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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1INTRODUCTIONTheIGBP-IHDP(InternationalGeosphere-BiosphereProgramme,andInternationalHumanDimensionPro-gramme)jointproject—LUCC(landuse/coverchange)isaninterdisciplinaryprogramdesignedtoimprovetheunderstandingofthedynamicsofland-useandland-coverchangeasinputstoandconsequencesofglobalenvironmentalchangeandsustainabledevelopment(CHARLOTTE,2002).Inparticularthedifferentbio-geochemicalmodeling(e.g.theglobalcarboncycle)ac-tivitiesandcomparativestudies,suchasthoseconduct-edbyIGBP-G…     

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在目前常用的周跳探测与修复方法基础上 ,提出了首先将观测资料按照观测历元不连续分成若干小弧段 ,然后利用差分法进行周跳探测 ,根据差分后周跳放大的特性判断周跳和野值 ,并确定其位置利用宽带组合和电离层组合的方法解算周跳大小。通过实例验证了其有效性。     

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