anchor.diff:  | *GPU*     | - | NVidia GPU with compute-capability > 6 and nvcc-8.0
   | *MPI*     | - | mpi installation compatible with compiler (must be cuda-aware in case hybrid MPI+GPU is the target system)
   |=======================================================================
  -____
  +[NOTE]
   Our GPU backend uses the
   https://developer.nvidia.com/cuda-zone[Nvidia-CUDA] programming
   environment and in order to compile and run a program for a GPU a user
  
positive.code: chooses the correct implementation for you! This is a first example of a
  __container free numerical algorithm__.
  
  === Using FELTOR on Windows
  FELTOR has been developed mostly on Linux machines. Recently, it has
  become possible to develop on Windows using https://www.visualstudio.com/[Microsoft Visual Studio].
  ____
  Unfortunately, the msvc compiler only supports an outdated OpenMP version so
  consider a performance penalty of approximately factor 2, when running the OpenMP backend on Windows.
  ____
  
  For the download of the library and its dependencies described <<sec_quickstart,previously>> you may want
  to activate Bash in Windows 10 or
   consider a software solution like https://desktop.github.com
  ____
  The include paths need to be set in the project properties.
  Please also enable the use of intrinsic functions, the AVX2 instruction set and
  activate OpenMP support.
  It is important to set the Platform to x64!
  ____
  
  === Running a FELTOR simulation
  
  Now, we want to compile and run a simulation program. To this end, we have to
  
negative.code: compiler. The CPU version of the backend is equally valid and provides
  the same functionality. Analogously, an MPI installation is only required if the user targets
  a distributed memory system.
  
  === Running a FELTOR test or benchmark program
  
  