Articles | Volume 8, issue 9
https://doi.org/10.5194/gmd-8-2815-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-8-2815-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
POM.gpu-v1.0: a GPU-based Princeton Ocean Model
S. Xu
Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, 100084, and Joint Center for Global Change Studies, Beijing, 100875, China
Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, 100084, and Joint Center for Global Change Studies, Beijing, 100875, China
L.-Y. Oey
Institute of Hydrological & Oceanic Sciences, National Central University, Jhongli, Taiwan
Program in Atmospheric & Oceanic Sciences, Princeton University, Princeton, New Jersey, USA
Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, 100084, and Joint Center for Global Change Studies, Beijing, 100875, China
H. Fu
Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, 100084, and Joint Center for Global Change Studies, Beijing, 100875, China
Y. Zhang
Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, 100084, and Joint Center for Global Change Studies, Beijing, 100875, China
G. Yang
Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, 100084, and Joint Center for Global Change Studies, Beijing, 100875, China
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- LICOM3-CUDA: a GPU version of LASG/IAP climate system ocean model version 3 based on CUDA J. Wei et al. 10.1007/s11227-022-05020-2
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- Typhoon Case Comparison Analysis Between Heterogeneous Many-Core and Homogenous Multicore Supercomputing Platforms X. Liu et al. 10.1007/s11802-023-5152-1
- Towards a real-time modeling of global ocean waves by the fully GPU-accelerated spectral wave model WAM6-GPU v1.0 Y. Yuan et al. 10.5194/gmd-17-6123-2024
- Solving global shallow water equations on heterogeneous supercomputers H. Fu et al. 10.1371/journal.pone.0172583
24 citations as recorded by crossref.
- Passive Tracer Transport in Ocean Modeling: Implementation on GPUs, Efficiency and Optimizations E. Gaschuk et al. 10.1134/S1995080223080152
- Computational Benefit of GPU Optimization for the Atmospheric Chemistry Modeling J. Sun et al. 10.1029/2018MS001276
- The GPU version of LASG/IAP Climate System Ocean Model version 3 (LICOM3) under the heterogeneous-compute interface for portability (HIP) framework and its large-scale application P. Wang et al. 10.5194/gmd-14-2781-2021
- Optimizing high-resolution Community Earth System Model on a heterogeneous many-core supercomputing platform S. Zhang et al. 10.5194/gmd-13-4809-2020
- swNEMO_v4.0: an ocean model based on NEMO4 for the new-generation Sunway supercomputer Y. Ye et al. 10.5194/gmd-15-5739-2022
- Accelerating the Lagrangian particle tracking of residence time distributions and source water mixing towards large scales C. Yang et al. 10.1016/j.cageo.2021.104760
- The FastEddy® Resident‐GPU Accelerated Large‐Eddy Simulation Framework: Model Formulation, Dynamical‐Core Validation and Performance Benchmarks J. Sauer & D. Muñoz‐Esparza 10.1029/2020MS002100
- A Barotropic Solver for High-Resolution Ocean General Circulation Models X. Yang et al. 10.3390/jmse9040421
- Porting the WAVEWATCH III (v6.07) wave action source terms to GPU O. Ikuyajolu et al. 10.5194/gmd-16-1445-2023
- Seasonal Exchanges of the Kuroshio and Shelf Waters and Their Impacts on the Shelf Currents of the East China Sea J. Wang & L. Oey 10.1175/JPO-D-15-0183.1
- OpenArray v1.0: a simple operator library for the decoupling of ocean modeling and parallel computing X. Huang et al. 10.5194/gmd-12-4729-2019
- A GPU accelerated finite volume coastal ocean model X. Zhao et al. 10.1016/S1001-6058(16)60780-1
- GPU-HADVPPM V1.0: a high-efficiency parallel GPU design of the piecewise parabolic method (PPM) for horizontal advection in an air quality model (CAMx V6.10) K. Cao et al. 10.5194/gmd-16-4367-2023
- Comparing the Performance of Julia on CPUs versus GPUs and Julia-MPI versus Fortran-MPI: a case study with MPAS-Ocean (Version 7.1) S. Bishnu et al. 10.5194/gmd-16-5539-2023
- Characterizing uncertainties of Earth system modeling with heterogeneous many-core architecture computing Y. Yu et al. 10.5194/gmd-15-6695-2022
- Comparison of an Explicit and Implicit Time Integration Method on GPUs for Shallow Water Flows on Structured Grids F. Buwalda et al. 10.3390/w15061165
- LICOM3-CUDA: a GPU version of LASG/IAP climate system ocean model version 3 based on CUDA J. Wei et al. 10.1007/s11227-022-05020-2
- GNAQPMS v1.1: accelerating the Global Nested Air Quality Prediction Modeling System (GNAQPMS) on Intel Xeon Phi processors H. Wang et al. 10.5194/gmd-10-2891-2017
- A high-precision hydrodynamic model coupled with the hydrological habitat suitability model to reveal estuarine vegetation distribution Q. Ma et al. 10.1007/s42241-022-0043-2
- MP CBM-Z V1.0: design for a new Carbon Bond Mechanism Z (CBM-Z) gas-phase chemical mechanism architecture for next-generation processors H. Wang et al. 10.5194/gmd-12-749-2019
- Porting LASG/ IAP Climate System Ocean Model to Gpus Using OpenAcc J. Jiang et al. 10.1109/ACCESS.2019.2932443
- GPU Acceleration of a Non-hydrostatic Ocean Model with a Multigrid Poisson/Helmholtz solver T. Yamagishi & Y. Matsumura 10.1016/j.procs.2016.05.502
- Typhoon Case Comparison Analysis Between Heterogeneous Many-Core and Homogenous Multicore Supercomputing Platforms X. Liu et al. 10.1007/s11802-023-5152-1
- Towards a real-time modeling of global ocean waves by the fully GPU-accelerated spectral wave model WAM6-GPU v1.0 Y. Yuan et al. 10.5194/gmd-17-6123-2024
1 citations as recorded by crossref.
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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...