A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.     

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案例推理的地学应用背景和方法

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新疆塔中南坡奥陶系的地层缺失和沉积相变化

按照奥陶系内部6个组沉积的时间片段拟定塔中南坡不同区块存在不同程度的缺失。部分关键层段的牙形石和几丁虫组合特征证明一间房组和恰尔巴克组在塔中部分井区是存在的,但恰尔巴克组的分布范围最狭窄。总体上,塔中南坡隆起区地层缺失较多,古城墟隆起基本完整。据缺失状况和岩相展布,显示塔中南坡的沉积单元具有由东往西迁移的特征,且各时段迁移的距离与速度存在较大差异。     

华北地台东部石炭系—二叠系优质煤储层形成分布控制因素

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无定河流域下游段河冰形成演变的数学模型研究

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中国东北依兰地区块状蓝片岩的地球化学特征

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