Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 5.154 IF 5.154
  • IF 5-year value: 5.697 IF 5-year
    5.697
  • CiteScore value: 5.56 CiteScore
    5.56
  • SNIP value: 1.761 SNIP 1.761
  • IPP value: 5.30 IPP 5.30
  • SJR value: 3.164 SJR 3.164
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 59 Scimago H
    index 59
  • h5-index value: 49 h5-index 49
Volume 8, issue 9
Geosci. Model Dev., 8, 2815–2827, 2015
https://doi.org/10.5194/gmd-8-2815-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 8, 2815–2827, 2015
https://doi.org/10.5194/gmd-8-2815-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 09 Sep 2015

Development and technical paper | 09 Sep 2015

POM.gpu-v1.0: a GPU-based Princeton Ocean Model

S. Xu et al.
Viewed  
Total article views: 4,017 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,610 1,220 187 4,017 240 154 159
  • HTML: 2,610
  • PDF: 1,220
  • XML: 187
  • Total: 4,017
  • Supplement: 240
  • BibTeX: 154
  • EndNote: 159
Views and downloads (calculated since 17 Nov 2014)
Cumulative views and downloads (calculated since 17 Nov 2014)
Cited  
Saved (final revised paper)  
Saved (discussion paper)  
Discussed (final revised paper)  
No discussed metrics found.
Discussed (discussion paper)  
No discussed metrics found.
Latest update: 13 Nov 2019
Publications Copernicus
Download
Short summary
In this paper, we redesign the mpiPOM with GPUs. Specifically, we first convert the model from its original Fortran form to a new CUDA-C version, POM.gpu-v1.0. Then we optimize the code on each of the GPUs, the communications between the GPUs, and the I/O between the GPUs and the CPUs. We show that the performance of the new model on a workstation containing 4 GPUs is comparable to that on a powerful cluster with 408 standard CPU cores, and it reduces the energy consumption by a factor of 6.8.
In this paper, we redesign the mpiPOM with GPUs. Specifically, we first convert the model from...
Citation