@@ -292,7 +292,7 @@ Unfortunately, it is practically impossible to design a single efficient represe
Hence, compromises had to be made and this section summarizes our design goals and techniques we found to achieve them.
This section will cover only the important ideas behind the implementation of the \ic{Mesh} class template in the TNL library.
The full public interface as well as examples how to use the data structure can be found in the documentation which is accessible through the TNL project's website (\url{https://tnl-project.org/documentation/}).\todo{cite as Online Resource?}
The full public interface as well as examples showing how to use the data structure can be found in the TNL project's documentation \cite{TNL:documentation}.
\subsubsection{Programming language}
\label{sec:meshes:programming language}
@@ -515,7 +515,7 @@ Unfortunately, the term \emph{orientation} is commonly used in two different con
\label{sec:meshes:implementation}
In this section, we describe additional implementation details related to mesh initialization, input from files and reordering of the mesh entities, which is a common optimization to improve data locality.
The high-level documentation for programmers, including code examples, can be found via the TNL project's website \todo{cite as Online Resource?}(\url{https://tnl-project.org/documentation/}), in the section related to unstructured meshes.
The high-level documentation for programmers, including code examples, can be found in the TNL project's documentation \cite{TNL:documentation} in the section related to unstructured meshes.
@@ -12,7 +12,7 @@ For three- and more-dimensional arrays there are even more combinations of possi
For these reasons, we developed a data structure which allows to configure the indexing of multidimensional data and thus optimize the data structure for given algorithm and hardware architecture.
The data structure was first proposed in \cite{klinkovsky:2017thesis} and its implementation based on the \CPPfourteen standard was later integrated into the TNL library \cite{oberhuber:2021tnl} and further developed therein.
The TNL project's documentation (\url{https://tnl-project.org/documentation/})\todo{cite as Online Resource?} provides an overview of the public interface and examples showing how the data structure can be used.
The TNL project's documentation \cite{TNL:documentation} provides an overview of the public interface and examples showing how the data structure can be used.
In the following subsections, we describe the \emph{distributed} version of the data structure, which is especially useful for the algorithms presented in \cref{chapter:numerical methods}.
\subsection{Distributed multidimensional array}
@@ -36,7 +36,7 @@ In order to represent a distributed multidimensional array, each MPI rank needs
According to the convention used in TNL, the local subarray spans the multidimensional interval $[\ic{localBegins}, \ic{localEnds})$.
\end{itemize}
Although these attributes of a distributed multidimensional array can be created separately and managed manually, TNL provides a convenient data structure \ic{TNL::Containers::DistributedNDArray} that provides a high-level interface to manage these attributes.
The TNL project's documentation (\url{https://tnl-project.org/documentation/})\todo{cite as Online Resource?} provides an overview of the public interface and examples showing how the data structure can be used.\todo{write the documentation for \ic{DistributedNDArray}}
The TNL project's documentation \cite{TNL:documentation} provides an overview of the public interface and examples showing how the data structure can be used.\todo{write the documentation for \ic{DistributedNDArray}}
\subsection{Operations}
Algorithms involving a distributed multidimensional array may perform different operations on the data structure.
