Solano Quinde, Lizandro DamiánGualan Saavedra, Ronald MarceloZúñiga Prieto, Miguel Ángel2018-01-112018-01-112016-03-129781450319089https://www.scopus.com/inward/record.uri?eid=2-s2.0-84968754875&doi=10.1145%2f2883404.2883407&partnerID=40&md5=a8d9bd2ae14b01d50bb0d6805a9ec7f4http://dspace.ucuenca.edu.ec/handle/123456789/29086The Weather Research and Forecasting (WRF), a next generation mesoscale numerical weather prediction system, has a considerable amount of work regarding GPU acceleration. However, the amount of works exploiting multi-GPU sys- tems is limited. This work constitutes an effort on using GPU computing over the WRF model and is focused on a computationally intensive portion of the WRF: the Horizontal Diffusion method. Particularly, this work presents the enhancements that enable a single-GPU based implementation to exploit the parallelism of multi-GPU systems. The performance of the multi-GPU and single-GPU based implementations are compared on a computational domain of 433x308 horizontal grid points with 35 vertical levels, and the resulting speedup of the kernel is 3.5x relative to one GPU. The experiments were carried out on a multi-core computer with two NVIDIA Tesla K40m GPUs.en-USCompute Unified Device Archi- Tecture (Cuda)Dynamic SolverGpgpuGraphics Processing Unit (Gpu)Horizon- Tal Diffusion MethodMulti-Gpu ImplementationNvidia Tesla K40m GpuWeather Research And Forecasting (Wrf) ModelArticle10.1145/2883404.2883407