Expanded the main page in the Doxygen documentation

![TNL logo](tnl-logo.jpg)

TNL is a collection of building blocks that facilitate the development of

efficient numerical solvers. It is implemented in C++ using modern programming

paradigms in order to provide flexible and user friendly interface. TNL provides

native support for modern hardware architectures such as multicore CPUs, GPUs,

and distributed systems, which can be managed via a unified interface.

Similarly to the STL, features provided by the TNL can be grouped into

several modules:

> TODO: Each topic in this list should have a separate page or tutorial.

- _Core concepts_.

  The main concept used in the TNL is the `Device` type which is used in most of

  the other parts of the library. For data structures such as

  \ref TNL::Containers::Array "Array" it specifies where the data should be

  allocated, whereas for algorithms such as \ref TNL::ParallelFor "ParallelFor"

  it specifies how the algorithm should be executed.

- \ref TNL::Containers "Containers".

  TNL provides generic containers such as array, multidimensional array or array

  views, which abstract data management on different hardware architectures.

- _Linear algebra._

  TNL provides generic data structures and algorithms for linear algebra, such

  as \ref TNL::Containers::Vector "vectors",

  \ref TNL::Matrices "sparse matrices",

  \ref TNL::Solvers::Linear "Krylov solvers" and

  \ref TNL::Solvers::Linear::Preconditioners "preconditioners".

   - Sparse matrix formats: CSR, Ellpack, Sliced Ellpack, tridiagonal,

     multidiagonal

   - Krylov solvers: CG, BiCGstab, GMRES, CWYGMRES, TFQMR

   - Preconditioners: Jacobi, ILU(0) (CPU only), ILUT (CPU only)

- \ref TNL::Meshes "Meshes".

  TNL provides data structures for the representation of structured or

  unstructured numerical meshes.

- _Solvers for differential equations._

  TNL provides a framework for the development of ODE or PDE solvers.

- \ref TNL::Images "Image processing".

  TNL provides structures for the representation of image data. Imports and

  exports from several file formats are provided using external libraries, such

  as [DCMTK](http://dicom.offis.de/dcmtk.php.en) for DICOM files,

  [libpng](http://www.libpng.org/pub/png/libpng.html) for PNG files, or

  [libjpeg](http://libjpeg.sourceforge.net/) for JPEG files.

## Installation

You can either download the [stable version](http://tnl-project.org/download/)

or directly clone the git repository via HTTPS:

    git clone https://mmg-gitlab.fjfi.cvut.cz/gitlab/tnl/tnl-dev.git

or via SSH:

    git clone gitlab@mmg-gitlab.fjfi.cvut.cz:tnl/tnl-dev.git

Since TNL is a header-only library, no installation is necessary to actually use

the library. You can just extract the [src/TNL](

https://mmg-gitlab.fjfi.cvut.cz/gitlab/tnl/tnl-dev/tree/develop/src/TNL)

subdirectory, for example into `~/.local/include/`, and configure your compiler

to include headers from this path.

Optionally, you may want to compile and install various [tools and examples](

#optional-components).

### Supported compilers

You need a compiler which supports the [C++14](

https://en.wikipedia.org/wiki/C%2B%2B14) standard, for example [GCC](

https://gcc.gnu.org/) 5.0 or later or [Clang](http://clang.llvm.org/) 3.4 or

later. For CUDA support, you also need [CUDA](

https://docs.nvidia.com/cuda/index.html) 9.0 or later.

### Optional components

TNL provides several optional components such as pre-processing and

post-processing tools which can be compiled and installed by executing the

`install` script:

    ./install

[CMake](https://cmake.org/) 3.12.2 or later is required for the compilation. The

script compiles and/or installs the following components into the `~/.local/`

directory:

- TNL header files from the

  [src/TNL](https://mmg-gitlab.fjfi.cvut.cz/gitlab/tnl/tnl-dev/tree/develop/src/TNL)

  directory.

- Various pre-processing and post-processing tools from the

  [src/Tools](https://mmg-gitlab.fjfi.cvut.cz/gitlab/tnl/tnl-dev/tree/develop/src/Tools)

  directory.

- Python bindings and scripts from the

  [src/Python](https://mmg-gitlab.fjfi.cvut.cz/gitlab/tnl/tnl-dev/tree/develop/src/Python)

  directory.

- Examples of various numerical solvers from the

  [src/Examples](https://mmg-gitlab.fjfi.cvut.cz/gitlab/tnl/tnl-dev/tree/develop/src/Examples)

  directory.

- Benchmarks from the

  [src/Benchmarks](https://mmg-gitlab.fjfi.cvut.cz/gitlab/tnl/tnl-dev/tree/develop/src/Benchmarks)

  directory.

- Compiles and executes the unit tests from the

  [src/UnitTests](https://mmg-gitlab.fjfi.cvut.cz/gitlab/tnl/tnl-dev/tree/develop/src/UnitTests)

  directory.

Individual components can be disabled and the installation prefix can be changed

by passing command-line arguments to the install script. Run `./install --help`

for details.

## Tutorials

> TODO

1. [Vectors](Tutorials/main-page.md)