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.

在大规模计算机上执行的科学模拟的性能高度取决于传输的数据量和可用的带宽以在模拟过程中传输数据。较高的性能减少了从科学模拟获得结果所需的时间，并可以更快地进行科学进步。 该项目在使用有损压缩时旨在了解性能和准确性的权衡，以减少科学模拟过程中传输的数据量。有损耗的数据压缩可以通过将解压缩数据的准确性损失交换以减少压缩数据大小的较大减少来大大减少科学数据的量。该项目的目的是研究有损压缩如何影响科学模拟结果的忠诚度，这对于了解如何有效地在实践中应用有损压缩至关重要。该项目促进了大型计算资源对本科生的使用和重要性，并为积极从事研究任务的本科生提供了研究经验。该项目考虑了使用有损压缩来进行科学模拟的重要权衡，例如：能源效率，压缩时间和减压时间和压缩比与压缩参数的选择以及对统计措施的影响。该项目分析了有损压缩对来自流体动力学，天体物理学和基因组学等各种领域的现实科学数据的影响。 了解这些权衡指导将有损数据压缩整合到计算内核和应用中。该项目由CCF/SHF核心计划共同资助，并启发竞争性研究的既定计划（EPSCOR）。该奖项反映了NSF的法定任务，并认为使用基金会的知识分子优点和更广泛的影响评估标准，认为值得通过评估来获得支持。

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