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


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

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



Download Download (PDF)   View View   Source Source   



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.
Rating: 2.5/5. From 2 votes.
Please wait...

Recent source codes

* * *

* * *

HGPU group © 2010-2024 hgpu.org

All rights belong to the respective authors

Contact us: