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

通过利用先进的并行计算系统，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能够以更高的分辨率和更高的复杂性进行详细的模拟。先进的可视化技术是科学家以高交互性和保真度探索大量复杂模拟数据以研究各种物理，化学和生物现象所必需的。虽然近年来可视化技术取得了显着的进步，传统的可视化技术还没有准备好的exascale系统和应用程序。预计未来的亿亿级系统将以众核处理器、深存储器层次结构和高并发级别为特征。新的可视化技术的设计必须适应从复杂和超大数据集以及这些新兴的硬件和软件趋势中及时发现的需要。该项目的目标是通过以整体的方式研究具有科学模拟的可视化管道的完整过程来解决当前的技术差距，从而确保exascale数据可视化分析的并行性和效率。该项目将把研究与教学和推广方案结合起来，其中科学应用的可视化将被用作一种有效手段，以提高学生对科学和工程研究的兴趣和熟练程度，并吸引和留住本科生和研究生，特别是女生，该项目计划直接说明与exascale计算的可视化分析的关键组件之间的复杂相互依赖关系。本项目主要研究三个方面的内容：（1）开发一种新的原位数据简化和索引算法，以捕捉大规模仿真数据的本质;（2）研究并行可视化算法，以保证基于原位紧凑数据表示的大规模仿真数据的高吞吐量和高分辨率探索的可扩展性能;以及（3）设计用户界面以解析和部署用于可视化分析的应用知识，从而以增强的用户体验和性能从现场模拟输出中获取关键的科学发现。该项目由现实世界的大规模科学应用驱动，涉及对具有异构数据类型的不断发展的现象进行建模和分析，并要求可视化分析的可扩展能力。科学合作者将参与解决方案的开发、评估和部署，以缩小先进可视化技术与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。在该项目中开发的技术将很容易适应于具有类似需求的主要示范目标之外的许多应用，从而对科学家的数据分析和可视化能力产生重大影响。这项研究的成功将潜在地改变传统的科学发现管道，并加速大规模模拟数据的研究。项目成果将通过项目网站（http：//cse.unl.edu/tagyu/research/nsf15_exascale/）公布的不同地点和形式传播。

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/).