A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing期刊界 All Journals 搜尽天下杂志传播学术成果专业期刊搜索期刊信息化学术搜索

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

Authors:	Peng Yue Fan Gao Boyi Shangguan Zheren Yan

Institution:	1. School of Remote Sensing and Information Engineering, Wuhan University , Wuhan, Hubei, China;2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University , Wuhan, Hubei, China;3. Hubei Province Engineering Center for Intelligent Geoprocessing (HPECIG), Wuhan University , Wuhan, Hubei, China;4. Collaborative Innovation Center of Geospatial Technology , Wuhan, Hubei, China pyue@whu.edu.cnhttps://orcid.org/0000-0003-3006-4542;6. School of Remote Sensing and Information Engineering, Wuhan University , Wuhan, Hubei, China

Abstract:	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.

Keywords:	Domain decomposition load balancing machine learning parallel geoprocessing AI GIS

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