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Getting Started
===============
Contributing
------------
Contributing new features, bug fixes
````````````````````````````````````
Any contribution is welcome, we are trying to follow a `gitflow <https://nvie.com/posts/a-successful-git-branching-model/>`_ workflow, so the project `developers` can create branches named `features/<name of my feature>` or `bugfixes/<name of the fix>` directly in the main `akantu` repository.
External fellows can `Fork <https://gitlab.com/akantu/akantu/-/forks/new>`_ the project.
In both cases the modifications have to be submitted in the form of a `Merge Request <https://gitlab.com/akantu/akantu/-/merge_requests/new>`_.
Asking for help, reporting issues
`````````````````````````````````
If you want to ask for help concerning Akantu's compilation, usage or problem with the code do not hesitate to open an `Issue <https://gitlab.com/akantu/akantu/-/issues/new>`_ on gitlab. If you want to contribute and don't know where to start, you are also invited to open an issue.
Building ``Akantu``
--------------------
Dependencies
````````````
In order to compile ``Akantu`` any compiler supporting fully C++14 should work.
In addition some libraries are required:
- CMake (>= 3.5.1)
- Boost (pre-processor and Spirit)
- Eigen3 (if not present the build system will try to download it)
For the python interface:
- Python (>=3 is recommended)
- pybind11 (if not present the build system will try to download it)
To run parallel simulations:
- MPI
- Scotch
To use the static or implicit dynamic solvers at least one of the following libraries is needed:
- MUMPS (since this is usually compiled in static you also need MUMPS dependencies)
- PETSc
To compile the tests and examples:
- Gmsh
- google-test (if not present the build system will try to download it)
On ``.deb`` based Linux systems
"""""""""""""""""""""""""""""""
.. code-block:: bash
> sudo apt install cmake libboost-dev gmsh libeigen3-dev
# For parallel
> sudo apt install mpi-default-dev libmumps-dev libscotch-dev
# For sequential
> sudo apt install libmumps-seq-dev
Using ``conda``
"""""""""""""""
This works only for sequential computation since `mumps` from conda-forge is compiled without MPI support:
.. code-block:: bash
> conda create -n akantu
> conda activate akantu
> conda install boost cmake
> conda install -c conda-forge mumps
Using ``homebrew``
""""""""""""""""""
.. code-block:: bash
> brew install gcc
> brew install boost@1.76
> brew tap brewsci/num
> brew install brewsci-mumps --without-brewsci-parmetis
If it does not work you can edit url to http://graal.ens-lyon.fr/MUMPS/MUMPS_5.3.5.tar.gz using the command:
.. code-block:: bash
> brew edit brewsci-mumps
Configuring and compilation
```````````````````````````
`Akantu` is a `CMake <https://cmake.org/>`_ project, so to configure it, you can follow the usual way:
.. code-block:: bash
> cd akantu
> mkdir build
> cd build
> ccmake ..
[ Set the options that you need ]
> make
> make install
On Mac OS X with ``homebrew``
"""""""""""""""""""""""""""""
You will need to specify the compiler explicitly
.. code-block:: bash
> CC=gcc-12 CXX=g++-12 FC=gfortran-12 cmake ..
Considering that `homebrew` is installed in ``/opt/homebrew``
Define the location of the ``Scotch`` library path:
.. code-block:: bash
> cmake .. -DSCOTCH_LIBRARY="/opt/homebrew/lib/libscotch.dylib;/opt/homebrew/lib/libscotcherr.dylib;/opt/homebrew/lib/libscotcherrexit.dylib"
Specify path to all ``MUMPS`` libraries:
.. code-block:: bash
> cmake .. -DMUMPS_DIR=/opt/homebrew/opt/brewsci-mumps
In case the above does not work, specify the ``MUMPS`` path manually using (e.g.):
.. code-block:: bash
> cmake .. -DMUMPS_LIBRARY_COMMON=/opt/homebrew/opt/brewsci-mumps/lib/libmumps_common.dylib
If compilation does not work change the path of the failing libraries to brew downloads in `/opt/homebrew/`.
Using the python interface
--------------------------
You can install ``Akantu`` using pip, this will install a pre-compiled version, this works only on Linux machines for now::
> pip install akantu
You can then import the package in a python script as::
import akantu
The python API is similar to the C++ one, see :ref:`reference` . If you encouter any problem with the python interface, you are welcome to do a merge request or post an issue on `GitLab <https://gitlab.com/akantu/akantu/-/issues>`_ .
Examples and Tutorials with the python interface
````````````````````````````````````````````````
To help getting started, you can find examples with the source code in the
`examples` sub-folder. If you just want to test the python examples without
having to compile the whole project you can use the following tarball
`akantu-python-examples.tgz
<https://gitlab.com/akantu/akantu/-/packages/22034181>`_.
In addition to the examples, multiple tutorials using the python interface are
available as notebooks with pre-installed version of `Akantu` on `Renku`. The
tutorials can be tested here: |renku|
.. |renku| image:: https://user-content.gitlab-static.net/52a4794df1236b248c8fc870bd74e9d787c0e2cb/68747470733a2f2f72656e6b756c61622e696f2f72656e6b752d62616467652e737667
:target: https://renkulab.io/projects/guillaume.anciaux/akantu-tutorials/sessions/new?autostart=1
Writing a ``main`` function
---------------------------
``Akantu`` first needs to be initialized. The memory management included in the
core library handles the correct allocation and de-allocation of vectors,
structures and/or objects. Moreover, in parallel computations, the
initialization procedure performs the communication setup. This is achieved by
the function :cpp:func:`initialize <akantu::initialize>` that is used as
follows::
#include "aka_common.hh"
#include "..."
using namespace akantu;
int main(int argc, char *argv[]) {
initialize("input_file.dat", argc, argv);
// your code ...
}
The :cpp:func:`initialize <akantu::initialize>` function takes the text input
file and the program parameters which can be parsed by ``Akantu`` in due form
(see sect:parser). Obviously it is necessary to include all files needed in
main. In this manual, all provided code implies the usage of ``akantu`` as
namespace.
Compiling your simulation
-------------------------
The easiest way to compile your simulation is to create a ``cmake`` project by
putting all your code in some directory of your choosing. Then, make sure that
you have ``cmake`` installed and create a ``CMakeLists.txt`` file. An example of
a minimal ``CMakeLists.txt`` file would look like this:
.. code-block:: cmake
cmake_minimum_required(VERSION 3.12.0)
project(my_simu)
find_package(Akantu REQUIRED)
add_akantu_simulation(my_simu my_simu.cc)
Then create a directory called ``build`` and inside it execute ``cmake
-DAkantu_DIR=<path_to_akantu> -DCMAKE_BUILD_TYPE=RelWithDebInfo ..``. If you
installed ``Akantu`` in a standard directory such as ``/usr/local`` (using
``make install``), you can omit the ``-DAkantu_DIR=<path_to_akantu>`` option.
Otherwise ``path_to_akantu`` is either the folder where you built ``Akantu`` if
you did not do a ``make install``, or if you installed ``Akantu`` in
``CMAKE_INSTALL_PREFIX`` it is ``<CMAKE_INSTALL_PREFIX>/share/cmake/Akantu``.
Once ``cmake`` managed to configure and generate a ``makefile`` you can just do
``make``.
.. _loading_mesh:
Creating and Loading a Mesh
---------------------------
In its current state, ``Akantu`` supports three types of meshes: Gmsh, Abaqus and
Diana. Once a :cpp:class:`akantu::Mesh` object is created with a given spatial
dimension, it can be filled by reading a mesh input file. The method
:cpp:func:`read <akantu::Mesh::read>` of the class :cpp:class:`Mesh
<akantu::Mesh>` infers the mesh type from the file extension. If a non-standard
file extension is used, the mesh type has to be specified.
.. code-block:: c++
Int spatial_dimension = 2;
Mesh mesh(spatial_dimension);
// Reading Gmsh files
mesh.read("my_gmsh_mesh.msh");
mesh.read("my_gmsh_mesh", _miot_gmsh);
The Gmsh reader adds the geometrical and physical tags as mesh data. The
physical values are stored as a :cpp:type:`Int <akantu::Int>` data called
``tag_0``, if a string name is provided it is stored as a ``std::string`` data
named ``physical_names``. The geometrical tag is stored as a :cpp:type:`Int
<akantu::Int>` data named ``tag_1``.
Running parallel simulation
---------------------------
In order to run distributed memory simulation a few extra steps have to be taken.
The mesh as to be distributed
.. code-block:: c++
const auto & comm = Communicator::getStaticCommunicator();
if (comm.whoAmI() == 0) { // MPI rank
// Read the mesh
mesh.read("square_2d.msh");
}
mesh.distribute();
All the communications and the distribution of the mesh and associated data will
be taken care automatically.
Currently the mesh decomposition is handled by the `Scotch
<https://gitlab.inria.fr/scotch/scotch>`_ library. Which means if needed you
could define different edge and vertex weights
.. code-block:: c++
mesh.distribute(_edge_weight_function =
[](auto &&, auto &&) { return 1; },
_vertex_weight_function =
[](auto &&) { return 1; });
The `vertex` weights correspond to the computational cost of the elements, and
the `edge` weights relates to the cost of communications between 2 elements.
To run the simulation you will need to use a runner appropriate to your machine,
like `mpirun`, `srun`, `arun`, etc.
.. code-block:: sh
$ mpirun -np 4 ./my_simulation

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