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# Template Numerical Library

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:

- _Core concepts_.
  The main concepts used in TNL are the _memory space_, which represents the
  part of memory where given data is allocated, and the _execution model_,
  which represents the way how given (typically parallel) algorithm is executed.
  For example, data can be allocated in the main system memory, in the GPU
  memory, or using the CUDA Unified Memory which can be accessed from the host
  as well as from the GPU. On the other hand, algorithms can be executed using
  either the host CPU or an accelerator (GPU), and for each there are many ways
  to manage parallel execution. The usage of memory spaces is abstracted with
  [allocators][allocators] and the execution model is represented by
  [devices][devices]. See the [Core concepts][core concepts] page for details.
- _[Containers][containers]_.
  TNL provides generic containers such as array, multidimensional array or array
  views, which abstract data management and execution of common operations on
  different hardware architectures.
- _Linear algebra._
  TNL provides generic data structures and algorithms for linear algebra, such
  as [vectors][vectors], [sparse matrices][matrices],
  [Krylov solvers][linear solvers] and [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)
- _[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.
- _[Image processing][image processing]_.
  TNL provides structures for the representation of image data. Imports and
  exports from several file formats such as DICOM, PNG, and JPEG are provided
  using external libraries (see below).

See also [Comparison with other libraries](
https://mmg-gitlab.fjfi.cvut.cz/doc/tnl/comparison_with_other_libraries.html).

[allocators]: https://mmg-gitlab.fjfi.cvut.cz/doc/tnl/namespaceTNL_1_1Allocators.html
[devices]: https://mmg-gitlab.fjfi.cvut.cz/doc/tnl/namespaceTNL_1_1Devices.html
[core concepts]: https://mmg-gitlab.fjfi.cvut.cz/doc/tnl/core_concepts.html
[containers]: https://mmg-gitlab.fjfi.cvut.cz/doc/tnl/namespaceTNL_1_1Containers.html
[vectors]: https://mmg-gitlab.fjfi.cvut.cz/doc/tnl/classTNL_1_1Containers_1_1Vector.html
[matrices]: https://mmg-gitlab.fjfi.cvut.cz/doc/tnl/namespaceTNL_1_1Matrices.html
[linear solvers]: https://mmg-gitlab.fjfi.cvut.cz/doc/tnl/namespaceTNL_1_1Solvers_1_1Linear.html
[preconditioners]: https://mmg-gitlab.fjfi.cvut.cz/doc/tnl/namespaceTNL_1_1Solvers_1_1Linear_1_1Preconditioners.html
[meshes]: https://mmg-gitlab.fjfi.cvut.cz/doc/tnl/namespaceTNL_1_1Meshes.html
[image processing]: https://mmg-gitlab.fjfi.cvut.cz/doc/tnl/namespaceTNL_1_1Images.html

TNL also provides several optional components:

- TNL header files in 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 in the
  [src/Tools](https://mmg-gitlab.fjfi.cvut.cz/gitlab/tnl/tnl-dev/tree/develop/src/Tools)
  directory.
- Python bindings and scripts in the
  [src/Python](https://mmg-gitlab.fjfi.cvut.cz/gitlab/tnl/tnl-dev/tree/develop/src/Python)
  directory.
- Examples of various numerical solvers in the
  [src/Examples](https://mmg-gitlab.fjfi.cvut.cz/gitlab/tnl/tnl-dev/tree/develop/src/Examples)
  directory.
- Benchmarks in the
  [src/Benchmarks](https://mmg-gitlab.fjfi.cvut.cz/gitlab/tnl/tnl-dev/tree/develop/src/Benchmarks)
  directory.

These components can be individually enabled or disabled and installed by a
convenient `install` script. See the [Installation][installation] section in
the documentation for details.

## Documentation

See the [full documentation][full documentation] for information about:

- [installation instructions][installation],
- [usage hints][usage],
- [tutorials][tutorials],
- [API reference manual][API],

and other documented topics.

[full documentation]: https://mmg-gitlab.fjfi.cvut.cz/doc/tnl/
[installation]: https://mmg-gitlab.fjfi.cvut.cz/doc/tnl/#installation
[usage]: https://mmg-gitlab.fjfi.cvut.cz/doc/tnl/#usage
[tutorials]: https://mmg-gitlab.fjfi.cvut.cz/doc/tnl/Tutorials.html
[API]: https://mmg-gitlab.fjfi.cvut.cz/doc/tnl/annotated.html

## Authors

Project leader: Tomáš Oberhuber

Main developers:
- Tomáš Oberhuber
- Jakub Klinkovský

Current developers:
- Aleš Wodecki – adaptive grids
- Matouš Fencl – solvers for the Hamilton–Jacobi equation
- Lukáš Čejka – sparse matrix formats

Former developers:
- Jan Schafer – solvers for compressible Navier–Stokes equations
- Vít Hanousek – distributed numerical grids
- Vítězslav Žabka – unstructured numerical meshes
- Tomáš Sobotík – solvers for the Hamilton–Jacobi equation
- Libor Bakajsa – sparse matrix formats
- Ondřej Székely – solvers for parabolic problems
- Vacata Jan – sparse matrix formats
- Heller Martin – sparse matrix formats
- Novotný Matěj – high precision arithmetics

See also the [full list of authors and their contributions](
https://mmg-gitlab.fjfi.cvut.cz/gitlab/tnl/tnl-dev/graphs/develop).

## License

Template Numerical Library is provided under the terms of the [MIT License](
https://mmg-gitlab.fjfi.cvut.cz/gitlab/tnl/tnl-dev/blob/develop/LICENSE).