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.     

基于GPIB接口的计数器数据采集与控制软件

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Geochemical survey of thermal system in Northeast China and their geothermometric characters

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CPLD技术及其在现代时统中的应用

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采用各向同性弹性地球模型推导了地面位移场速率与地壳内任意点应力场变化的边界积分关系,同时利用青藏高原东北缘1999～2001年观测的GPS资料对观测区地壳深度为5 km和25 km的主应力和最大剪应力进行了计算分析.结果表明,青藏高原东北缘的主应力变化主要集中在祁连山断裂、海原断裂等,在1920～1954年间历史上发生过多次震级为7.0～8.5级强震的断裂附近,并具有主应力变化沿断层走向分布、最大剪应力沿断层走向交替变化等特征.     

DISASTER ANALYSIS AND COUNTERMEASURES OF LAND SUBSIDENCE CAUSED BY COAL CUTTING IN CHINA

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