WRF Physics Models Using GP-GPUs with CUDA Fortran

Kim, Youngtae; Lee, Yong Hee; Chung, Kwan-Young

doi:10.14191/atmos.2013.23.2.231

Cited by 2 publications

(3 citation statements)

References 5 publications

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“…Vanderbauwhede and Takemi [5] developed an OpenCL-based Weather Research and Forecasting (WRF) model, and the results showed that the computational execution time of the model using the GPU was reduced by two times compared to the case of the model using the CPU. Kim et al [10] developed a CUDA Fortran-based WRF model, and the result of this study also said that GPU can improve computation efficiency by five times compared to the CPU. Chang [11] applied CUDA to compressible flow cases based on high-order accuracy numerical techniques.…”

Section: Introductionmentioning

confidence: 65%

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GPU-Accelerated Laplace Equation Model Development Based on CUDA Fortran

Kim

Yoon

2021

Water

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In this study, a CUDA Fortran-based GPU-accelerated Laplace equation model was developed and applied to several cases. The Laplace equation is one of the equations that can physically analyze the groundwater flows, and is an equation that can provide analytical solutions. Such a numerical model requires a large amount of data to physically regenerate the flow with high accuracy, and requires computational time. These numerical models require a large amount of data to physically reproduce the flow with high accuracy and require computational time. As a way to shorten the computation time by applying CUDA technology, large-scale parallel computations were performed on the GPU, and a program was written to reduce the number of data transfers between the CPU and GPU. A GPU consists of many ALUs specialized in graphic processing, and can perform more concurrent computations than a CPU using multiple ALUs. The computation results of the GPU-accelerated model were compared with the analytical solution of the Laplace equation to verify the accuracy. The computation results of the GPU-accelerated Laplace equation model were in good agreement with the analytical solution. As the number of grids increased, the computational time of the GPU-accelerated model gradually reduced compared to the computational time of the CPU-based Laplace equation model. As a result, the computational time of the GPU-accelerated Laplace equation model was reduced by up to about 50 times.

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Section: Introductionmentioning

confidence: 65%

“…Therefore, Equation (8) becomes Equation (10). A more detailed description of Equation ( 9) is presented by Fletcher [12].…”

Section: Finite Volume Methodsmentioning

confidence: 99%

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GPU-Accelerated Laplace Equation Model Development Based on CUDA Fortran

Kim

Yoon

2021

Water

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Parallelization with load balancing of the weather scheme WSM7 for heterogeneous CPU-GPU platforms

Jakobs,

Klöckner,

Rünger

2024

J Supercomput

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This article provides an enhanced parallelization of the WSM7 microphysics scheme for the Weather Research and Forecasting Model (WRF). The parallelization is designed to maximize the utilization of a heterogeneous computing system consisting of CPUs, GPUs or both. Therefore the reference implementation of the WSM7 scheme is re-implemented for the heterogeneous execution model. For each time step, a dynamic load distribution is introduced which balances the computational load between the two components aiming for an overall minimum execution time. The evaluation of the parallelized implementation is done for a specific weather situation. Specifically, the precipitation of the low-pressure zone “Bernd” from July 2021 is simulated using an Intel Core i7-7700 CPU and a NVIDIA GTX 1070 GPU. The results show a speedup of up to 28.51 for the GPU version in comparison with the reference implementation. The heterogeneous dynamic load balancing increases the speedup achieved even further by introducing a distribution factor that is updated for each time step.

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WRF Physics Models Using GP-GPUs with CUDA Fortran

Cited by 2 publications

References 5 publications

GPU-Accelerated Laplace Equation Model Development Based on CUDA Fortran

GPU-Accelerated Laplace Equation Model Development Based on CUDA Fortran

Parallelization with load balancing of the weather scheme WSM7 for heterogeneous CPU-GPU platforms

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