Articles | Volume 10, issue 8
https://doi.org/10.5194/gmd-10-2891-2017
https://doi.org/10.5194/gmd-10-2891-2017
Development and technical paper
 | 
01 Aug 2017
Development and technical paper |  | 01 Aug 2017

GNAQPMS v1.1: accelerating the Global Nested Air Quality Prediction Modeling System (GNAQPMS) on Intel Xeon Phi processors

Hui Wang, Huansheng Chen, Qizhong Wu, Junmin Lin, Xueshun Chen, Xinwei Xie, Rongrong Wang, Xiao Tang, and Zifa Wang

Related authors

Biogenic and anthropogenic contributions to urban terpenoid fluxes
Erin F. Katz, Caleb M. Arata, Eva Y. Pfannerstill, Robert J. Weber, Darian Ng, Michael J. Milazzo, Haley Byrne, Hui Wang, Alex B. Guenther, Camilo Rey-Sanchez, Joshua Apte, Dennis D. Baldocchi, and Allen H. Goldstein
EGUsphere, https://doi.org/10.5194/egusphere-2025-2682,https://doi.org/10.5194/egusphere-2025-2682, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
A long-term estimation of biogenic volatile organic compound (BVOC) emission in China from 2001–2016: the roles of land cover change and climate variability
Hui Wang, Qizhong Wu, Alex B. Guenther, Xiaochun Yang, Lanning Wang, Tang Xiao, Jie Li, Jinming Feng, Qi Xu, and Huaqiong Cheng
Atmos. Chem. Phys., 21, 4825–4848, https://doi.org/10.5194/acp-21-4825-2021,https://doi.org/10.5194/acp-21-4825-2021, 2021
Short summary
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
Geosci. Model Dev., 12, 749–764, https://doi.org/10.5194/gmd-12-749-2019,https://doi.org/10.5194/gmd-12-749-2019, 2019
Short summary
Sensitivity of biogenic volatile organic compound emissions to leaf area index and land cover in Beijing
Hui Wang, Qizhong Wu, Hongjun Liu, Yuanlin Wang, Huaqiong Cheng, Rongrong Wang, Lanning Wang, Han Xiao, and Xiaochun Yang
Atmos. Chem. Phys., 18, 9583–9596, https://doi.org/10.5194/acp-18-9583-2018,https://doi.org/10.5194/acp-18-9583-2018, 2018
Short summary
Summer ozone variation in North China based on satellite and site observations
Lihua Zhou, Jing Zhang, Hui Wang, Wenhao Xue, Xiaohui Zheng, and Siguang Zhu
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-537,https://doi.org/10.5194/acp-2018-537, 2018
Preprint retracted
Short summary

Related subject area

Atmospheric sciences
A new set of indicators for model evaluation complementing FAIRMODE's modelling quality objective (MQO)
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, and Enrico Pisoni
Geosci. Model Dev., 18, 4231–4245, https://doi.org/10.5194/gmd-18-4231-2025,https://doi.org/10.5194/gmd-18-4231-2025, 2025
Short summary
Impact of multiple radar wind profiler data assimilation on convective-scale short-term rainfall forecasts: OSSE studies over the Beijing–Tianjin–Hebei region
Juan Zhao, Jianping Guo, and Xiaohui Zheng
Geosci. Model Dev., 18, 4075–4101, https://doi.org/10.5194/gmd-18-4075-2025,https://doi.org/10.5194/gmd-18-4075-2025, 2025
Short summary
New submodel for emissions from Explosive Volcanic ERuptions (EVER v1.1) within the Modular Earth Submodel System (MESSy, version 2.55.1)
Matthias Kohl, Christoph Brühl, Jennifer Schallock, Holger Tost, Patrick Jöckel, Adrian Jost, Steffen Beirle, Michael Höpfner, and Andrea Pozzer
Geosci. Model Dev., 18, 3985–4007, https://doi.org/10.5194/gmd-18-3985-2025,https://doi.org/10.5194/gmd-18-3985-2025, 2025
Short summary
Quantifying the oscillatory evolution of simulated boundary-layer cloud fields using Gaussian process regression
Gunho Loren Oh and Philip H. Austin
Geosci. Model Dev., 18, 3921–3940, https://doi.org/10.5194/gmd-18-3921-2025,https://doi.org/10.5194/gmd-18-3921-2025, 2025
Short summary
Numerical investigations on the modelling of ultrafine particles in SSH-aerosol-v1.3a: size resolution and redistribution
Oscar Jacquot and Karine Sartelet
Geosci. Model Dev., 18, 3965–3984, https://doi.org/10.5194/gmd-18-3965-2025,https://doi.org/10.5194/gmd-18-3965-2025, 2025
Short summary

Cited articles

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, https://doi.org/10.1029/JD092Id12p14681, 1987.
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.
Chrysos, G.: Intel® Xeon Phi coprocessor (codename Knights Corner), 2012 IEEE Hot Chips 24 Symposium (HCS), 27–29 August 2012, Cupertino, CA, USA, 1–31, 2012.
Feng, F., Wang, Z., Li, J., and Carmichael, G. R.: A nonnegativity preserved efficient algorithm for atmospheric chemical kinetic equations, Appl. Math. Comput., 271, 519–531, https://doi.org/10.1016/j.amc.2015.09.033, 2015.
Ge, B. Z., Wang, Z. F., Xu, X. B., Wu, J. B., Yu, X. L., and Li, J.: Wet deposition of acidifying substances in different regions of China and the rest of East Asia: Modeling with updated NAQPMS, Environ. Pollut., 187, 10–21, https://doi.org/10.1016/j.envpol.2013.12.014, 2014.
Short summary
We introduced some methods to port our Global Nested Air Quality Prediction Modeling System (GNAQPMS) model on Intel Knight Landing (KNL). In this paper, we introduced both common and specific methods to accelerate out model better. With the guidance of the resources material on Intel Websites (http://www.intel.com/content/www/us/en/products/processors/xeon-phi.html) and relative books, this paper could be an example for the model developers to take advantage of KNL for their model.
Share