SHF:Small: Collaborative Research: Understanding, Modeling, and System Support for HPC Data Reduction

SHF:Small：协作研究：HPC 数据缩减的理解、建模和系统支持

• 批准号: 1813081
• 负责人: Xubin He
• 金额: $ 19.99万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1813081&HistoricalAwards=false
• 关键词: SHF; Small; Collaborative; Research; Understanding

High-performance computing (HPC) enables high fidelity science missions that capture microscopic phenomena that were impossible to study in the past. In order to allow science missions to be accomplished in a timely manner, it is critical to manage the massive datasets that HPC generates in efficient way so that the time to knowledge can be shortened. This project aims to understand the role and usage of data reduction in large computational applications. Research and educational opportunities are provided to train a new generation of computer scientists and engineers, particularly those under-represented groups, to ensure the U.S. competitiveness in high-performance computing. The goal of this project is to address a number of critical gaps in using data reduction for HPC-based science missions. In particular, 1) the impact of reduction error on scientific discovery is mathematically and experimentally studied; 2) analytical models are formulated to estimate the reduction performance, without forcing users to compress the full data. For data-intensive applications, having this capability is important so that domain scientists do not have to go through the cumbersome trial-and-error process to figure out what reduction can offer; 3) the project provides potentially more efficient data analysis and reduction capabilities for exascale computing. The integrated research activities in this NSF project will significantly improve the understanding and usage of data reduction on future systems.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.

高性能计算 (HPC) 能够实现高保真科学任务，捕捉过去无法研究的微观现象。为了及时完成科学任务，有效管理 HPC 生成的海量数据集以缩短获得知识的时间至关重要。该项目旨在了解数据缩减在大型计算应用中的作用和用法。提供研究和教育机会来培训新一代计算机科学家和工程师，特别是那些代表性不足的群体，以确保美国在高性能计算领域的竞争力。 该项目的目标是解决在基于 HPC 的科学任务中使用数据缩减的一些关键差距。特别是，1）通过数学和实验研究了减少误差对科学发现的影响； 2）制定分析模型来估计缩减性能，而不强迫用户压缩完整数据。对于数据密集型应用程序来说，拥有这种能力非常重要，这样领域科学家就不必经历繁琐的试错过程来找出减少可以提供什么； 3) 该项目为百亿亿次计算提供了潜在的更高效的数据分析和缩减能力。该 NSF 项目中的综合研究活动将显着提高对未来系统数据缩减的理解和使用。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。

项目成果

Exploring Transfer Learning to Reduce Training Overhead of HPC Data in Machine Learning

• DOI: 10.1109/nas.2019.8834723
• 发表时间: 2019-08
• 期刊: 2019 IEEE International Conference on Networking, Architecture and Storage (NAS)
• 作者: Tong Liu;Shakeel Alibhai;Jinzhen Wang;Qing Liu;Xubin He;Chentao Wu

Zperf: A Statistical Gray-Box Approach to Performance Modeling and Extrapolation for Scientific Lossy Compression

Zperf：科学有损压缩性能建模和外推的统计灰盒方法

• DOI: 10.1109/tc.2023.3257517
• 发表时间: 2023
• 期刊: IEEE Transactions on Computers
• 影响因子: 3.7
• 作者: Wang, Jinzhen;Chen, Qi;Liu, Tong;Liu, Qing;He, Xubin
• 通讯作者: He, Xubin

EC-Fusion: An Efficient Hybrid Erasure Coding Framework to Improve Both Application and Recovery Performance in Cloud Storage Systems

• DOI: 10.1109/ipdps47924.2020.00029
• 发表时间: 2020-05
• 期刊: 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
• 作者: Han Qiu;Chentao Wu;Jie Li;M. Guo;Tong Liu;Xubin He;Yuanyuan Dong;Yafei Zhao

Locality-based transfer learning on compression autoencoder for efficient scientific data lossy compression

• DOI: 10.1016/j.jnca.2022.103452
• 发表时间: 2022-06
• 期刊: J. Netw. Comput. Appl.
• 作者: Nan Wang;Tong Liu;Jinzhen Wang;Qing Liu;Shakeel Alibhai;Xubin He

High-Ratio Lossy Compression: Exploring the Autoencoder to Compress Scientific Data

• DOI: 10.1109/tbdata.2021.3066151
• 发表时间: 2023-02
• 期刊: IEEE Transactions on Big Data
• 影响因子: 7.2
• 作者: Tong Liu;Jinzhen Wang;Qing Liu;Shakeel Alibhai;Tao Lu;Xubin He