Although supercomputers are becoming increasingly powerful, their components have thus far not scaled proportionately. Compute power is growing enormously and is enabling finely resolved simulations that produce never-before-seen features. However, I/O capabilities lag by orders of magnitude, which means only a fraction of the simulation data can be stored for post hoc analysis. Prespecified plans for saving features and quantities of interest do not work for features that have not been seen before. Data-driven intelligent sampling schemes are needed to detect and save important parts of the simulation while it is running. Here, we propose a novel sampling scheme that reduces the size of the data by orders-of-magnitude while still preserving important regions. The approach we develop selects points with unusual data values and high gradients. We demonstrate that our approach outperforms traditional sampling schemes on a number of tasks.

中文翻译：

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通过多标准重要性分析的概率数据驱动抽样。

尽管超级计算机变得越来越强大，但它们的组件迄今为止还没有成比例地扩展。计算能力正在大幅增长，并且正在实现精细解析的模拟，从而产生前所未有的特征。然而，I/O 能力滞后几个数量级，这意味着只能存储一小部分仿真数据用于事后分析。用于保存感兴趣的特征和数量的预先指定的计划不适用于以前从未见过的特征。需要数据驱动的智能采样方案来检测和保存模拟运行时的重要部分。在这里，我们提出了一种新颖的采样方案，该方案将数据的大小减少了数量级，同时仍保留了重要区域。我们开发的方法选择具有异常数据值和高梯度的点。