Collaborative Research: OAC Core: CEAPA: A Systematic Approach to Minimize Compression Error Propagation in HPC Applications

合作研究：OAC 核心：CEAPA：一种最小化 HPC 应用中压缩错误传播的系统方法

• 批准号: 2247060
• 负责人: Dingwen Tao
• 金额: $ 25万
• 依托单位: Indiana University
• 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: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2247060&HistoricalAwards=false
• 关键词: Collaborative; Research; OAC; Core; CEAPA

Today’s high-performance computing (HPC) applications produce vast volumes of data for post-analysis, presenting a major storage and I/O burden for HPC systems. To significantly reduce this burden, researchers have explored to use lossy compression techniques. While lossy compression can effectively reduce the size of data, it also introduces errors to the compressed data that often lead to incorrect computation results. As a result, scientists hesitate to use lossy compression in their scientific research. Thus, there is a critical need to develop an effective method to identify compression strategies which minimize error impact for a diversity of programs. This project aims to develop a systematic approach that helps scientists automatically select a lossy compression algorithm with the lowest error impact based their HPC programs and target compression ratios. It also integrates educational and outreach activities including student training and development of new curriculum on trustworthy data reduction and dependable HPC systems. Modeling compression error propagation in HPC programs is challenging because existing lossy compressors are developed with distinct principles that generate largely different compression errors on diverse HPC data. This project includes four key thrusts: (1) developing an accurate and efficient fault injection infrastructure that integrates with the fault models of commonly used lossy compression algorithms; (2) designing a fine-grained approach to characterize error propagation in HPC programs through program analysis and deposition based on the data dependencies and life cycle of compressed data; (3) developing a predictive model using machine learning techniques to select a compression strategy that minimizes the error impact on a given program and compression ratio; and (4) integrating the technique with domain-specific error impact metrics in real-world HPC applications and demonstrates the effectiveness of the technique by selecting compression strategies that give low error impact for the same ratios. Not only this project has an enormous positive impact on HPC cyberinfrastructure, but it also helps redefine the optimization of lossy compression techniques with emphasis on both efficiency and error impact.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.

当今的高性能计算（HPC）应用程序可产生大量的数据，用于分析后，提供了主要存储空间，并为HPC系统提供了I/O燃烧。为了大大减少这种燃烧，研究人员探索了使用损失压缩技术。虽然损耗压缩可以有效地减少数据的大小，但它也将错误引入了压缩数据，这些数据通常会导致不正确的计算结果。结果，科学家不愿在其科学研究中使用损失压缩。这是迫切需要开发一种有效的方法来识别压缩策略，以最大程度地减少对各种程序的错误影响。该项目旨在开发一种系统的方法，该方法可以帮助科学家自动选择基于HPC程序和目标压缩比的误差影响最低的损耗压缩算法。它还整合了教育和外展活动，包括学生培训和开发有关可信赖数据减少和可靠的HPC系统的新课程。在HPC程序中建模压缩误差传播是挑战的，因为现有的损耗压缩机的开发具有不同的原理，这些原理在潜水员HPC数据上产生了很大不同的压缩误差。该项目包括四个关键推力：（1）开发一个准确有效的断层注入基础架构，该基础结构与常用损耗压缩算法的断层模型集成； （2）设计一种细粒度的方法，以根据数据依赖性和压缩数据的生命周期来表征HPC程序中的错误传播； （3）使用机器学习技术开发预测模型，以选择一种压缩策略，以最大程度地减少对给定程序和压缩比的影响； （4）将该技术与现实世界中HPC应用中的域特异性错误影响指标相结合，并通过选择压缩策略来证明该技术的有效性，从而对相同比率产生较低的误差影响。该项目不仅对HPC网络基础架构产生了增强的积极影响，而且还有助于重新定义损失压缩技术的优化，重点是有效性和错误影响。该奖项反映了NSF的法定任务，并被认为是通过使用该基金会的智力和更广泛影响的评估来审查CRITERIA的评估来通过评估来获得的支持。

项目成果

Design of a Quantization-Based DNN Delta Compression Framework for Model Snapshots and Federated Learning

• DOI: 10.1109/tpds.2022.3230840
• 发表时间: 2023-03
• 期刊: IEEE Transactions on Parallel and Distributed Systems
• 影响因子: 5.3
• 作者: Haoyu Jin;Donglei Wu;Shuyu Zhang;Xiangyu Zou;Sian Jin;Dingwen Tao;Qing Liao;Wen Xia