babrodtk/gpu-ocean

GPU Ocean OpenCL/C++ codebase

gpu-ocean

GPU Ocean codebase.

Installation

In order to run this code, you need to have access to a CUDA enabled GPU, with CUDA toolkit and appropriate drivers installed. If you are on Windows, you also need to have installed Visual Studios and add the path to its bin folder in PATH. This is so that pycuda can find a C++ compiler.

We recommend that you set up your python environment using Conda as follows:

• Install miniconda (which is a minimal subset of Anaconda)
• Install jupyter notebook (unless you already have it installed on your system) by opening a terminal (or Anaconda prompt if on Windows) and type

  conda install -c conda-forge jupyter
  

• Install the conda extensions that allows jupyter notebook to select conda environments as kernels:

  conda install -c conda-forge nb_conda_kernels
  

• Create a new conda environment according to the environment file in this repository

  conda env create -f conda_environment.yml
  

You should now be able to start a jupyter notebook server, open one of our notebooks, select the conda environment 'gpuocean' as kernel, and run the code.

Have fun!

Download all data files

cd <project root directory>
wget -r -np -nH -R "index.html*" http://gpu-ocean.met.no:9000/gpu_ocean