CRII: OAC: Enabling Quantities-of-Interest Error Control for Trust-Driven Lossy Compression

CRII：OAC：为信任驱动的有损压缩启用感兴趣数量错误控制

• 批准号: 2153451
• 负责人: Xin Liang
• 金额: $ 17.5万
• 依托单位: Missouri University of Science and Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2153451&HistoricalAwards=false
• 关键词: CRII; OAC; Enabling; Quantities; Interest

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Scientific simulations and instruments are producing data at volumes and velocities that overwhelm network and storage systems. Although error-controlled lossy compressors have been employed to mitigate these data issues, many scientists still feel reluctant to adopt them because these compressors provide no guarantee on the accuracy of downstream analysis results derived from raw data. This project aims to fill this gap by developing a trust-driven lossy data compression infrastructure capable of strictly controlling the errors in downstream analysis theoretically and practically to facilitate the use of data reduction in scientific applications. Success of this project will promote the progress of science in multiple disciplines via effective data reduction, and contribute to resolving important societal problems including electric generation, weather forecasting, material design, and transportation. Moreover, this project will contribute to the growth and development of future generations of scientists and engineers through educational and engagement activities, including development of new curriculum and recruitment of K-12 students.Existing lossy compression techniques either overlook error quantification or provide error control only for raw data, leaving uncertainties in the outcome of downstream quantities of interest (QoIs) computed from the raw data. This greatly concerns many computational scientists who wish to reduce their data while preserving necessary information, preventing them from adopting lossy compression in their applications. This research will address these problems through an integration of theory and implementation via three tasks. First, a novel theory enabling error control on downstream QoIs will be developed. This will fundamentally address the trustability issues of existing error controlled lossy compressors that provide error control only on raw data. Second, an optimization method ensuring tight error control will be applied based on rigorous analysis, to achieve higher compression ratios under the same requirements. Third, a scalable infrastructure will be built through a careful integration with advanced compression frameworks and tailored parallelization based on target QoIs, in order to take full advantage of existing compression algorithms and computational patterns in the target QoIs. The project will enable application scientists to store the most valuable information in their data based on their unique needs, creating opportunities for novel findings in multiple scientific disciplines including climatology, cosmology, and seismology.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.

该奖项的全部或部分资金来源于《2021 年美国救援计划法案》（公法 117-2）。科学模拟和仪器产生的数据量和速度足以淹没网络和存储系统。尽管已采用误差控制有损压缩器来缓解这些数据问题，但许多科学家仍然不愿意采用它们，因为这些压缩器无法保证从原始数据得出的下游分析结果的准确性。该项目旨在通过开发一种信任驱动的有损数据压缩基础设施来填补这一空白，该基础设施能够在理论上和实践上严格控制下游分析中的错误，以促进数据缩减在科学应用中的使用。该项目的成功将通过有效的数据缩减促进多个学科的科学进步，并有助于解决包括发电、天气预报、材料设计和交通等重要的社会问题。此外，该项目将通过教育和参与活动，包括开发新课程和招收 K-12 学生，为未来几代科学家和工程师的成长和发展做出贡献。现有的有损压缩技术要么忽视误差量化，要么仅提供误差控制对于原始数据，根据原始数据计算出的下游感兴趣量 (QoIs) 的结果存在不确定性。这让许多计算科学家非常担忧，他们希望在保留必要信息的同时减少数据，从而阻止他们在应用程序中采用有损压缩。本研究将通过三项任务，通过理论与实施的结合来解决这些问题。首先，将开发一种能够对下游 QoIs 进行错误控制的新理论。这将从根本上解决现有错误控制有损压缩器的可信性问题，这些压缩器仅对原始数据提供错误控制。其次，在严格分析的基础上，采用严格误差控制的优化方法，在相同要求下实现更高的压缩比。第三，将通过与先进压缩框架的仔细集成以及基于目标 QoI 的定制并行化来构建可扩展的基础设施，以便充分利用目标 QoI 中的现有压缩算法和计算模式。该项目将使应用科学家能够根据自己的独特需求在数据中存储最有价值的信息，为气候学、宇宙学和地震学等多个科学学科的新发现创造机会。该奖项反映了 NSF 的法定使命，并被认为值得获得通过使用基金会的智力优势和更广泛的影响审查标准进行评估来提供支持。

项目成果

Region-adaptive, Error-controlled Scientific Data Compression using Multilevel Decomposition

• DOI: 10.1145/3538712.3538717
• 发表时间: 2022-07
• 期刊: Proceedings of the 34th International Conference on Scientific and Statistical Database Management
• 作者: Qian Gong;Ben Whitney;Chengzhu Zhang;Xin Liang;A. Rangarajan;Jieyang Chen;Lipeng Wan;P. Ullrich;Qing Liu;R. Jacob;Sanjay Ranka;S. Klasky

Toward Quantity-of-Interest Preserving Lossy Compression for Scientific Data

• DOI: 10.14778/3574245.3574255
• 发表时间: 2022-12
• 期刊: Proc. VLDB Endow.
• 作者: Pu Jiao;S. Di;Hanqi Guo;Kai Zhao;Jiannan Tian;Dingwen Tao;Xin Liang;F. Cappello

Dynamic Quality Metric Oriented Error Bounded Lossy Compression for Scientific Datasets

科学数据集的动态质量度量导向误差有损压缩

• DOI: 10.1109/sc41404.2022.00067
• 发表时间: 2022
• 期刊: Storage and Analysis
• 作者: Liu, Jinyang;Di, Sheng;Zhao, Kai;Liang, Xin;Chen, Zizhong;Cappello, Franck
• 通讯作者: Cappello, Franck

Toward Feature-Preserving Vector Field Compression

走向保留特征的矢量场压缩

• DOI: 10.1109/tvcg.2022.3214821
• 发表时间: 2022
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Liang, Xin;Di, Sheng;Cappello, Franck;Raj, Mukund;Liu, Chunhui;Ono, Kenji;Chen, Zizhong;Peterka, Tom;Guo, Hanqi
• 通讯作者: Guo, Hanqi

SZ3: A Modular Framework for Composing Prediction-Based Error-Bounded Lossy Compressors

SZ3：用于组合基于预测的误差有限有损压缩器的模块化框架

• DOI: 10.1109/tbdata.2022.3201176
• 发表时间: 2023
• 期刊: IEEE Transactions on Big Data
• 影响因子: 7.2
• 作者: Liang, Xin;Zhao, Kai;Di, Sheng;Li, Sihuan;Underwood, Robert;Gok, Ali M.;Tian, Jiannan;Deng, Junjing;Calhoun, Jon C.;Tao, Dingwen
• 通讯作者: Tao, Dingwen