Collaborative Research: OAC Core: Topology-Aware Data Compression for Scientific Analysis and Visualization

合作研究：OAC 核心：用于科学分析和可视化的拓扑感知数据压缩

• 批准号: 2313122
• 负责人: Xin Liang
• 金额: $ 20.18万
• 依托单位: University of Kentucky Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313122&HistoricalAwards=false
• 关键词: Collaborative; Research; OAC; Core; Topology

Today's large-scale simulations are producing vast amounts of data that are revolutionizing scientific thinking and practices. For instance, a fusion simulation can produce 200 petabytes of data in a single run, while a climate simulation can generate 260 terabytes of data every 16 seconds with a 1 square kilometer resolution. As the disparity between data generation rates and available I/O bandwidths continues to grow, data storage and movement are becoming significant bottlenecks for extreme-scale scientific simulations in terms of in situ and post hoc analysis and visualization. The disparity necessitates data compression, which compresses large-scale simulations data in situ, and decompresses data in situ and/or post hoc for analysis and exploration. On the other hand, a critical step in extracting insight from large-scale simulations involves the definition, extraction, and evaluation of features of interest. Topological data analysis has provided powerful tools to capture features from scientific data in turbulent combustion, astronomy, climate science, computational physics and chemistry, and ecology. While lossy compression is leveraged to address the big data challenges, most existing lossy compressors are agnostic of and thus fail to preserve topological features that are essential to scientific discoveries. This project aims to research and develop advanced lossy compression techniques and software that preserve topological features in data for in situ and post hoc analysis and visualization at extreme scales. The success of this project will promote scientific research on driving applications in cosmology, climate, and fusion by enabling efficient and effective compression for scientific data, and the impact scales to other science and engineering disciplines. Furthermore, the research products of this project will be integrated into visualization and parallel processing curricula, disseminated via research and training workshops, and used to attract underrepresented students for broadening participation in computing. This project tackles the data compression, analysis, and visualization needs in extreme-scale scientific simulations by developing a suite of topology-aware data compression algorithms for scalar field and vector field data. Such algorithms effectively reduce the size of data while preserving critical features defined by topological notions. This project will define and enforce topology-aware constraints over advanced lossy compression algorithms. Such capabilities have not been studied systematically within today’s data compression paradigm. This project will impact specific fields, including computational science, data analysis, data compression, and visualization, and the broader scientific community. The research products of this project will be delivered as publicly available software to significantly advance the research cyberinfrastructure for current and upcoming exascale systems. This project will foster novel discoveries in multiple scientific disciplines beyond cosmology, climate, and fusion by enabling efficient and effective compression on a wide range of platforms.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

当今的大规模模拟正在产生大量数据，这些数据正在彻底改变科学思维和实践，例如，核聚变模拟一次可以产生 200 PB 的数据，而气候模拟每 16 秒可以产生 260 TB 的数据。 1 平方公里的分辨率随着数据生成速率和可用 I/O 带宽之间的差距不断扩大，数据存储和移动正在成为超大规模科学模拟的重大瓶颈。现场和事后分析和可视化的差异需要数据压缩，即在现场压缩大规模模拟数据，并在现场和/或事后解压缩数据以进行分析和探索。从大规模模拟中获取洞察涉及感兴趣特征的定义、提取和评估拓扑数据分析为从湍流燃烧、天文学、气候科学、计算物理和化学等领域的科学数据中捕获特征提供了强大的工具。虽然有损压缩被用来解决大数据挑战，但大多数现有的有损压缩器不知道，因此无法保留对科学发现至关重要的拓扑特征。该项目旨在研究和开发先进的有损压缩技术和软件。保留数据中的拓扑特征，以便在极端尺度下进行原位和事后分析和可视化，该项目的成功将通过对科学数据及其影响进行高效和有效的压缩，促进推动宇宙学、气候和融合应用的科学研究。秤此外，该项目的研究产品将被整合到可视化和并行处理课程中，通过研究和培训研讨会进行传播，并用于吸引代表性不足的学生扩大对计算的参与。通过开发一套针对标量场和矢量场数据的拓扑感知数据压缩算法，这些算法可以有效地减少数据大小，同时保留拓扑概念定义的关键特征。该项目将定义并强制执行对高级有损压缩算法的拓扑感知约束。在当今的数据压缩范例中，此类功能尚未得到系统研究。该项目将影响特定领域，包括计算科学、数据分析、数据压缩和可视化。该项目的研究产品将作为公开软件提供，以显着推进当前和即将到来的亿亿级系统的研究网络基础设施。该项目将通过实现高效的方式，促进宇宙学、气候和核聚变以外的多个科学学科的新发现。和有效的该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。