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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The auriferous veins at Jinniushan occurs within the Jinniushan faulted zone in the Kunyushan Granite. Optical observation reveals that gold ore body formed during the main stage of hydrothermal activity. Detailed geothermometric studies of fluid inclusions from the veins show that the forming temperature ranges between 130℃ and 370℃ and the salinity is from 4.01 to 15.21 wvt percent NaCl. The ore-forming fluid is featured by low to moderate salinity, and low to moderate temperature. According to investigations of the values of vapor/liquid and temperatures of the ore-forming fluids, we propose that the boiling fluid inclusions exist in the main mineralization stages. Fluid boiling is suggested as a mechanism for the precipitation of gold from the hydrothermal fluid in the Jinniushan gold deposit.     

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Is Precipitation the Dominant Controlling Factor of High Inorganic Nitrogen Content in the Changjiang River and Its Mouth？

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中国南部二叠、三叠纪“混生生物群”及二叠、三叠系界线

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