Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 10, issue 8
Articles | Volume 10, issue 8
https://doi.org/10.5194/gmd-10-2891-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-10-2891-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Articles | Volume 10, issue 8
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

Viewed

Total article views: 3,743 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,577 958 208 3,743 233 127 150
  • HTML: 2,577
  • PDF: 958
  • XML: 208
  • Total: 3,743
  • Supplement: 233
  • BibTeX: 127
  • EndNote: 150
Views and downloads (calculated since 22 Feb 2017)
Cumulative views and downloads (calculated since 22 Feb 2017)

Viewed (geographical distribution)

Total article views: 3,743 (including HTML, PDF, and XML) Thereof 3,504 with geography defined and 239 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 16 Jul 2024
Download
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.
Read more