Hybrid Approaches for Data Reduction of Spatiotemporal Scientific Applications

Scientists conduct large-scale simulations to compute derived quantities from primary data. Thus, it is crucial that data compression techniques maintain bounded errors on these derived quantities or quantities of interest (QOI). For many spatiotemporal applications, these QOIs are binary in nature and represent presence or absence of a physical phenomenon. In this work, we propose to use a hybrid approah for differential compression for such applications. We use a neural network (NN) approach to determine regions-of-interest (ROIs) where the binary QOIs are going to be prevalent. This is then used with traditional approaches that compress at a lower level (and higher accuracy) for these ROIs as compared to other regions.

科学家们进行大规模模拟，以便从原始数据计算派生量。因此，数据压缩技术在这些派生量或感兴趣的量（QOI）上保持有限误差至关重要。对于许多时空应用，这些QOI本质上是二元的，代表一种物理现象的存在与否。在这项工作中，我们建议对这类应用采用一种混合差分压缩方法。我们使用神经网络（NN）方法来确定感兴趣区域（ROI），在这些区域二元QOI将会普遍存在。然后，与其他区域相比，针对这些ROI，将其与在较低层级（且精度更高）进行压缩的传统方法一起使用。