论文标题
通过局部平衡条件模拟在GPU上快速填充大量数据
Fast gap-filling of massive data by local-equilibrium conditional simulations on GPU
论文作者
论文摘要
现代时空数据集的不断增长的大小，例如通过遥感收集的数据集，需要新技术来实现其高效和自动化处理，包括缺少值的差距填充差距。基于CUDA的GPU并行化已成为一种流行的方式，可以显着提高各种方法的计算效率。最近，我们提出了一种计算高效，竞争性但简单的空间预测方法，该方法灵感来自统计物理模型，称为改良平面旋转器（MPR）方法。与CPU计算相比，其GPU实施允许超过两个数量级的其他令人印象深刻的计算加速度。在当前的研究中，我们提出了一种相当普遍的方法，用于通过向模型参数引入空间可变性，以在GPU实施的空间预测方法中建模空间异质性。假设``局部''平衡条件从非平衡条件模拟获得了未知值的预测。我们证明了所提出的方法会导致预测性能和计算效率的显着提高。
The ever-growing size of modern space-time data sets, such as those collected by remote sensing, requires new techniques for their efficient and automated processing, including gap-filling of missing values. CUDA-based parallelization on GPU has become a popular way to dramatically increase computational efficiency of various approaches. Recently, we have proposed a computationally efficient and competitive, yet simple spatial prediction approach inspired from statistical physics models, called modified planar rotator (MPR) method. Its GPU implementation allowed additional impressive computational acceleration exceeding two orders of magnitude in comparison with CPU calculations. In the current study we propose a rather general approach to modelling spatial heterogeneity in GPU-implemented spatial prediction methods for two-dimensional gridded data by introducing spatial variability to model parameters. Predictions of unknown values are obtained from non-equilibrium conditional simulations, assuming ``local'' equilibrium conditions. We demonstrate that the proposed method leads to significant improvements in both prediction performance and computational efficiency.
