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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Zircon U–Pb age, trace element and Hf isotope composition of Kongling terrane in the Yangtze Craton: refining the timing of Palaeoproterozoic high-grade metamorphism

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Characteristics of Spatial and Temporal Variations of Monthly Mean Surface Air Temperature over Qinghai-Tibet Plateau

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对地方电视台转播CCTV时延的测定

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CARBON DYNAMICS OF WETLAND IN THE SANJIANG PLAIN

1INTRODUCTIONWetlandsplayanimportant roleintheprocessofcar-bonstorage.Thetotalcarbonstoredindifferentkindsofwetlandsisabout15%-35%ofthetotalcarboninthegloballandsoils(POSTetal.,1982;GORHAM,1991).Inaddition,wetlandsaresignificantnaturalsources fortheatmospheric CH4 (MOORE,1994).It isestimatedthatabout110×1012gCH4 originates fromanaerobicdecompositioninthenaturalwetlands,CH4 emission fromthenaturalwetlandsis15%-30%oftheglobalCH4 emission andtheCH4 emission from thepeat land at hi…