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

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和在模拟期间传输数据的可用带宽。更高的性能减少了从科学模拟中获得结果所需的时间，并使科学进步更快。该项目旨在了解在使用有损压缩以减少科学模拟过程中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，从而显著减少了科学数据的量。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有效地应用于实践至关重要。该项目促进了大规模计算资源对STEM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。该项目考虑了在科学模拟中使用有损压缩的重要权衡，例如：能源效率，压缩和解压时间的敏感性以及压缩参数选择的压缩比，以及对统计度量的影响。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不同领域的真实科学数据的影响。理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。本项目由CCF/SHF核心项目和促进竞争性研究的既定项目（EPSCoR）共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.