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In order to numerically calculate physical observables we need to discretise  the problem. To this end, we use a mesh representation (e.g. which changes the charge  density and the electrostatic potential to discrete functions), with values defined  over a finite number of points distributed over a mesh. As shown in the Figure \ref{fig:partitioning}, two parameters define the mesh: the length and the spacing. Once constructed, it is partitioned over the processes (6 in the example). Real-space mesh approach is really efficient in terms of performance. {\sc Octopus} has shown a high performance in current architectures \cite{Alberdi_2014}, not only because of the mesh-partitioning but also because of other kind of parallelizations. One of those natural way of parallelization is the state parallelization. Indeed, every state (or wavefunction) of the system is represented over a discrete mesh, being each state independent of the others. The shape of the mesh can be parallelepiped, sphere or adaptive (as shown in Figure \ref{fig:partitioning}. In general, besides the general mesh, another one is used for the Poisson solver. This latter one has to be necessarily parallelepiped.