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Chinese Journal of Space Science ›› 2020, Vol. 40 ›› Issue (2): 169-175.doi: 10.11728/cjss2020.02.169

• Research Aeticles • Previous Articles     Next Articles

Parallel Computing Technology for CME Parameter Detection Model Based on MapReduce

YANG Shitong1,2, CAI Yanxia1,2, LU Guorui1, WANG Jingjing1   

  1. 1 National Space Science Center, Chinese Academy of Sciences, Beijing 100190;
    2 University of Chinese Academy of Sciences, Beijing 100049
  • Received:2019-03-12 Revised:2019-11-03 Online:2020-03-15 Published:2020-04-11

Abstract: Space environment prediction model is an important part of space environment business. Coronal Mass Ejection (CME) is the source of many space events and near-Earth space environment disturbances. The CME parameter detection model is an important part of the solar wind forecasting process. In order to improve the accuracy of solar wind forecasting in space environment forecasting, it is necessary to improve the accuracy of CME parameter detection. However, the model runs in serial mode with low calculating efficiency, which leads to long operation time of the model and can not meet the requirement. Based on the parallel computing framework of MapReduce, according to the characteristics of CME parameter detection model, the calculation flow of CME parameter detection model is improved. A CDMR (CME Detection under MapReduce) method is presented, which can realize the parallel computing of CME parameter detection model. Moreover, the running time of the CME parameter detection model between serial computing and MapReduce parallel computing is compared. The experimental results show that the running time is reduced by using MapReduce parallel computing, and the detection accuracy and calculation efficiency of the model are improved.

Key words: CME parameters detection model, MapReduce, Parallel calculating efficiency

CLC Number: