Articles | Volume 12, issue 2
https://doi.org/10.5194/gmd-12-749-2019
https://doi.org/10.5194/gmd-12-749-2019
Development and technical paper
 | 
20 Feb 2019
Development and technical paper |  | 20 Feb 2019

MP CBM-Z V1.0: design for a new Carbon Bond Mechanism Z (CBM-Z) gas-phase chemical mechanism architecture for next-generation processors

Hui Wang, Junmin Lin, Qizhong Wu, Huansheng Chen, Xiao Tang, Zifa Wang, Xueshun Chen, Huaqiong Cheng, and Lanning Wang

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Chang, J. S., Brost, R. A., Isaksen, I. S. A., Madronich, S., Middleton, P., Stockwell, W. R., and Walcek, C. J.: A three-dimensional Eulerian acid deposition model: Physical concepts and formulation, J. Geophys. Res.-Atmos., 92, 14681–14700, 1987. 
Chen, H., Wang, Z., Qizhong, W. U., Jianbin, W. U., Yan, P., Tang, X., and Wang, Z.: Application of Air Quality Multi-Model Forecast System in Guangzhou: Model Description and Evaluation of PM10 Forecast Performance, Clim. Environ. Res., 18, 427–435, 2013. 
Chen, H. S., Wang, Z. F., Li, J., Tang, X., Ge, B. Z., Wu, X. L., Wild, O., and Carmichael, G. R.: GNAQPMS-Hg v1.0, a global nested atmospheric mercury transport model: model description, evaluation and application to trans-boundary transport of Chinese anthropogenic emissions, Geosci. Model Dev., 8, 2857–2876, https://doi.org/10.5194/gmd-8-2857-2015, 2015. 
Feng, F., Wang, Z., Li, J., and Carmichael, G. R.: A nonnegativity preserved efficient algorithm for atmospheric chemical kinetic equations, Appl. Mathe. Comput., 271, 519–531, 2015. 
Gao, M., Carmichael, G. R., Wang, Y., Saide, P. E., Yu, M., Xin, J., Liu, Z., and Wang, Z.: Modeling study of the 2010 regional haze event in the North China Plain, Atmos. Chem. Phys., 16, 1673–1691, https://doi.org/10.5194/acp-16-1673-2016, 2016. 
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Short summary
A new framework was designed for the widely used Carbon Bond Mechanism Z (CBM-Z) gas-phase chemical kinetics kernel to adapt the single-instruction, multiple-data (SIMD) technology in next-generation processors like Knights Landing (KNL) to improve their calculation performance. The optimization is aimed at implementing the fine-grain level parallelization of CBM-Z. The test results showed significant acceleration with our optimization on both CPU and KNL platforms.