10986

Highly Optimized Full GPU-Acceleration of Non-hydrostatic Weather Model SCALE-LES

Mohamed Wahib, Naoya Maruyama
RIKEN Advanced Institute for Computational Science, Kobe, Japan
IEEE Cluster 2013

@article{wahib2014highly,

   author={Wahib, Mohamed and Maruyama, Naoya},

   title={Highly Optimized Full GPU-Acceleration of Non-hydrostatic Weather Model SCALE-LES},

   year={2013}

}

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SCALE-LES is a non-hydrostatic weather model developed at RIKEN, Japan. It is intended to be a global high- resolution model that would be scaled to exascale systems. This paper introduces the full GPU acceleration of all SCALE-LES modules. Moreover, the paper demonstrates the strategies to handle the unique challenges of accelerating SCALE-LES using GPU. The proposed acceleration is important for identifying the expectations and requirements of scaling SCALE-LES, and similar real world applications, into the exascale era. The GPU implementation includes the optimized GPU acceleration of SCALE-LES for a single GPU with both CUDA Fortran and OpenACC. It also includes scaling SCALE-LES for GPU- accelerated clusters. The results and analysis show how the optimization strategies affect the performance gain in SCALE- LES when moving from conventional CPU clusters towards GPU- powered clusters.
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Highly Optimized Full GPU-Acceleration of Non-hydrostatic Weather Model SCALE-LES, 5.0 out of 5 based on 2 ratings

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