Articles | Volume 10, issue 8
Geosci. Model Dev., 10, 2891–2904, 2017
https://doi.org/10.5194/gmd-10-2891-2017
Geosci. Model Dev., 10, 2891–2904, 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 et al.

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Zifa Wang on behalf of the Authors (15 Jun 2017)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (23 Jun 2017) by Paul Ullrich
ED: Publish as is (06 Jul 2017) by Paul Ullrich
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.