we are investigating modeling discrete scientific data by a functional basis representation based on a tensor product of nonuniform rational B-spline functions (NURBS). The functional model is an approximation that is more efficient to communicate, store, and analyze than the original form, while having geometric and analytic properties making it useful for further analysis without translating back to the discrete form. This paper presents four main contributions. The first is modeling in high dimensions, not just curves or surfaces but volumes and hyper-volumes. The second is an adaptive algorithm that adds knots and control points until the maximum error is below a threshold set by the user. The adaptive algorithm also works in high dimensions. The third is precise evaluation of values and high-order derivatives anywhere in the domain, again in high-dimensional space. The fourth contribution is parallelization on high-performance supercomputers. We evaluate our model using both synthetic and actual scientific datasets.","PeriodicalId":241591,"journal":{"name":"2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128094421","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: 14

Exploring Visualization Techniques with HACC Simulation Data 探索可视化技术与HACC仿真数据

2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2018-10-01 DOI: 10.1109/LDAV.2018.8739228

Joseph Adamo, J. D. Emberson, Edouard B. Brooks, S. Rizzi, J. Insley, M. Papka

引用次数: 1