Maintaining Trust in Reduction: Preserving the Accuracy of Quantities of Interest for Lossy Compression
Maintaining Trust in Reduction: Preserving the Accuracy of Quantities of Interest for Lossy Compression
复制标题
保持对缩减的信任：保持有损压缩感兴趣数量的准确性
DOI：
10.1007/978-3-030-96498-6_2
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
S. Klasky
中科院分区：
文献类型：
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作者：
Qian Gong;Xin Liang;Ben Whitney;J. Choi;Jieyang Chen;Lipeng Wan;S. Ethier;S. Ku;R. Churchill;Choong;M. Ainsworth;O. Tugluk;T. Munson;D. Pugmire;Rick Archibald;S. Klasky
随着数据大小的增长继续超过计算资源的增长，迫切需要能够显著减少数据量并量化压缩过程中产生的错误的数据缩减技术。压缩科学数据给约简技术带来了许多挑战，因为它通常是在非均匀或非结构化的网格上，来自高维空间，并且有许多需要保留的兴趣量(QOI)。为了说明这些挑战，我们关注来自大规模融合代码XGC的数据。XGC使用细胞内粒子(PIC)技术，每天从数千个时间步长生成数百PB的数据。XGC使用非结构化网格，需要从原始数据计算许多QOI，例如，减少的一个关键方面是我们需要确保从数据(密度、温度、通量表面平均动量等)得出QOI。保持较高的精确度。结果表明，通过在定义数据的高维非均匀网格上压缩XGC数据，并根据QOI的特征对分解系数进行自适应量化，使用多级压缩器(MGARD)获得的各种误差容限下的压缩比提高了十倍以上。然后，我们介绍了如何从数学上保证从简化的FI计算出的QOI的精度在压缩过程中保持不变。使用MGARD的数学QOI误差控制理论，在1000个时间步的模拟过程中，XGC密度的误差可以保持在用户指定的容差范围内，而传统的对要减少的数据的误差控制不能保证QOI的精度。
As the growth of data sizes continues to outpace computational resources, there is a pressing need for data reduction techniques that can significantly reduce the amount of data and quantify the error incurred in compression. Compressing scientific data presents many challenges for reduction techniques since it is often on non-uniform or unstructured meshes, is from a high-dimensional space, and has many Quantities of Interests (QoIs) that need to be preserved. To illustrate these challenges, we focus on data from a large scale fusion code, XGC. XGC uses a Particle-In-Cell (PIC) technique which generates hundreds of PetaBytes (PBs) of data a day, from thousands of timesteps. XGC uses an unstructured mesh, and needs to compute many QoIs from the raw data,f.One critical aspect of the reduction is that we need to ensure that QoIs derived from the data (density, temperature, flux surface averaged momentums, etc.) maintain a relative high accuracy. We show that by compressing XGC data on the high-dimensional, nonuniform grid on which the data is defined, and adaptively quantizing the decomposed coefficients based on the characteristics of the QoIs, the compression ratios at various error tolerances obtained using a multilevel compressor (MGARD) increases more than ten times. We then present how to mathematically guarantee that the accuracy of the QoIs computed from the reducedfis preserved during the compression. We show that the error in the XGC density can be kept under a user-specified tolerance over 1000 timesteps of simulation using the mathematical QoI error control theory of MGARD, whereas traditional error control on the data to be reduced does not guarantee the accuracy of the QoIs.