Improving Parallel Performance of a Finite-Difference AGCM on Modern High-Performance Computers

Li Liu * Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing, China

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Ruizhe Li Department of Computer Science and Technology, Tsinghua University, Beijing, China

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Guangwen Yang Department of Computer Science and Technology, and Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing, China

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Bin Wang Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, and State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Lijuan Li State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Ye Pu State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Abstract

The rapid development of science and technology has enabled finer and finer resolutions in atmospheric general circulation models (AGCMs). Parallelization becomes progressively more critical as the resolution of AGCMs increases. This paper presents a new parallel version of the finite-difference Gridpoint Atmospheric Model of the Institute of Atmospheric Physics (IAP)–State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG; GAMIL) with various parallel optimization strategies, including two-dimensional hybrid parallel decomposition; hybrid parallel programming; parallel communications for coupling the physical packages, land surface, and dynamical core; and a cascading solution to the tridiagonal equations used in the dynamical core. The new parallel version under two different horizontal resolutions (1° and 0.25°) is evaluated. The new parallel version enables GAMIL to achieve higher parallel efficiency and utilize a greater number of CPU cores. GAMIL1° achieves 37.8% parallel efficiency using 960 CPU cores, while GAMIL0.25° achieves 57.5% parallel efficiency.

Corresponding author address: Dr. Li Liu, Center for Earth System Science, Tsinghua University, Meng Minwei Science and Technology Building, Room S-817, Beijing 100084, China. E-mail: liuli-cess@tsinghua.edu.cn

Abstract

The rapid development of science and technology has enabled finer and finer resolutions in atmospheric general circulation models (AGCMs). Parallelization becomes progressively more critical as the resolution of AGCMs increases. This paper presents a new parallel version of the finite-difference Gridpoint Atmospheric Model of the Institute of Atmospheric Physics (IAP)–State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG; GAMIL) with various parallel optimization strategies, including two-dimensional hybrid parallel decomposition; hybrid parallel programming; parallel communications for coupling the physical packages, land surface, and dynamical core; and a cascading solution to the tridiagonal equations used in the dynamical core. The new parallel version under two different horizontal resolutions (1° and 0.25°) is evaluated. The new parallel version enables GAMIL to achieve higher parallel efficiency and utilize a greater number of CPU cores. GAMIL1° achieves 37.8% parallel efficiency using 960 CPU cores, while GAMIL0.25° achieves 57.5% parallel efficiency.

Corresponding author address: Dr. Li Liu, Center for Earth System Science, Tsinghua University, Meng Minwei Science and Technology Building, Room S-817, Beijing 100084, China. E-mail: liuli-cess@tsinghua.edu.cn
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  • Bellenger, H., Guilyardi E. , Leloup J. , Lengaigne M. , and Vialard J. , 2013: ENSO representation in climate models: From CMIP3 to CMIP5. Climate Dyn., 42, 1999–2018, doi:10.1007/s00382-013-1783-z.

    • Search Google Scholar
    • Export Citation
  • Dennis, J., and Coauthors, 2012: CAM-SE: A scalable spectral element dynamical core for the Community Atmosphere Model. Int. J. High-Perform. Comput. Appl.,26, 74–89, doi:10.1177/1094342011428142.

  • Dong, L., Li L.-J. , Huang W.-Y. , Wang Y. , and Wang B. , 2012: Preliminary evaluation of the cloud fraction simulations by GAMIL2 using COSP. Atmos.Oceanic Sci. Lett., 5, 258263.

    • Search Google Scholar
    • Export Citation
  • Guo, Z., Wu C. , Zhou T. , and Wu T. , 2011: A comparison of cloud radiative forcings simulated by LASG/IAP and BCC atmospheric general circulation models (in Chinese). Chin. J. Atmos. Sci., 35, 739752.

    • Search Google Scholar
    • Export Citation
  • Hodson, D. L. R., Sutton R. T. , Cassou C. , Keenlyside N. , Okumura Y. , and Zhou T. , 2010: Climate impacts of recent multidecadal changes in Atlantic Ocean sea surface temperature: A multimodel comparison. Climate Dyn., 34, 10411058, doi:10.1007/s00382-009-0571-2.

    • Search Google Scholar
    • Export Citation
  • Khairoutdinov, M. F., and Randall D. A. , 2001: A cloud resolving model as a cloud parameterization in the NCAR Community Climate System Model: Preliminary results. Geophys. Res. Lett., 28, 36173620, doi:10.1029/2001GL013552.

    • Search Google Scholar
    • Export Citation
  • Kuang, X. Y., Zhang Y. C. , Liu J. , and Guo L. L. , 2009: A numerical study of the effect of anomalous surface heating in the Kuroshio current region in winter on the East Asian subtropical westerly jet (in Chinese). Chin. J. Atmos. Sci., 33, 8189.

    • Search Google Scholar
    • Export Citation
  • Kucharski, F., and Coauthors, 2009: The CLIVAR C20C project: Skill of simulating Indian monsoon rainfall on interannual to decadal timescales. Does GHG forcing play a role? Climate Dyn., 33, 615627, doi:10.1007/s00382-008-0462-y.

    • Search Google Scholar
    • Export Citation
  • Lauritzen, P. H., Jablonowski C. , Taylor M. A. , and Nair R. D. , Eds., 2011: Numerical Techniques for Global Atmospheric Models. Lecture Notes in Computational Science and Engineering, Vol. 80, Springer, 556 pp.

  • Li, L. J., and Wang B. , 2010: Influences of two convective schemes on the radiative energy budget in GAMIL1.0. Acta Meteor. Sin., 24, 318327.

    • Search Google Scholar
    • Export Citation
  • Li, L. J., Wang B. , Wang Y. Q. , and Wan H. , 2007a: Improvements in climate simulation with modifications to the Tiedtke convective parameterization in the Grid-point Atmospheric Model of IAP LASG (GAMIL). Adv. Atmos. Sci., 24, 323335, doi:10.1007/s00376-007-0323-3.

    • Search Google Scholar
    • Export Citation
  • Li, L. J., Wang B. , and Zhou T. J. , 2007b: Impacts of external forcing on the 20th century global warming. Chin. Sci. Bull., 52, 31483154, doi:10.1007/s11434-007-0463-y.

    • Search Google Scholar
    • Export Citation
  • Li, L. J., and Coauthors, 2013a: Evaluation of grid-point atmospheric model of IAP LASG version 2 (GAMIL2). Adv. Atmos. Sci., 30, 855867, doi:10.1007/s00376-013-2157-5.

    • Search Google Scholar
    • Export Citation
  • Li, L. J., and Coauthors, 2013b: The Flexible Global Ocean-Atmosphere-Land System Model: Grid-point version 2: FGOALS-g2. Adv. Atmos. Sci., 30, 543560, doi:10.1007/s00376-012-2140-6.

    • Search Google Scholar
    • Export Citation
  • Lin, J. L., 2007: The double-ITCZ problem in IPCC AR4 coupled GCMs: Ocean–atmosphere feedback analysis. J. Climate, 20, 44974525, doi:10.1175/JCLI4272.1.

    • Search Google Scholar
    • Export Citation
  • Mao, J.-Y., and Li L.-J. , 2012: An assessment of MJO and tropical waves simulated by different versions of the GAMIL model. Atmos. Oceanic Sci. Lett., 5, 2631.

    • Search Google Scholar
    • Export Citation
  • Michalakes, J., Dudhia J. , Gill D. , Henderson T. B. , Klemp J. B. , Skamarock W. , and Wang W. , 2004: The Weather Research and Forecast Model: Software architecture and performance. Proc. 11th Workshop on the Use of High Performance Computing in Meteorology, Reading, United Kingdom, ECMWF, 156168.

  • Press, W. H., Teukolsky S. A. , Vetterling W. T. , and Flannery B. P. , 2007: Numerical Recipes: The Art of Scientific Computing. 3rd ed. Cambridge University Press, 1256 pp.

  • Sato, T., 2004: The earth simulator: Roles and impacts. Parallel Comput., 30, 12791286, doi:10.1016/j.parco.2004.09.003.

  • Scaife, A. A., and Coauthors, 2009: The CLIVAR C20C project: Selected twentieth century climate events. Climate Dyn., 33, 603–614, doi:10.1007/s00382-008-0451-1.

    • Search Google Scholar
    • Export Citation
  • Schaffer, D. S., and Suarez M. J. , 2000: Design and performance analysis of a massively parallel atmospheric general circulation model. Sci. Comput., 8, 4957.

    • Search Google Scholar
    • Export Citation
  • Thomas, L. H., 1949: Elliptic problems in linear differential equations over a network. Watson Scientific Computing Laboratory Rep., Columbia University.

  • Vertenstein, M., Oleson K. , Levis S. , and Hoffman F. , 2004: Community Land Model Version 3.0 (CLM3.0) user’s guide. NCAR, 38 pp. [Available online at http://www.cgd.ucar.edu/tss/clm/distribution/clm3.0/UsersGuide/UsersGuide.pdf.]

  • Wang, B., Wan H. , Ji Z. , Zhang X. , Yu R. , Yu Y. , and Liu H. , 2004: Design of a new dynamical core for global atmospheric models based on some efficient numerical methods. Sci. China,47A, 421, doi:10.1360/04za0001.

    • Search Google Scholar
    • Export Citation
  • Wu, Z. W., and Li J. , 2008: Prediction of the Asian-Australian monsoon interannual variations with the grid-point atmospheric model of IAP LASG (GAMIL). Adv. Atmos. Sci., 25, 387394, doi:10.1007/s00376-008-0387-8.

    • Search Google Scholar
    • Export Citation
  • Xie, X., Wang B. , Li L.-J. , and Dong L. , 2012: MJO simulations by GAMIL1.0 and GAMIL2.0. Atmos. Oceanic Sci. Lett., 5, 4854.

  • Yan, L., Wang P. X. , Yu Y. Q. , Li L. J. , and Wang B. , 2010: Potential predictability of sea surface temperature in a coupled ocean–atmosphere GCM. Adv. Atmos. Sci., 27, 921936, doi:10.1007/s00376-009-9062-y.

    • Search Google Scholar
    • Export Citation
  • Yu, R. C., 1994: A two-step shape-preserving advection scheme. Adv. Atmos. Sci., 11, 479–490, doi:10.1007/BF02658169.

  • Yu, Y., and Sun D. Z. , 2009: Response of ENSO and the mean state of the tropical Pacific to extratropical cooling and warming: A study using the IAP coupled model. J. Climate, 22, 59025917, doi:10.1175/2009JCLI2902.1.

    • Search Google Scholar
    • Export Citation
  • Zheng, W. P., and Yu Y. Q. , 2009: The Asian monsoon system of the middle Holocene simulated by a coupled GCM. Quat. Sci., 29, 11351145.

    • Search Google Scholar
    • Export Citation
  • Zou, L. W., Zhou T. J. , Wu B. , Chen H. M. , and Li L. J. , 2009: The interannual variability of summertime western Pacific subtropical high hindcasted by GAMIL CliPAS experiments (in Chinese). Chin. J. Atmos. Sci., 33, 959970.

    • Search Google Scholar
    • Export Citation
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