To compile and run this project, there are very few requirements which you can easily install using your package manager on Linux or MacOS:

git for external requirements and to clone this repository.
CMake for the build systems of DiFfRG, deal.ii and other libraries.
GNU Make or another generator of your choice.
A compiler supporting at least the C++20 standard. This project is only tested using the GCC compiler suite, as well as with AppleClang, but in principle, ICC or standard Clang should also work.
LAPACK and BLAS in some form, e.g. OpenBlas.
The GNU Scientific Library GSL. If not found by DiFfRG, it will try to install it by itself.
Doxygen and graphviz to build the documentation.

The following requirements are optional:

Python is used in the library for visualization purposes. Furthermore, adaptive phase diagram calculation is implemented as a python routine.
ParaView, a program to visualize and post-process the vtk data saved by DiFfRG when treating FEM discretizations.
CUDA for integration routines on the GPU, which gives a huge speedup for the calculation of fully momentum dependent flow equations (10 - 100x). In case you wish to use CUDA, make sure you have a compiler available on your system compatible with your version of nvcc, e.g. g++<=13.2 for CUDA 12.5

All other requirements are bundled and automatically built with DiFfRG. The framework has been tested with the following systems:

Arch Linux

$ pacman -S git cmake gcc blas-openblas blas64-openblas paraview python doxygen cuda graphviz gsl

For a CUDA-enabled build, additionally install

$ pacman -S cuda

Rocky Linux

$ dnf --enablerepo=devel install -y gcc-toolset-12 cmake git openblas-devel doxygen doxygen-latex python3 python3-pip gsl-devel patch

$ scl enable gcc-toolkit-12 bash

The second line is necessary to switch into a shell where g++-12 is available

Ubuntu

$ apt-get update

$ apt-get install git cmake libopenblas-dev paraview build-essential python3 doxygen libeigen3-dev graphviz libgsl-dev

MacOS

First, install xcode and homebrew, then run

$ brew install cmake doxygen paraview eigen graphviz gsl

Windows

If using Windows, instead of running the project directly, it is recommended to use WSL and then go through the installation as if on Linux (e.g. Arch or Ubuntu).

Docker and other container runtime environments

Although a native install should be unproblematic in most cases, the setup with CUDA functionality may be daunting. Especially on high-performance clusters, and also depending on the packages available for chosen distribution, it may be much easier to work with the framework inside a container.

The specific choice of runtime environment is up to the user, however we provide a small build script to create docker container in which DiFfRG will be built. To do this, you will need docker, docker-buildx and the NVIDIA container toolkit in case you wish to create a CUDA-compatible image.

For a CUDA-enabled build, run

$ bash setup_docker.sh -c 12.5.1 -j8

in the above, you may want to replace the version 12.5.1 with another version you can find on docker hub at nvidia/cuda. Alternatively, for a CUDA-less build, run simply

$ bash setup_docker.sh -j8

If using other environments, e.g. ENROOT, the preferred approach is simply to build an image on top of the CUDA images by NVIDIA. Optimal compatibility is given using nvidia/cuda:12.5.1-devel-rockylinux. Proceed with the installation setup for Rocky Linux above.

For example, with ENROOT a DiFfRG image can be built by following these steps:

$ enroot import docker://nvidia/cuda:12.5.1-devel-rockylinux9
$ enroot create --name DiFfRG nvidia+cuda+12.5.1-devel-rockylinux9.sqsh
$ enroot start --root --rw -m ./:/DiFfRG_source DiFfRG bash

Afterwards, one proceeds with the above Rocky Linux setup.

Setup

If all requirements are met, you can clone the git to a directory of your choice,

$ git clone https://github.com/satfra/DiFfRG.git

and start the build after switching to the git directory.

$ cd DiFfRG

$ bash -i build.sh -j8 -i /opt/DiFfRG

The build_DiFfRG.sh bash script will build and setup the DiFfRG project and all its requirements. This can take up to half an hour as the deal.ii library is quite large. This script has the following options:

-c Use CUDA when building the DiFfRG library.
-i <directory> Set the installation directory for the library.
-j <threads> Set the number of threads passed to make and git fetch.
--help Display this information.

Depending on your amount of CPU cores, you should adjust the -j parameter which indicates the number of threads used in the build process. Note that choosing this too large may lead to extreme RAM usage, so tread carefully.

As soon as the build has finished, you can find the build folder in the DiFfRG_build subfolder and a full install of the library in /opt/DiFfRG.

If you have changes to the library code, you can always update the library by running

$ bash -i update_DiFfRG.sh -clm -j8 -i /opt/DiFfRG

where once again the -j parameter should be adjusted to your amount of CPU cores. The update_DiFfRG.sh script takes the following optional arguments:

-c Use CUDA when building the DiFfRG library.
-i <directory> Set the installation directory for the library.
-j <threads> Set the number of threads passed to make and git fetch.
-m Install the Mathematica package locally.
--help Display this information.