SHF: Small: Using Error-Bounded Lossy Compression to Improve High-Performance Computing Systems and Applications

SHF：小型：使用误差有限有损压缩来改进高性能计算系统和应用程序

• 批准号: 1910197
• 负责人: Jon Calhoun
• 金额: $ 30万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910197&HistoricalAwards=false
• 关键词: SHF; Small; Using; Error; Bounded

Performance of scientific simulations that are executed on large-scale computers is highly dependent on the volume of data transferred and the available bandwidth to transfer the data during the simulation. Higher performance decreases the time necessary for obtaining results from a scientific simulation and enables faster scientific advancement. This project seeks to understand the trade-offs of performance and accuracy when using lossy compression to reduce the volume of data transferred during scientific simulations. Lossy data compression significantly reduces the volume of scientific data by trading a loss in accuracy of the decompressed data for larger reductions in the compressed data size. The goal of this project is to investigate how lossy compression impacts the fidelity of the results of scientific simulations, which is critical for understanding how lossy compression can be effectively applied in practice. This project fosters the use and importance of large-scale computing resources for undergraduate STEM students and includes research experiences for undergraduates who are actively engaged in research tasks. This project considers important trade-offs in using lossy compression for scientific simulations such as: energy efficiency, sensitivity of compression and decompression time and compression ratio to the selection of compression parameters, and impact on statistical measures. This project analyzes the effects of lossy compression on real-world scientific data from a diverse set of domains such as fluid dynamics, astrophysics, and genomics. Understanding these trade-offs guides the integration of lossy data compression into computational kernels and applications. This project is jointly funded by CCF/SHF core program and the Established Program to Stimulate Competitive Research (EPSCoR).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.

在大型计算机上执行的科学模拟的性能高度依赖于传输的数据量以及模拟期间传输数据的可用带宽。更高的性能可以减少从科学模拟中获取结果所需的时间，并实现更快的科学进步。 该项目旨在了解使用有损压缩来减少科学模拟期间传输的数据量时性能和准确性的权衡。有损数据压缩通过牺牲解压缩数据的准确性来换取压缩数据大小的更大减少，从而显着减少科学数据量。该项目的目标是研究有损压缩如何影响科学模拟结果的保真度，这对于理解有损压缩如何在实践中有效应用至关重要。该项目促进本科生 STEM 学生对大规模计算资源的使用和重要性，并为积极从事研究任务的本科生提供研究经验。该项目考虑了使用有损压缩进行科学模拟的重要权衡，例如：能源效率、压缩和解压时间的敏感性以及压缩比对压缩参数选择的影响，以及对统计测量的影响。该项目分析有损压缩对流体动力学、天体物理学和基因组学等不同领域的真实科学数据的影响。 了解这些权衡可以指导将有损数据压缩集成到计算内核和应用程序中。该项目由 CCF/SHF 核心计划和刺激竞争性研究既定计划 (EPSCoR) 共同资助。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

Analyzing the Energy Consumption of Synchronous and Asynchronous Checkpointing Strategies

分析同步和异步检查点策略的能耗

• DOI: 10.1109/supercheck56652.2022.00006
• 发表时间: 2022
• 期刊: Third International Symposium on Checkpointing for Supercomputing (SuperCheck-SC22
• 作者: Wilkins, Grant;Gossman, Mikaila J.;Nicolae, Bogdan;Smith, Melissa C.;Calhoun, Jon C.
• 通讯作者: Calhoun, Jon C.

Exploring Lossy Compressibility through Statistical Correlations of Scientific Datasets

通过科学数据集的统计相关性探索有损压缩性

• DOI: 10.1109/drbsd754563.2021.00011
• 发表时间: 2021
• 期刊: 2021 7th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-7
• 作者: Krasowska, David;Bessac, Julie;Underwood, Robert;Calhoun, Jon C.;Di, Sheng;Cappello, Franck
• 通讯作者: Cappello, Franck

Modeling Power Consumption of Lossy Compressed I/O for Exascale HPC Systems

对百亿亿级 HPC 系统有损压缩 I/O 的功耗进行建模

• DOI: 10.1109/ipdpsw55747.2022.00184
• 发表时间: 2022
• 期刊: 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW
• 作者: Wilkins, Grant;Calhoun, Jon C.
• 通讯作者: Calhoun, Jon C.

Exploring Data Reduction Techniques for Additive Manufacturing Analysis

探索增材制造分析的数据缩减技术

• DOI: 10.1109/drbsd56682.2022.00008
• 发表时间: 2022
• 期刊: 2022 8th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-7
• 作者: Nichols, Coleman;Fulp, Megan Hickman;DeBardeleben, Nathan;Calhoun, Jon C.
• 通讯作者: Calhoun, Jon C.

Productive and Performant Generic Lossy Data Compression with LibPressio

使用 LibPressio 进行高效且高性能的通用有损数据压缩

• DOI: 10.1109/drbsd754563.2021.00005
• 发表时间: 2021
• 期刊: 2021 7th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-7
• 作者: Underwood, Robert;Malvoso, Victoriana;Calhoun, Jon C.;Di, Sheng;Cappello, Franck
• 通讯作者: Cappello, Franck