Efficient GPU-Based Parallel Kriging Algorithm for Predicting the Air Quality Index

Air quality index (AQI) is an evaluation standard of air quality and a major concern in the daily lives of people. However, predicting the AQI is difficult; existing prediction methods are few and they cannot satisfy the requirements of real-time prediction. This paper proposes an efficient GPU-based parallel Kriging algorithm (GP-Kriging) for real-time AQI prediction. Parallel computing strategy is used in the sub-steps of the serial Kriging algorithm, and some steps are designed and implemented in GPU to accelerate the computing speed. The data sets of the experiments are collected from Beijing Environmental Monitoring Center. Experimental results show that the time requirement of the GP-Kriging algorithm is approximately 20 times faster than that of serial Kriging. The successful application of the GP-Kriging algorithm in this study suggests that the method can be used to predict AQI.

[1]  Hongda Hu,et al.  An improved coarse-grained parallel algorithm for computational acceleration of ordinary Kriging interpolation , 2015, Comput. Geosci..

[2]  Pejman Tahmasebi,et al.  Accelerating geostatistical simulations using graphics processing units (GPU) , 2012, Comput. Geosci..

[3]  Ramesh C. Jain,et al.  Integration of Diverse Data Sources for Spatial PM2.5 Data Interpolation , 2017, IEEE Transactions on Multimedia.

[4]  Fabrice Dupros,et al.  An Out-of-core GPU Approach for Accelerating Geostatistical Interpolation , 2014, ICCS.

[5]  Tangpei Cheng,et al.  Accelerating universal Kriging interpolation algorithm using CUDA-enabled GPU , 2013, Comput. Geosci..

[6]  M. Saafi,et al.  Comparison of linear and nonlinear kriging methods for characterization and interpolation of soil data , 2012 .

[7]  Erhan Kozan,et al.  A new approach to spatial data interpolation using higher-order statistics , 2015, Stochastic Environmental Research and Risk Assessment.

[8]  Ana Cortés,et al.  Parallel ordinary kriging interpolation incorporating automatic variogram fitting , 2011, Comput. Geosci..

[9]  Qun Wang,et al.  On Parallelizing Universal Kriging Interpolation Based on OpenMP , 2010, 2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science.

[10]  Francisco J. Jiménez-Hornero,et al.  Using general-purpose computing on graphics processing units (GPGPU) to accelerate the ordinary kriging algorithm , 2014, Comput. Geosci..

[11]  Mahmoud Al-Ayyoub,et al.  Accelerating compute intensive medical imaging segmentation algorithms using hybrid CPU-GPU implementations , 2017, Multimedia Tools and Applications.