Added comparison with other libraries to the documentation

Documentation/Pages/comparison-with-other-libraries.md

+\page comparison_with_other_libraries  Comparison with other libraries
+
+## Memory space and execution model
+
+TNL has separate concepts for the memory space and execution model, which are
+represented by different template parameters. See the \ref core_concepts
+"Core concepts" page for details.
+
+- Most other libraries have separate types for CPU and GPU data structures
+  (e.g. `Vector` and `cuVector`):
+  - [Thrust](https://github.com/thrust/thrust/): `host_vector`, `device_vector`
+    (plus macro-based selection, see below)
+  - [Paralution](http://www.paralution.com/documentation/): `HostVector`, `AcceleratorVector`
+  - [Bandicoot](https://coot.sourceforge.io/), [Kaldi](http://kaldi-asr.org/doc/about.html)
+- These libraries have the concept of a "memory space" which is configurable as
+  a template parameter:
+  - [CUV](https://github.com/deeplearningais/CUV)
+  - [CUSP](http://cusplibrary.github.io/classcusp_1_1array1d.html) - but CUSP
+    uses Thrust, so `device_memory` might be the same as `host_memory` if OpenMP
+    is used as the `device`
+  - [Kokkos](https://github.com/kokkos/kokkos) - they have a concept of a
+    "memory space" and "execution space", but there is also some default choice
+    of the spaces, possibly even through command-line arguments (in which case
+    the array type would be polymorphic, because something has to store the
+    current memory/execution space)
+- These libraries have transparent access to the data from GPU and CPU:
+  - the CUDA toolkit itself, via `cudaMallocManaged`
+  - [cudarrays](https://github.com/cudarrays/cudarrays) - they have custom
+    virtual memory system using `cudaMalloc` and the standard host allocator
+- These libraries select the (default) device based on some macro
+  (this approach is way too simple, because multiple different devices cannot be
+  combined):
+  - Thrust: see [device backends](https://github.com/thrust/thrust/wiki/Device-Backends)
+    and [host backends](https://github.com/thrust/thrust/wiki/Host-Backends)
+- These libraries do not abstract memory space, only execution model:
+  - [RAJA](https://github.com/LLNL/RAJA)
+  - [Nebo](https://www.sciencedirect.com/science/article/pii/S0164121216000182)
+    (also with a macro-based selection)
+
+## Multidimensional arrays
+
+TODO: compare the implementation of multidimensional arrays
+(features described in the merge request: https://mmg-gitlab.fjfi.cvut.cz/gitlab/tnl/tnl-dev/merge_requests/18 )
+
+- http://cpptruths.blogspot.cz/2011/10/multi-dimensional-arrays-in-c11.html
+- http://www.nongnu.org/tensors/ (last commit in 2012)
+- https://bitbucket.org/wlandry/ftensor/src
+- [Eigen tensors](https://bitbucket.org/eigen/eigen/src/default/unsupported/Eigen/CXX11/src/Tensor/README.md?at=default&fileviewer=file-view-default) - Many operations, expression templates, either pure-static or pure-dynamic sizes, only column-major format (row-major support is incomplete), little GPU support.
+- [cudarrays](https://github.com/cudarrays/cudarrays) - Only up to 3D arrays, both static and dynamic, compile-time permutations using `std::tuple`.
+- [RAJA](https://github.com/LLNL/RAJA) - No memory management, views are initialized with a raw pointer, index permutations are initialized at runtime, only dynamic dimensions.
+- [Kokkos](https://github.com/kokkos/kokkos) - Configurable layout and default selection based on the memory/execution space, but only AoS and SoA are considered, even for `N > 2`. For parallel work there is only one leading dimension - it does not map to 2D or 3D CUDA grids.
+- [CUV](https://github.com/deeplearningais/CUV) - Assumption that "everything is an n-dimensional array" (like Matlab), CPU and GPU support, column-major or row-major, integration with Python and Numpy.

Documentation/Pages/main-page.md

@@ -51,6 +51,8 @@ several modules:
   [libpng](http://www.libpng.org/pub/png/libpng.html) for PNG files, or
   [libjpeg](http://libjpeg.sourceforge.net/) for JPEG files.
 
+See also \ref comparison_with_other_libraries "Comparison with other libraries".
+
 ## Installation
 
 You can either download the [stable version](http://tnl-project.org/download/)