ROIBIN-SZ: Fast and Science-Preserving Compression for Serial Crystallography
ROIBIN-SZ: Fast and Science-Preserving Compression for Serial Crystallography
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ROIBIN-SZ：用于串行晶体学的快速且保持科学性的压缩
作者：
Robert Underwood;Chun Hong Yoon;A. M. Gok;S. Di;F. Cappello
晶体学是研究蛋白质原子结构的主要技术，它产生大量信息，可能会对同步加速器和自由电子激光光源的存储及数据传输能力造成压力。有损压缩已被确定为应对不断增长的数据量的一种可能手段；然而，先前的方法未能以足够的速率产生足够的质量以满足科学需求。本文介绍了一种带有SZ有损压缩的感兴趣区域分箱（ROIBIN - SZ）方法，这是一种新颖的、并行的且加速的压缩方案，它通过对背景信息进行有损压缩来分离动态选择的关键区域的保存。我们对这种压缩方案的协同设计所产生的性能和质量结果进行了广泛评估并展示出来。我们在溶菌酶和硒代生物素 - 链霉亲和素上能够实现高达196倍和46.44倍的压缩比，同时充分保留数据，以便在接近即将出现的光源需求的带宽和规模下重建结构。
Crystallography is the leading technique to study atomic structures of proteins and produces enormous volumes of information that can place strains on the storage and data transfer capabilities of synchrotron and free-electron laser light sources. Lossy compression has been identified as a possible means to cope with the growing data volumes; however, prior approaches have not produced sufficient quality at a sufficient rate to meet scientific needs. This paper presents Region Of Interest BINning with SZ lossy compression (ROIBIN-SZ) a novel, parallel, and accelerated compression scheme that separates the dynamically selected preservation of key regions with lossy compression of background information. We perform and present an extensive evaluation of the performance and quality results made by the co-design of this compression scheme. We can achieve up to a 196x and 46.44x compression ratio on lysozyme and selenobiotinyl-streptavidin while preserving the data sufficiently to reconstruct the structure at bandwidths and scales that approach the needs of the upcoming light sources
OptZConfig：有损压缩配置的高效并行优化
影响因子：
5.3
作者：
Underwood, Robert;Calhoun, Jon C;Di, Sheng;Apon, Amy;Cappello, Franck
通讯作者：
Cappello, Franck
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
影响因子：
--
作者：
Underwood, Robert;Malvoso, Victoriana;Calhoun, Jon C.;Di, Sheng;Cappello, Franck
通讯作者：
Cappello, Franck
通过预测深度神经网络压缩时间演化图像数据
DOI：
--
发表时间：
2021
期刊：
Cloud and Grid Computing
影响因子：
--
作者：
Roy, Rupak;Sato, Kento;Bhattachrya, Subhadeep;Fang, Xingang;Joti, Yasumasa;Hatsui, Takaki;Hiraki, Toshiyuki;Guo, Jian;Yu, Weikuan.
通讯作者：
Yu, Weikuan.