-Traditional iterative solvers have global synchronizations that penalize the
-scalability. Two possible solutions consists either in using asynchronous
-iterative methods~\cite{ref18} or to use multisplitting algorithms. In this
-paper, we will reconsider the use of a multisplitting method. In opposition to
-traditional multisplitting method that suffer from slow convergence, as
-proposed in~\cite{huang1993krylov}, the use of a minimization process can
-drastically improve the convergence.
-
-The paper is organized as follows. First in Section~\ref{sec:02} is given some related works and the main principle of multisplitting methods. The, in Section~\ref{sec:03} is presented the algorithm of our Krylov multisplitting method based on inner-outer iterations. Finally, in Section~\ref{sec:04}, the parallel experiments on Hector architecture show the performances of the Krylov multisplitting algorithm compared to the classical GMRES algorithm to solve a 3D Poisson problem.
+%Traditional iterative solvers have global synchronizations that penalize the
+%scalability. Two possible solutions consists either in using asynchronous
+%iterative methods~\cite{ref18} or to use multisplitting algorithms. In this
+%paper, we will reconsider the use of a multisplitting method. In opposition to
+%traditional multisplitting method that suffer from slow convergence, as
+%proposed in~\cite{huang1993krylov}, the use of a minimization process can
+%drastically improve the convergence.
+
+Traditional parallel iterative solvers are based on fine-grain computations that
+frequently require data exchanges between computing nodes and have global
+synchronizations that penalize the scalability. Particularly, they are more
+penalized on large scale architectures or on distributed platforms composed of
+distant clusters interconnected by a high-latency network. It is therefore
+imperative to develop coarse-grain based algorithms to reduce the communications
+in the parallel iterative solvers. Two possible solutions consists either in
+using asynchronous iterative methods~\cite{ref18} or to use multisplitting
+algorithms. In this paper, we will reconsider the use of a multisplitting
+method. In opposition to traditional multisplitting method that suffer from slow
+convergence, as proposed in~\cite{huang1993krylov}, the use of a minimization
+process can drastically improve the convergence.
+
+The present paper is organized as follows. First, Section~\ref{sec:02} presents
+some related works and the principle of multisplitting methods. Then, in
+Section~\ref{sec:03} is presented the algorithm of our Krylov multisplitting
+method based on inner-outer iterations. Finally, in Section~\ref{sec:04}, the
+parallel experiments on Hector architecture show the performances of the Krylov
+multisplitting algorithm compared to the classical GMRES algorithm to solve a 3D
+Poisson problem.
