III: CGV: Small: A Scalable Visual Analytics Framework for Exascale Scientific Simulations

III：CGV：小型：用于百亿亿次科学模拟的可扩展视觉分析框架

• 批准号: 1423487
• 负责人: Hongfeng Yu
• 金额: $ 39.74万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1423487&HistoricalAwards=false
• 关键词: III; CGV; Small; Scalable; Visual

By leveraging advanced parallel computing systems, scientists can answer important questions that are critical to US energy and economic security. Exascale computing will further enable scientists to perform detailed simulations at higher resolution and greater complexity. Advanced visualization is necessary for scientists to explore massive and complex simulation data at high interactivity and fidelity to study various physical, chemical, and biological phenomena. Although visualization technology has significantly progressed in recent years, conventional visualization techniques are not yet ready for exascale systems and applications. Future exascale systems are expected to be characterized with many-core processors, deep memory hierarchies, and high levels of concurrency. The design of new visualization techniques must adapt to the need for timely discovery from complex and extremely large data sets as well as these emerging hardware and software trends. The goal of this project is to address the current technology gap by investigating a complete course of visualization pipeline with scientific simulations in a holistic fashion, and thus ensure parallelism and efficiency in exascale data visual analytics. This project will integrate research with teaching and outreach programs, where visualization of scientific applications will be used as an effective means to promote students' interest and proficiency in science and engineering studies, and to attract and retain both undergraduate and graduate students, particularly female students, into research.This project plans to account directly for the complex interdependencies with and among the critical components of visual analytics for exascale computing. This project focuses on three key research tasks: (1) developing a novel in-situ data reduction and indexing algorithm to capture essentials from large-scale simulations; (2) studying parallel visualization algorithms to promise scalable performance for high-throughput and high-resolution exploration of large-scale simulation data based on in-situ compact data representations; and (3) designing user interface to parse and deploy application knowledge for visual analytics to acquire critical scientific discovery from in-situ simulation output with enhanced user experience and performance. This project is driven by real-world large-scale scientific applications that involve the modeling and analysis of evolving phenomena with heterogeneous data types, and demand scalable capabilities of visual analytics. Scientific collaborators will be involved into the development, evaluation, and deployment of the solutions to close the gap between advanced visualization techniques and scientific applications, and help solve some of the most challenging scientific problems. The techniques developed within this project will be readily adapted for use by many applications beyond the primary demonstration targets with similar needs, and thus will have a significant impact on scientists' capability for data analysis and visualization. The success of this research will potentially change the conventional scientific discovery pipeline and accelerate the study of large-scale simulation data. The project results will be disseminated through different venues and forms that are publicized at the project website (http://cse.unl.edu/~yu/research/nsf15_exascale/).

通过利用先进的平行计算系统，科学家可以回答对美国能源和经济安全至关重要的重要问题。 Exascale计算将进一步使科学家以更高的分辨率和更高的复杂性进行详细的模拟。高级可视化对于科学家来说是必要的，以探索高相互作用和忠诚度以研究各种物理，化学和生物学现象的大规模和复杂的模拟数据。尽管近年来可视化技术已经取得了显着发展，但传统的可视化技术尚未为Exascale系统和应用做好准备。预计未来的Exascale系统将以多核处理器，深度内存层次结构和高水平的并发性来表征。新的可视化技术的设计必须适应从复杂且极大的数据集以及这些新兴的硬件和软件趋势中及时发现的需求。该项目的目的是通过以整体方式调查科学模拟的完整可视化管道来解决当前的技术差距，从而确保Exascale数据视觉分析的并行性和效率。该项目将将研究与教学和外展计划融合在一起，在该计划中，科学应用的可视化将被用作有效的手段，以促进学生对科学和工程学研究的兴趣和熟练程度，并吸引和保留研究生，尤其是女学生，尤其是女学生。该项目计划直接涉及与视觉分析的重要组成部分，以直接依赖于相互依存的相互依赖性。该项目侧重于三个关键的研究任务：（1）开发一种新型的原位数据减少和索引算法，以从大规模模拟中捕获必需品； （2）研究平行可视化算法，以根据基于原位紧凑的数据表示对大型模拟数据进行高通量和高分辨率探索的可扩展性能； （3）设计用户界面以解析和部署应用程序知识，以供视觉分析，以从现场模拟输出中获取重要的科学发现，并具有增强的用户体验和性能。该项目是由现实世界中的大规模科学应用驱动的，该应用涉及使用异构数据类型对不断发展的现象进行建模和分析，并需要视觉分析的可扩展功能。科学合作者将参与解决方案的开发，评估和部署，以缩小先进的可视化技术和科学应用之间的差距，并帮助解决一些最具挑战性的科学问题。除了具有相似需求的主要演示目标之外，许多应用程序将很容易地使用该项目中开发的技术，因此将对科学家的数据分析和可视化能力产生重大影响。这项研究的成功将有可能改变传统的科学发现管道，并加速大规模仿真数据的研究。项目结果将通过在项目网站（http://cse.unl.edu/~yu/research/nsf15_exascale/）上宣传的不同场所和表格进行传播。

项目成果

Uncertainty Analysis and Visualization for Nitrogen Leaching with the Maize-N Model

• DOI: 10.1109/bigdata50022.2020.9378105
• 发表时间: 2020-12
• 期刊: 2020 IEEE International Conference on Big Data (Big Data)
• 作者: B. Samani;S. Samani;Haishun Yang;Hongfeng Yu