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.

该奖项是根据2021年《美国救援计划法》的全部或部分资助的（公共法117-2）。科学的模拟和工具正在以压倒网络和存储系统的量和速度生产数据。尽管已使用错误控制的有损压缩机来减轻这些数据问题，但许多科学家仍然不愿意采用它们，因为这些压缩机无法保证从原始数据中得出的下游分析结果的准确性。该项目的目的是通过开发一个能够严格控制下游分析中的错误，理论上和实际上以促进科学应用程序中的数据减少的使用，以填补这一空白。该项目的成功将通过有效的数据来促进多个学科的科学进步，并有助于解决重要的社会问题，包括发电，天气预报，材料设计和运输。此外，该项目将通过教育和参与活动为子孙后代的发展和发展做出贡献，包括开发新课程和K-12学生的招聘。存在损失的压缩技术要么仅忽略错误量化，要么仅为原始数据提供错误的错误控制，从而在QOIS的下降量（QOIS）的结果中造成了不确定性的计算。这极大地涉及许多希望减少数据的计算科学家，同时保留必要的信息，从而阻止他们在应用程序中采用损失压缩。这项研究将通过通过三个任务整合理论和实施来解决这些问题。首先，将开发出一种新的理论，从而可以对下游QOIS进行误差控制。这将从根本上解决现有的控制损失压缩机的可信度问题，这些压缩机仅在原始数据上提供错误控制。其次，一种优化方法确保将基于严格的分析应用严格的误差控制，以在相同的要求下实现更高的压缩比。第三，将通过基于目标QOI的高级压缩框架和量身定制的并行化来构建可扩展的基础架构，以充分利用目标Qois中现有的压缩算法和计算模式。该项目将使应用科学家根据其独特的需求在其数据中存储最有价值的信息，从而为多个科学学科的新发现创造机会，包括气候学，宇宙学和地震学。该奖项反映了NSF的法定任务，并认为通过使用该基金会的知识分子和更广泛的影响来审查Criteria的评估，并被视为值得通过评估的支持。

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