SZx: an Ultra-fast Error-bounded Lossy Compressor for Scientific Datasets
SZx: an Ultra-fast Error-bounded Lossy Compressor for Scientific Datasets
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SZx：用于科学数据集的超快速误差有界有损压缩器
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
F. Cappello
中科院分区：
文献类型：
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作者：
Xiaodong Yu;S. Di;Kai Zhao;Jiannan Tian;Dingwen Tao;Xin Liang;F. Cappello
当今的科学高性能计算（HPC）应用程序或先进仪器正在广泛的领域中产生大量数据，这给数据传输和存储带来了严重的负担。误差有界有损压缩技术在科学界得到了发展和广泛应用，因为它不仅可以显著减少数据量，而且可以根据用户指定的误差界严格控制数据失真。然而，现有的有损压缩器不能提供超快的压缩速度，这是相当多的应用或用例（例如内存压缩和在线仪器数据压缩）高度要求的。在本文中，我们提出了一种新的超快速错误有界有损压缩器，它可以获得相当高的压缩性能的CPU和GPU上，也有相当高的压缩比。主要贡献有三个方面：（1）我们提出了一个新的，通用的超快速错误有界有损压缩框架-UFZ，通过限制我们的设计，只有超轻量级的操作，如按位和加/减操作，仍然保持一定的高压缩比。(2)我们在CPU和GPU上实现了UFZ，并根据它们的体系结构仔细优化了性能。(3)我们在CPU和GPU上使用6个真实的生产级科学数据集进行了全面的评估。实验结果表明，UFZ在CPU和GPU上的压缩和解压缩速度是第二快的现有错误有界有损压缩器（SZ或ZFP）的2~ 16倍。
Today's scientific high performance computing (HPC) applications or advanced instruments are producing vast volumes of data across a wide range of domains, which introduces a serious burden on data transfer and storage. Error-bounded lossy compression has been developed and widely used in scientific community, because not only can it significantly reduce the data volumes but it can also strictly control the data distortion based on the use-specified error bound. Existing lossy compressors, however, cannot offer ultra-fast compression speed, which is highly demanded by quite a few applications or use-cases (such as in-memory compression and online instrument data compression). In this paper, we propose a novel ultra-fast error-bounded lossy compressor, which can obtain fairly high compression performance on both CPU and GPU, also with reasonably high compression ratios. The key contributions are three-fold: (1) We propose a novel, generic ultra-fast error-bounded lossy compression framework -- called UFZ, by confining our design to be composed of only super-lightweight operations such as bitwise and addition/subtraction operation, still keeping a certain high compression ratio. (2) We implement UFZ on both CPU and GPU and optimize the performance according to their architectures carefully. (3) We perform a comprehensive evaluation with 6 real-world production-level scientific datasets on both CPU and GPU. Experiments show that UFZ is 2~16X as fast as the second-fastest existing error-bounded lossy compressor (either SZ or ZFP) on CPU and GPU, with respect to both compression and decompression.
cuSZ：一种基于 GPU 的高效科学数据误差有限有损压缩框架
DOI：
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发表时间：
2020
期刊：
Proceedings of the ACM International Conference on Parallel Architectures and Compilation Techniques
影响因子：
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作者：
Jiannan Tian, Sheng Di
通讯作者：
Jiannan Tian, Sheng Di
FRaZ：科学浮点数据的通用高保真固定比率有损压缩框架
DOI：
10.1109/ipdps47924.2020.00065
发表时间：
2020
期刊：
2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS
影响因子：
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作者：
Underwood, Robert;Di, Sheng;Calhoun, Jon C.;Cappello, Franck
通讯作者：
Cappello, Franck