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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CCD联合平差方法及其初步实例检验

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电离氢区S140,S141和S142的红外发射研究

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关于网络用户经济效益评测模型的研究

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Energy flow in Branchiura sowerbyi (Oligochaeta: tubificidae) in a shallow macrophyte-dominated lake, Biandantang Lake

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基于Chandra的活动星系核观测

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ENERGY FLOW OF BELIAMYA AERUGINOSA IN A SHALLOW ALGAL LAKE, HOUHU LAKE （WUHAN, CHINA）

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