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Toward Decoupling the Selection of Compression Algorithms from Quality Constraints

机译：从质量约束中分离压缩算法的选择

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Data intense scientific domains use data compression to reduce the storage space needed. Lossless data compression preserves the original information accurately but on the domain of climate data usually yields a compression factor of only 2:1. Lossy data compression can achieve much higher compression rates depending on the tolerable error/precision needed. Therefore, the field of lossy compression is still subject to active research. From the perspective of a scientist, the compression algorithm does not matter but the qualitative information about the implied loss of precision of data is a concern. With the Scientific Compression Library (SCIL), we are developing a meta-compressor that allows users to set various quantities that define the acceptable error and the expected performance behavior. The ongoing work a preliminary stage for the design of an automatic compression algorithm selector. The task of this missing key component is the construction of appropriate chains of algorithms to yield the users requirements. This approach is a crucial step towards a scientifically safe use of much-needed lossy data compression, because it disentangles the tasks of determining scientific ground characteristics of tolerable noise, from the task of determining an optimal compression strategy given target noise levels and constraints. Future algorithms are used without change in the application code, once they are integrated into SCIL. In this paper, we describe the user interfaces and quantities, two compression algorithms and evaluate SCIL's ability for compressing climate data. This will show that the novel algorithms are competitive with state-of-the-art compressors ZFP and SZ and illustrate that the best algorithm depends on user settings and data properties.
机译：数据密集型科学领域使用数据压缩来减少所需的存储空间。无损数据压缩可以准确地保留原始信息，但是在气候数据领域，通常产生的压缩因子仅为2：1。有损数据压缩可以实现更高的压缩率，具体取决于所需的可容忍错误/精度。因此，有损压缩领域仍需积极研究。从科学家的角度来看，压缩算法并不重要，但是有关隐含数据精度损失的定性信息值得关注。借助科学压缩库（SCIL），我们正在开发一种元压缩器，该压缩器允许用户设置各种量，这些量定义了可接受的误差和预期的性能行为。正在进行的工作是设计自动压缩算法选择器的初步阶段。缺少关键组件的任务是构建适当的算法链以产生用户需求。这种方法是朝科学安全地使用急需的有损数据压缩迈出的关键一步，因为它将确定目标噪声水平和约束条件下确定最佳压缩策略的任务与确定可容忍噪声的科学地面特征的任务分离开来。一旦将它们集成到SCIL中，就可以使用将来的算法，而无需更改应用程序代码。在本文中，我们描述了用户界面和数量，两种压缩算法，并评估了SCIL压缩气候数据的能力。这将表明新颖的算法与最新的压缩器ZFP和SZ竞争，并说明最佳算法取决于用户设置和数据属性。

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