Articles | Volume 4, issue 3
Geosci. Model Dev., 4, 835–844, 2011

Special issue: The FAMOUS climate model

Geosci. Model Dev., 4, 835–844, 2011

Development and technical paper 27 Sep 2011

Development and technical paper | 27 Sep 2011

FAMOUS, faster: using parallel computing techniques to accelerate the FAMOUS/HadCM3 climate model with a focus on the radiative transfer algorithm

P. Hanappe1, A. Beurivé1, F. Laguzet1,*, L. Steels1, N. Bellouin2, O. Boucher2,**, Y. H. Yamazaki3,***, T. Aina3, and M. Allen3 P. Hanappe et al.
  • 1Sony Computer Science Laboratory, Paris, France
  • 2Met Office, Exeter, UK
  • 3University of Oxford, Oxford, UK
  • *now at: Laboratoire de Recherche en Informatique, Orsay, France
  • **now at: Laboratoire de Météorologie Dynamiqe, IPSL, CNRS/UPMC, Paris, France
  • ***now at: School of Geography, Politics and Sociology, Newcastle University, Newcastle, UK

Abstract. We have optimised the atmospheric radiation algorithm of the FAMOUS climate model on several hardware platforms. The optimisation involved translating the Fortran code to C and restructuring the algorithm around the computation of a single air column. Instead of the existing MPI-based domain decomposition, we used a task queue and a thread pool to schedule the computation of individual columns on the available processors. Finally, four air columns are packed together in a single data structure and computed simultaneously using Single Instruction Multiple Data operations.

The modified algorithm runs more than 50 times faster on the CELL's Synergistic Processing Element than on its main PowerPC processing element. On Intel-compatible processors, the new radiation code runs 4 times faster. On the tested graphics processor, using OpenCL, we find a speed-up of more than 2.5 times as compared to the original code on the main CPU. Because the radiation code takes more than 60 % of the total CPU time, FAMOUS executes more than twice as fast. Our version of the algorithm returns bit-wise identical results, which demonstrates the robustness of our approach. We estimate that this project required around two and a half man-years of work.