-\section{Introduction}
-The use of multi-core architectures for solving large scientific problems seems to become imperative in a lot of cases.
-Whatever the scale of these architectures (distributed clusters, computational grids, embedded multi-core \ldots) they are generally
-well adapted to execute complexe parallel applications operating on a large amount of data. Unfortunately, users (industrials or scientists),
-who need such computational resources, may not have an easy access to such efficient architectures. The cost of using the platform and/or the cost of
-testing and deploying an application are often very important. So, in this context it is difficult to optimize a given application for a given
-architecture. In this way and in order to reduce the access cost to these computing resources it seems very interesting to use a simulation environment.
-The advantages are numerous: development life cycle, code debugging, ability to obtain results quickly \ldots at the condition that the simulation results are in education with the real ones.
-
-In this paper we focus on a class of highly efficient parallel algorithms called \emph{iterative algorithms}. The
-parallel scheme of iterative methods is quite simple. It generally involves the division of the problem
-into several \emph{blocks} that will be solved in parallel on multiple
-processing units. Each processing unit has to
-compute an iteration, to send/receive some data dependencies to/from
-its neighbors and to iterate this process until the convergence of
-the method. Several well-known methods demonstrate the convergence of these algorithms~\cite{BT89,Bahi07}.
-In this processing mode a task cannot begin a new iteration while it
-has not received data dependencies from its neighbors. We say that the iteration computation follows a synchronous scheme.
-In the asynchronous scheme a task can compute a new iteration without having to
-wait for the data dependencies coming from its neighbors. Both
-communication and computations are asynchronous inducing that there is
-no more idle times, due to synchronizations, between two
-iterations~\cite{bcvc06:ij}. This model presents some advantages and drawbacks that we detail in section 2 but even if the number of iterations required to converge is
-generally greater than for the synchronous case, it appears that the asynchronous iterative scheme can significantly reduce overall execution
-times by suppressing idle times due to synchronizations~(see \cite{Bahi07} for more details).
-
-Nevertheless, in both cases (synchronous or asynchronous) it is very time consuming to find optimal configuration and deployment requirements
-for a given application on a given multi-core architecture. Finding good resource allocations policies under varying CPU power, network speeds and
-loads is very challenging and labor intensive.~\cite{Calheiros:2011:CTM:1951445.1951450}. This problematic is even more difficult for the asynchronous scheme
-where variations of the parameters of the execution platform can lead to very different number of iterations required to converge and so to very different execution times.
-In this challenging context we think that the use of a simulation tool can greatly leverage the possibility of testing various platform scenarios.
-
-The main contribution of this paper is to show that the use of a simulation tool (i.e. the SimGrid toolkit~\cite{SimGrid}) in the context of real
-parallel applications (i.e. large linear system solver) can help developers to better tune their application for a given multi-core architecture.
-To show the validity of this approach we first compare the simulated execution of the multisplitting algorithm with the GMRES (Generalized Minimal Residual) solver
-\cite{ref1} in synchronous mode. The obtained results on different simulated multi-core architectures confirm the real results previously obtained on non simulated architectures.
-We also confirm the efficiency of the asynchronous multisplitting algorithm comparing to the synchronous GMRES. In this way and with a simple computing architecture (a laptop)
-SimGrid allows us to run a test campaign of a real parallel iterative applications on different simulated multi-core architectures.
-To our knowledge, there is no related work on the large-scale multi-core simulation of a real synchronous and asynchronous iterative application.
-
-This paper is organized as follows. Section 1 \ref{sec:synchro} presents the iteration model we use and more particularly the asynchronous scheme.
-In section \ref{sec:simgrid} the SimGrid simulation toolkit is presented. Section \ref{sec:04} details the different solvers that we use.
-Finally our experimental results are presented in section \ref{\sec:expe} followed by some concluding remarks and perspectives.
-
-
-\section{The asynchronous iteration model}
+\section{Introduction} The use of multi-core architectures to solve large
+scientific problems seems to become imperative in many situations.
+Whatever the scale of these architectures (distributed clusters, computational
+grids, embedded multi-core,~\ldots) they are generally well adapted to execute
+complex parallel applications operating on a large amount of data.
+Unfortunately, users (industrials or scientists), who need such computational
+resources, may not have an easy access to such efficient architectures. The cost
+of using the platform and/or the cost of testing and deploying an application
+are often very important. So, in this context it is difficult to optimize a
+given application for a given architecture. In this way and in order to reduce
+the access cost to these computing resources it seems very interesting to use a
+simulation environment. The advantages are numerous: development life cycle,
+code debugging, ability to obtain results quickly\dots{} In counterpart, the simulation results need to be consistent with the real ones.
+
+In this paper we focus on a class of highly efficient parallel algorithms called
+\emph{iterative algorithms}. The parallel scheme of iterative methods is quite
+simple. It generally involves the division of the problem into several
+\emph{blocks} that will be solved in parallel on multiple processing
+units. Each processing unit has to compute an iteration to send/receive some
+data dependencies to/from its neighbors and to iterate this process until the
+convergence of the method. Several well-known studies demonstrate the
+convergence of these algorithms~\cite{BT89,bahi07}. In this processing mode a
+task cannot begin a new iteration while it has not received data dependencies
+from its neighbors. We say that the iteration computation follows a
+\textit{synchronous} scheme. In the asynchronous scheme a task can compute a new
+iteration without having to wait for the data dependencies coming from its
+neighbors. Both communication and computations are \textit{asynchronous}
+inducing that there is no more idle time, due to synchronizations, between two
+iterations~\cite{bcvc06:ij}. This model presents some advantages and drawbacks
+that we detail in section~\ref{sec:asynchro} but even if the number of
+iterations required to converge is generally greater than for the synchronous
+case, it appears that the asynchronous iterative scheme can significantly
+reduce overall execution times by suppressing idle times due to
+synchronizations~(see~\cite{bahi07} for more details).
+
+Nevertheless, in both cases (synchronous or asynchronous) it is very time
+consuming to find optimal configuration and deployment requirements for a given
+application on a given multi-core architecture. Finding good resource
+allocations policies under varying CPU power, network speeds and loads is very
+challenging and labor intensive~\cite{Calheiros:2011:CTM:1951445.1951450}. This
+problematic is even more difficult for the asynchronous scheme where a small
+parameter variation of the execution platform and of the application data can
+lead to very different numbers of iterations to reach the converge and so to
+very different execution times. In this challenging context we think that the
+use of a simulation tool can greatly leverage the possibility of testing various
+platform scenarios.
+
+The main contribution of this paper is to show that the use of a simulation tool
+(i.e. the SimGrid toolkit~\cite{SimGrid}) in the context of real parallel
+applications (i.e. large linear system solvers) can help developers to better
+tune their application for a given multi-core architecture. To show the validity
+of this approach we first compare the simulated execution of the multisplitting
+algorithm with the GMRES (Generalized Minimal Residual)
+solver~\cite{saad86} in synchronous mode. The simulation results allow us to
+determine which method to choose given a specified multi-core architecture.
+
+\LZK{Pas trop convainquant comme argument pour valider l'approche de simulation. \\On peut dire par exemple: on a pu simuler différents algos itératifs à large échelle (le plus connu GMRES et deux variantes de multisplitting) et la simulation nous a permis (sans avoir le vrai matériel) de déterminer quelle serait la meilleure solution pour une telle configuration de l'archi ou vice versa.\\A revoir...}
+\DL{OK : ajout d'une phrase précisant tout cela}
+
+Moreover the obtained results on different simulated multi-core architectures
+confirm the real results previously obtained on non simulated architectures.
+More precisely the simulated results are in accordance (i.e. with the same order
+of magnitude) with the works presented in~\cite{couturier15}, which show that the synchronous
+multisplitting method is more efficient than GMRES for large scale clusters.
+
+\LZK{Il n y a pas dans la partie expé cette comparaison et confirmation des
+résultats entre la simulation et l'exécution réelle des algos sur les vrais
+clusters.\\ Sinon on pourrait ajouter dans la partie expé une référence vers le
+journal supercomput de krylov multi pour confirmer que cette méthode est
+meilleure que GMRES sur les clusters large échelle.} \DL{OK ajout d'une phrase.
+Par contre je n'ai pas la ref. Merci de la mettre}
+
+Simulated results also confirm the efficiency of the asynchronous
+multisplitting algorithm compared to the synchronous GMRES especially in case of
+geographically distant clusters.
+
+\LZK{P.S.: Pour tout le papier, le principal objectif n'est pas de faire des comparaisons entre des méthodes itératives!!\\Sinon, les deux algorithmes Krylov multisplitting synchrone et multisplitting asynchrone sont plus efficaces que GMRES sur des clusters à large échelle.\\Et préciser, si c'est vraiment le cas, que le multisplitting asynchrone est plus efficace et adapté aux clusters distants par rapport aux deux autres algos (je n'ai pas encore lu la partie expé)}
+\DL{Tu as raison on s'est posé la question de garder ou non cette partie des résultats. On a décidé de la garder pour avoir plus de chose à montrer. J'ai essayer de clarifier un peu}
+
+In
+this way and with a simple computing architecture (a laptop) SimGrid allows us
+to run a test campaign of a real parallel iterative applications on
+different simulated multi-core architectures. To our knowledge, there is no
+related work on the large-scale multi-core simulation of a real synchronous and
+asynchronous iterative application.
+
+This paper is organized as follows. Section~\ref{sec:asynchro} presents the
+iteration model we use and more particularly the asynchronous scheme. In
+section~\ref{sec:simgrid} the SimGrid simulation toolkit is presented.
+Section~\ref{sec:04} details the different solvers that we use. Finally our
+experimental results are presented in section~\ref{sec:expe} followed by some
+concluding remarks and perspectives.
+
+\LZK{Proposition d'un titre pour le papier: Grid-enabled simulation of large-scale linear iterative solvers.}
+
+
+\section{The asynchronous iteration model and the motivations of our work}
