modif intro

[hpcc2014.git] / hpcc.tex

diff --git a/hpcc.tex b/hpcc.tex
index dd06eebc905d58734f2d3d5c16022c85850180be..29d00a12b6988e8e5291c2f29a9b6d550054f356 100644 (file)
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -31,6 +31,8 @@
   \todo[color=blue!10,#1]{\sffamily\textbf{LZK:} #2}\xspace}
 \newcommand{\RC}[2][inline]{%
   \todo[color=red!10,#1]{\sffamily\textbf{RC:} #2}\xspace}
   \todo[color=blue!10,#1]{\sffamily\textbf{LZK:} #2}\xspace}
 \newcommand{\RC}[2][inline]{%
   \todo[color=red!10,#1]{\sffamily\textbf{RC:} #2}\xspace}

+\newcommand{\CER}[2][inline]{%
+  \todo[color=pink!10,#1]{\sffamily\textbf{CER:} #2}\xspace}

 
 \algnewcommand\algorithmicinput{\textbf{Input:}}
 \algnewcommand\Input{\item[\algorithmicinput]}
 
 \algnewcommand\algorithmicinput{\textbf{Input:}}
 \algnewcommand\Input{\item[\algorithmicinput]}


@@ -39,17 +41,18 @@
 \algnewcommand\Output{\item[\algorithmicoutput]}
 
 \newcommand{\MI}{\mathit{MaxIter}}
 \algnewcommand\Output{\item[\algorithmicoutput]}
 
 \newcommand{\MI}{\mathit{MaxIter}}

+\newcommand{\Time}[1]{\mathit{Time}_\mathit{#1}}

 
 \begin{document}
 
 
 \begin{document}
 

-\title{Simulation of Asynchronous Iterative Numerical Algorithms Using SimGrid}
+\title{Simulation of Asynchronous Iterative Algorithms Using SimGrid}

 
 \author{%
   \IEEEauthorblockN{%
     Charles Emile Ramamonjisoa\IEEEauthorrefmark{1},
 
 \author{%
   \IEEEauthorblockN{%
     Charles Emile Ramamonjisoa\IEEEauthorrefmark{1},

+    Lilia Ziane Khodja\IEEEauthorrefmark{2},

     David Laiymani\IEEEauthorrefmark{1},
     David Laiymani\IEEEauthorrefmark{1},

-    Arnaud Giersch\IEEEauthorrefmark{1},
-    Lilia Ziane Khodja\IEEEauthorrefmark{2} and
+    Arnaud Giersch\IEEEauthorrefmark{1} and

     Raphaël Couturier\IEEEauthorrefmark{1}
   }
   \IEEEauthorblockA{\IEEEauthorrefmark{1}%
     Raphaël Couturier\IEEEauthorrefmark{1}
   }
   \IEEEauthorblockA{\IEEEauthorrefmark{1}%


@@ -68,31 +71,20 @@
 
 \maketitle
 
 
 \maketitle
 

-\RC{Ordre des auteurs pas définitif.}

 \begin{abstract}
 \begin{abstract}

-In recent years, the scalability of large-scale implementation in a 
-distributed environment of algorithms becoming more and more complex has 
-always been hampered by the limits of physical computing resources 
-capacity. One solution is to run the program in a virtual environment 
-simulating a real interconnected computers architecture. The results are 
-convincing and useful solutions are obtained with far fewer resources 
-than in a real platform. However, challenges remain for the convergence 
-and efficiency of a class of algorithms that concern us here, namely 
-numerical parallel iterative algorithms executed in asynchronous mode, 
-especially in a large scale level. Actually, such algorithm requires a 
-balance and a compromise between computation and communication time 
-during the execution. Two important factors determine the success of the 
-experimentation: the convergence of the iterative algorithm on a large 
-scale and the execution time reduction in asynchronous mode. Once again, 
-from the current work, a simulated environment like SimGrid provides
-accurate results which are difficult or even impossible to obtain in a 
-physical platform by exploiting the flexibility of the simulator on the 
-computing units clusters and the network structure design. Our 
-experimental outputs showed a saving of up to \np[\%]{40} for the algorithm
-execution time in asynchronous mode compared to the synchronous one with 
-a residual precision up to \np{E-11}. Such successful results open
-perspectives on experimentations for running the algorithm on a 
-simulated large scale growing environment and with larger problem size. 
+
+Synchronous  iterative  algorithms  are  often less  scalable  than  asynchronous
+iterative  ones.  Performing  large  scale experiments  with  different kind  of
+network parameters is not easy  because with supercomputers such parameters are
+fixed. So one  solution consists in using simulations first  in order to analyze
+what parameters  could influence or not  the behaviors of an  algorithm. In this
+paper, we show  that it is interesting to use SimGrid  to simulate the behaviors
+of asynchronous  iterative algorithms. For that,  we compare the  behaviour of a
+synchronous  GMRES  algorithm  with  an  asynchronous  multisplitting  one  with
+simulations  in  which we  choose  some parameters.   Both  codes  are real  MPI
+codes. Simulations allow us to see when the multisplitting algorithm can be more
+efficient than the GMRES one to solve a 3D Poisson problem.
+

 
 % no keywords for IEEE conferences
 % Keywords: Algorithm distributed iterative asynchronous simulation SimGrid
 
 % no keywords for IEEE conferences
 % Keywords: Algorithm distributed iterative asynchronous simulation SimGrid


@@ -105,62 +97,82 @@ problems raised by  researchers on various scientific disciplines but also by in
 increasing complexity of these requested  applications combined with a continuous increase of their sizes lead to  write
 distributed and parallel algorithms requiring significant hardware  resources (grid computing, clusters, broadband
 network, etc.) but also a non-negligible CPU execution time. We consider in this paper a class of highly efficient
 increasing complexity of these requested  applications combined with a continuous increase of their sizes lead to  write
 distributed and parallel algorithms requiring significant hardware  resources (grid computing, clusters, broadband
 network, etc.) but also a non-negligible CPU execution time. We consider in this paper a class of highly efficient

-parallel algorithms called \emph{numerical iterative algorithms} executed in a distributed environment. As their name
+parallel algorithms called \emph{iterative algorithms} executed in a distributed environment. As their name

 suggests, these algorithms solve a given problem by successive iterations ($X_{n +1} = f(X_{n})$) from an initial value
 $X_{0}$ to find an approximate value $X^*$ of the solution with a very low residual error. Several well-known methods
 suggests, these algorithms solve a given problem by successive iterations ($X_{n +1} = f(X_{n})$) from an initial value
 $X_{0}$ to find an approximate value $X^*$ of the solution with a very low residual error. Several well-known methods

-demonstrate the convergence of these algorithms \cite{BT89,Bahi07}. 
+demonstrate the convergence of these algorithms~\cite{BT89,Bahi07}.

 
 Parallelization of such algorithms generally involve the division of the problem into several \emph{blocks} that will
 be solved in parallel on multiple processing units. The latter will communicate each intermediate results before a new
 iteration starts and until the approximate solution is reached. These parallel  computations can be performed either in
 \emph{synchronous} mode where a new iteration begins only when all nodes communications are completed,
 or in \emph{asynchronous} mode where processors can continue independently with few or no synchronization points. For
 
 Parallelization of such algorithms generally involve the division of the problem into several \emph{blocks} that will
 be solved in parallel on multiple processing units. The latter will communicate each intermediate results before a new
 iteration starts and until the approximate solution is reached. These parallel  computations can be performed either in
 \emph{synchronous} mode where a new iteration begins only when all nodes communications are completed,
 or in \emph{asynchronous} mode where processors can continue independently with few or no synchronization points. For

-instance in the \textit{Asynchronous Iterations~-- Asynchronous Communications (AIAC)} model \cite{bcvc06:ij}, local
+instance in the \textit{Asynchronous Iterations~-- Asynchronous Communications (AIAC)} model~\cite{bcvc06:ij}, local

 computations do not need to wait for required data. Processors can then perform their iterations with the data present
 at that time. Even if the number of iterations required before the convergence is generally greater than for the
 synchronous case, AIAC algorithms can significantly reduce overall execution times by suppressing idle times due to
 computations do not need to wait for required data. Processors can then perform their iterations with the data present
 at that time. Even if the number of iterations required before the convergence is generally greater than for the
 synchronous case, AIAC algorithms can significantly reduce overall execution times by suppressing idle times due to

-synchronizations especially in a grid computing context (see \cite{Bahi07} for more details).
-
-Parallel numerical applications (synchronous or asynchronous) may have different configuration and deployment
-requirements.  Quantifying their resource allocation policies and application scheduling algorithms in
-grid computing environments under varying load, CPU power and network speeds is very costly, very labor intensive and very time
-consuming \cite{Calheiros:2011:CTM:1951445.1951450}. The case of AIAC algorithms is even more problematic since they are very sensible to the
-execution environment context. For instance, variations in the network bandwidth (intra and inter-clusters), in the
-number and the power of nodes, in the number of clusters... can lead to very different number of iterations and so to
-very different execution times. Then, it appears that the use of simulation tools to explore various platform
-scenarios and to run large numbers of experiments quickly can be very promising. In this way, the use of a simulation
-environment to execute parallel  iterative algorithms found some interests in reducing the highly cost of  access to
-computing resources: (1) for the applications development life  cycle and in code debugging (2) and in production to get
-results in a reasonable execution time with a simulated infrastructure not accessible  with physical resources. Indeed,
-the launch of distributed iterative  asynchronous algorithms to solve a given problem on a large-scale  simulated
-environment challenges to find optimal configurations giving the best results with a lowest residual error and in the
-best of execution time. 
-
-To our knowledge, there is no existing work on the large-scale simulation of a real AIAC application. The aim of this
-paper is twofold. First we give a first approach of the simulation of AIAC algorithms using a simulation tool (i.e. the
-SimGrid toolkit \cite{SimGrid}). Second, we confirm the effectiveness of asynchronous mode algorithms by comparing their
-performance with the synchronous mode. More precisely, we had implemented a program for solving large non-symmetric
-linear system of equations by numerical method GMRES (Generalized Minimal Residual) []. We show, that with minor
-modifications of the initial MPI code, the SimGrid toolkit allows us to perform a test campaign of a real AIAC
-application on different computing architectures. The simulated results we obtained are in line with real results
-exposed in ??\AG[]{??}. SimGrid had allowed us to launch the application from a modest computing infrastructure by simulating
-different distributed architectures composed by clusters nodes interconnected by variable speed networks.
-With selected parameters on the network platforms (bandwidth, latency of inter  cluster network) and
-on the clusters architecture (number, capacity calculation power) in the simulated environment, the experimental results
-have demonstrated not only the algorithm convergence within a reasonable time compared with the physical environment
-performance, but also a time saving of up to \np[\%]{40} in asynchronous mode.
-
-This article is structured as follows: after this introduction, the next  section will give a brief description of
-iterative asynchronous model.  Then, the simulation framework SimGrid is presented with the settings to create various
-distributed architectures. The algorithm of  the multisplitting method used by GMRES written with MPI primitives and
-its adaptation to SimGrid with SMPI (Simulated MPI) is detailed in the next section. At last, the experiments results
-carried out will be presented before some concluding remarks and future works.
+synchronizations especially in a grid computing context (see~\cite{Bahi07} for more details).
+
+Parallel   (synchronous  or  asynchronous)   applications  may   have  different
+configuration   and  deployment   requirements.    Quantifying  their   resource
+allocation  policies and  application  scheduling algorithms  in grid  computing
+environments under  varying load, CPU power  and network speeds  is very costly,
+very          labor           intensive          and          very          time
+consuming~\cite{Calheiros:2011:CTM:1951445.1951450}.     The   case    of   AIAC
+algorithms  is  even  more problematic  since  they  are  very sensible  to  the
+execution environment context. For instance, variations in the network bandwidth
+(intra and inter-clusters), in the number  and the power of nodes, in the number
+of clusters\dots{}  can lead to  very different number  of iterations and  so to
+very  different execution times.  Then, it  appears that  the use  of simulation
+tools  to  explore  various platform  scenarios  and  to  run large  numbers  of
+experiments quickly can be very promising.  In this way, the use of a simulation
+environment  to execute parallel  iterative algorithms  found some  interests in
+reducing  the  highly  cost  of  access  to computing  resources:  (1)  for  the
+applications development life cycle and  in code debugging (2) and in production
+to get  results in a reasonable  execution time with  a simulated infrastructure
+not  accessible  with physical  resources.  Indeed,  the  launch of  distributed
+iterative  asynchronous algorithms  to solve  a given  problem on  a large-scale
+simulated environment challenges to  find optimal configurations giving the best
+results with a lowest residual error and in the best of execution time.
+
+To our knowledge,  there is no existing work on the  large-scale simulation of a
+real  AIAC application.   {\bf  The contribution  of  the present  paper can  be
+  summarised  in two  main  points}.  First  we  give a  first  approach of  the
+simulation  of  AIAC algorithms  using  a  simulation  tool (i.e.   the  SimGrid
+toolkit~\cite{SimGrid}).    Second,  we   confirm  the   effectiveness   of  the
+asynchronous  multisplitting algorithm  by  comparing its  performance with  the
+synchronous GMRES (Generalized Minimal  Residual) \cite{ref1}.  Both these codes
+can be  used to  solve large linear  systems. In  this paper, we  focus on  a 3D
+Poisson  problem.  We show,  that with  minor modifications  of the  initial MPI
+code, the SimGrid  toolkit allows us to  perform a test campaign of  a real AIAC
+application on different computing architectures.
+% The  simulated results  we
+%obtained are  in line with real  results exposed in  ??\AG[]{ref?}. 
+SimGrid  had  allowed us  to  launch the  application  from  a modest  computing
+infrastructure  by simulating  different distributed  architectures  composed by
+clusters  nodes interconnected by  variable speed  networks.  Parameters  of the
+network  platforms  are   the  bandwidth  and  the  latency   of  inter  cluster
+network. Parameters on the cluster's architecture are the number of machines and
+the  computation power  of a  machine.  Simulations show  that the  asynchronous
+multisplitting algorithm  can solve the  3D Poisson problem  approximately twice
+faster than GMRES with two distant clusters.
+
+
+
+This article is structured as follows: after this introduction, the next section
+will  give a  brief  description  of iterative  asynchronous  model.  Then,  the
+simulation framework  SimGrid is presented  with the settings to  create various
+distributed architectures.  Then, the  multisplitting method is presented, it is
+based  on GMRES to  solve each  block obtained  of the  splitting. This  code is
+written with MPI  primitives and its adaptation to  SimGrid with SMPI (Simulated
+MPI) is  detailed in the next  section. At last, the  simulation results carried
+out will be presented before some concluding remarks and future works.

  
 \section{Motivations and scientific context}
 
 As exposed in the introduction, parallel iterative methods are now widely used in many scientific domains. They can be
 classified in three main classes depending on how iterations and communications are managed (for more details readers
  
 \section{Motivations and scientific context}
 
 As exposed in the introduction, parallel iterative methods are now widely used in many scientific domains. They can be
 classified in three main classes depending on how iterations and communications are managed (for more details readers

-can refer to \cite{bcvc06:ij}). In the \textit{Synchronous Iterations~-- Synchronous Communications (SISC)} model data
+can refer to~\cite{bcvc06:ij}). In the \textit{Synchronous Iterations~-- Synchronous Communications (SISC)} model data

 are exchanged at the end of each iteration. All the processors must begin the same iteration at the same time and
 important idle times on processors are generated. The \textit{Synchronous Iterations~-- Asynchronous Communications
 (SIAC)} model can be compared to the previous one except that data required on another processor are sent asynchronously
 are exchanged at the end of each iteration. All the processors must begin the same iteration at the same time and
 important idle times on processors are generated. The \textit{Synchronous Iterations~-- Asynchronous Communications
 (SIAC)} model can be compared to the previous one except that data required on another processor are sent asynchronously


@@ -169,12 +181,14 @@ but unfortunately, the overlapping is only partial and important idle times rema
 computing context, where the number of computational nodes is large, heterogeneous and widely distributed, the idle
 times generated by synchronizations are very penalizing. One way to overcome this problem is to use the
 \textit{Asynchronous Iterations~-- Asynchronous Communications (AIAC)} model. Here, local computations do not need to
 computing context, where the number of computational nodes is large, heterogeneous and widely distributed, the idle
 times generated by synchronizations are very penalizing. One way to overcome this problem is to use the
 \textit{Asynchronous Iterations~-- Asynchronous Communications (AIAC)} model. Here, local computations do not need to

-wait for required data. Processors can then perform their iterations with the data present at that time. Figure
-\ref{fig:aiac} illustrates this model where the gray blocks represent the computation phases, the white spaces the idle
-times and the arrows the communications. With this algorithmic model, the number of iterations required before the
-convergence is generally greater than for the two former classes. But, and as detailed in \cite{bcvc06:ij}, AIAC
+wait for required data. Processors can then perform their iterations with the data present at that time. Figure~\ref{fig:aiac}
+illustrates this model where the gray blocks represent the computation phases, the white spaces the idle
+times and the arrows the communications.
+\AG{There are no ``white spaces'' on the figure.}
+With this algorithmic model, the number of iterations required before the
+convergence is generally greater than for the two former classes. But, and as detailed in~\cite{bcvc06:ij}, AIAC

 algorithms can significantly reduce overall execution times by suppressing idle times due to synchronizations especially
 algorithms can significantly reduce overall execution times by suppressing idle times due to synchronizations especially

-in a grid computing context.
+in a grid computing context.\LZK{Répétition par rapport à l'intro}

 
 \begin{figure}[!t]
   \centering
 
 \begin{figure}[!t]
   \centering


@@ -184,18 +198,25 @@ in a grid computing context.
 \end{figure}
 
 
 \end{figure}
 
 

-It is very challenging to develop efficient applications for large scale, heterogeneous and distributed platforms such
-as computing grids. Researchers and engineers have to develop techniques for maximizing application performance of these
-multi-cluster platforms, by redesigning the applications and/or by using novel algorithms that can account for the
-composite and heterogeneous nature of the platform. Unfortunately, the deployment of such applications on these very
-large scale systems is very costly, labor intensive and time consuming. In this context, it appears that the use of
-simulation tools to explore various platform scenarios at will and to run enormous numbers of experiments quickly can be
-very promising. Several works...
+It is very challenging to develop efficient applications for large scale,
+heterogeneous and distributed platforms such as computing grids. Researchers and
+engineers have to develop techniques for maximizing application performance of
+these multi-cluster platforms, by redesigning the applications and/or by using
+novel algorithms that can account for the composite and heterogeneous nature of
+the platform. Unfortunately, the deployment of such applications on these very
+large scale systems is very costly, labor intensive and time consuming. In this
+context, it appears that the use of simulation tools to explore various platform
+scenarios at will and to run enormous numbers of experiments quickly can be very
+promising. Several works\dots{}

 
 

-In the context of AIAC algorithms, the use of simulation tools is even more relevant. Indeed, this class of applications
-is very sensible to the execution environment context. For instance, variations in the network bandwidth (intra and
-inter-clusters), in the number and the power of nodes, in the number of clusters... can lead to very different number of
-iterations and so to very different execution times.
+\AG{Several works\dots{} what?\\
+  Le paragraphe suivant se trouve déjà dans l'intro ?}
+In the context of AIAC algorithms, the use of simulation tools is even more
+relevant. Indeed, this class of applications is very sensible to the execution
+environment context. For instance, variations in the network bandwidth (intra
+and inter-clusters), in the number and the power of nodes, in the number of
+clusters\dots{} can lead to very different number of iterations and so to very
+different execution times.

 
 
 
 
 
 


@@ -214,79 +235,106 @@ SimGrid provides several programming interfaces: MSG to simulate Concurrent
 Sequential Processes, SimDAG to simulate DAGs of (parallel) tasks, and SMPI to
 run real applications written in MPI~\cite{MPI}.  Apart from the native C
 interface, SimGrid provides bindings for the C++, Java, Lua and Ruby programming
 Sequential Processes, SimDAG to simulate DAGs of (parallel) tasks, and SMPI to
 run real applications written in MPI~\cite{MPI}.  Apart from the native C
 interface, SimGrid provides bindings for the C++, Java, Lua and Ruby programming

-languages.  The SMPI interface supports applications written in C or Fortran,
-with little or no modifications.  SMPI implements about \np[\%]{80} of the MPI
-2.0 standard~\cite{bedaride:hal-00919507}.
-
-%%% explain simulation
-%- simulated processes folded in one real process
-%- simulates interactions on the network, fluid model
-%- able to skip long-lasting computations
-%- traces + visu?
-
-%%% platforms
-%- describe resources and their interconnection, with their properties
-%- XML files
-
-%%% validation + refs
+languages.  SMPI is the interface that has been used for the work exposed in
+this paper.  The SMPI interface implements about \np[\%]{80} of the MPI 2.0
+standard~\cite{bedaride:hal-00919507}, and supports applications written in C or
+Fortran, with little or no modifications.
+
+Within SimGrid, the execution of a distributed application is simulated on a
+single machine.  The application code is really executed, but some operations
+like the communications are intercepted, and their running time is computed
+according to the characteristics of the simulated execution platform.  The
+description of this target platform is given as an input for the execution, by
+the mean of an XML file.  It describes the properties of the platform, such as
+the computing nodes with their computing power, the interconnection links with
+their bandwidth and latency, and the routing strategy.  The simulated running
+time of the application is computed according to these properties.
+
+To compute the durations of the operations in the simulated world, and to take
+into account resource sharing (e.g. bandwidth sharing between competing
+communications), SimGrid uses a fluid model.  This allows to run relatively fast
+simulations, while still keeping accurate
+results~\cite{bedaride:hal-00919507,tomacs13}.  Moreover, depending on the
+simulated application, SimGrid/SMPI allows to skip long lasting computations and
+to only take their duration into account.  When the real computations cannot be
+skipped, but the results have no importance for the simulation results, there is
+also the possibility to share dynamically allocated data structures between
+several simulated processes, and thus to reduce the whole memory consumption.
+These two techniques can help to run simulations at a very large scale.

 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \section{Simulation of the multisplitting method}
 %Décrire le problème (algo) traité ainsi que le processus d'adaptation à SimGrid.
 Let $Ax=b$ be a large sparse system of $n$ linear equations in $\mathbb{R}$, where $A$ is a sparse square and nonsingular matrix, $x$ is the solution vector and $b$ is the right-hand side vector. We use a multisplitting method based on the block Jacobi splitting to solve this linear system on a large scale platform composed of $L$ clusters of processors~\cite{o1985multi}. In this case, we apply a row-by-row splitting without overlapping  
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \section{Simulation of the multisplitting method}
 %Décrire le problème (algo) traité ainsi que le processus d'adaptation à SimGrid.
 Let $Ax=b$ be a large sparse system of $n$ linear equations in $\mathbb{R}$, where $A$ is a sparse square and nonsingular matrix, $x$ is the solution vector and $b$ is the right-hand side vector. We use a multisplitting method based on the block Jacobi splitting to solve this linear system on a large scale platform composed of $L$ clusters of processors~\cite{o1985multi}. In this case, we apply a row-by-row splitting without overlapping  

-\[
-\left(\begin{array}{ccc}
-A_{11} & \cdots & A_{1L} \\
-\vdots & \ddots & \vdots\\
-A_{L1} & \cdots & A_{LL}
-\end{array} \right)
-\times 
-\left(\begin{array}{c}
-X_1 \\
-\vdots\\
-X_L
-\end{array} \right)
-=
-\left(\begin{array}{c}
-B_1 \\
-\vdots\\
-B_L
-\end{array} \right)\] 
-in such a way that successive rows of matrix $A$ and both vectors $x$ and $b$ are assigned to one cluster, where for all $l,m\in\{1,\ldots,L\}$ $A_{lm}$ is a rectangular block of $A$ of size $n_l\times n_m$, $X_l$ and $B_l$ are sub-vectors of $x$ and $b$, respectively, of size $n_l$ each and $\sum_{l} n_l=\sum_{m} n_m=n$.
+\begin{equation*}
+  \left(\begin{array}{ccc}
+      A_{11} & \cdots & A_{1L} \\
+      \vdots & \ddots & \vdots\\
+      A_{L1} & \cdots & A_{LL}
+    \end{array} \right)
+  \times
+  \left(\begin{array}{c}
+      X_1 \\
+      \vdots\\
+      X_L
+    \end{array} \right)
+  =
+  \left(\begin{array}{c}
+      B_1 \\
+      \vdots\\
+      B_L
+    \end{array} \right)
+\end{equation*}
+in such a way that successive rows of matrix $A$ and both vectors $x$ and $b$
+are assigned to one cluster, where for all $\ell,m\in\{1,\ldots,L\}$, $A_{\ell
+  m}$ is a rectangular block of $A$ of size $n_\ell\times n_m$, $X_\ell$ and
+$B_\ell$ are sub-vectors of $x$ and $b$, respectively, of size $n_\ell$ each,
+and $\sum_{\ell} n_\ell=\sum_{m} n_m=n$.

 
 The multisplitting method proceeds by iteration to solve in parallel the linear system on $L$ clusters of processors, in such a way each sub-system
 \begin{equation}
 
 The multisplitting method proceeds by iteration to solve in parallel the linear system on $L$ clusters of processors, in such a way each sub-system
 \begin{equation}

-\left\{
-\begin{array}{l}
-A_{ll}X_l = Y_l \mbox{,~such that}\\
-Y_l = B_l - \displaystyle\sum_{\substack{m=1\\ m\neq l}}^{L}A_{lm}X_m
-\end{array}
-\right.
-\label{eq:4.1}
+  \label{eq:4.1}
+  \left\{
+    \begin{array}{l}
+      A_{\ell\ell}X_\ell = Y_\ell \text{, such that}\\
+      Y_\ell = B_\ell - \displaystyle\sum_{\substack{m=1\\ m\neq \ell}}^{L}A_{\ell m}X_m
+    \end{array}
+  \right.

 \end{equation}
 \end{equation}

-is solved independently by a cluster and communications are required to update the right-hand side sub-vector $Y_l$, such that the sub-vectors $X_m$ represent the data dependencies between the clusters. As each sub-system (\ref{eq:4.1}) is solved in parallel by a cluster of processors, our multisplitting method uses an iterative method as an inner solver which is easier to parallelize and more scalable than a direct method. In this work, we use the parallel algorithm of GMRES method~\cite{ref1} which is one of the most used iterative method by many researchers. 
+is solved independently by a cluster and communications are required to update
+the right-hand side sub-vector $Y_\ell$, such that the sub-vectors $X_m$
+represent the data dependencies between the clusters. As each sub-system
+(\ref{eq:4.1}) is solved in parallel by a cluster of processors, our
+multisplitting method uses an iterative method as an inner solver which is
+easier to parallelize and more scalable than a direct method. In this work, we
+use the parallel algorithm of GMRES method~\cite{ref1} which is one of the most
+used iterative method by many researchers.

 
 \begin{figure}[!t]
   %%% IEEE instructions forbid to use an algorithm environment here, use figure
   %%% instead
 \begin{algorithmic}[1]
 
 \begin{figure}[!t]
   %%% IEEE instructions forbid to use an algorithm environment here, use figure
   %%% instead
 \begin{algorithmic}[1]

-\Input $A_l$ (sparse sub-matrix), $B_l$ (right-hand side sub-vector)
-\Output $X_l$ (solution sub-vector)\vspace{0.2cm}
-\State Load $A_l$, $B_l$
+\Input $A_\ell$ (sparse sub-matrix), $B_\ell$ (right-hand side sub-vector)
+\Output $X_\ell$ (solution sub-vector)\medskip
+
+\State Load $A_\ell$, $B_\ell$

 \State Set the initial guess $x^0$
 \For {$k=0,1,2,\ldots$ until the global convergence}
 \State Restart outer iteration with $x^0=x^k$
 \State Inner iteration: \Call{InnerSolver}{$x^0$, $k+1$}
 \State Set the initial guess $x^0$
 \For {$k=0,1,2,\ldots$ until the global convergence}
 \State Restart outer iteration with $x^0=x^k$
 \State Inner iteration: \Call{InnerSolver}{$x^0$, $k+1$}

-\State Send shared elements of $X_l^{k+1}$ to neighboring clusters
-\State Receive shared elements in $\{X_m^{k+1}\}_{m\neq l}$
+\State\label{algo:01:send} Send shared elements of $X_\ell^{k+1}$ to neighboring clusters
+\State\label{algo:01:recv} Receive shared elements in $\{X_m^{k+1}\}_{m\neq \ell}$

 \EndFor
 
 \Statex
 
 \Function {InnerSolver}{$x^0$, $k$}
 \EndFor
 
 \Statex
 
 \Function {InnerSolver}{$x^0$, $k$}

-\State Compute local right-hand side $Y_l$: \[Y_l = B_l - \sum\nolimits^L_{\substack{m=1 \\m\neq l}}A_{lm}X_m^0\]
-\State Solving sub-system $A_{ll}X_l^k=Y_l$ with the parallel GMRES method
-\State \Return $X_l^k$
+\State Compute local right-hand side $Y_\ell$:
+       \begin{equation*}
+         Y_\ell = B_\ell - \sum\nolimits^L_{\substack{m=1\\ m\neq \ell}}A_{\ell m}X_m^0
+       \end{equation*}
+\State Solving sub-system $A_{\ell\ell}X_\ell^k=Y_\ell$ with the parallel GMRES method
+\State \Return $X_\ell^k$

 \EndFunction
 \end{algorithmic}
 \caption{A multisplitting solver with GMRES method}
 \EndFunction
 \end{algorithmic}
 \caption{A multisplitting solver with GMRES method}


@@ -297,15 +345,15 @@ Algorithm on Figure~\ref{algo:01} shows the main key points of the
 multisplitting method to solve a large sparse linear system. This algorithm is
 based on an outer-inner iteration method where the parallel synchronous GMRES
 method is used to solve the inner iteration. It is executed in parallel by each
 multisplitting method to solve a large sparse linear system. This algorithm is
 based on an outer-inner iteration method where the parallel synchronous GMRES
 method is used to solve the inner iteration. It is executed in parallel by each

-cluster of processors. For all $l,m\in\{1,\ldots,L\}$, the matrices and vectors
-with the subscript $l$ represent the local data for cluster $l$, while
-$\{A_{lm}\}_{m\neq l}$ are off-diagonal matrices of sparse matrix $A$ and
-$\{X_m\}_{m\neq l}$ contain vector elements of solution $x$ shared with
-neighboring clusters. At every outer iteration $k$, asynchronous communications
-are performed between processors of the local cluster and those of distant
-clusters (lines $6$ and $7$ in Figure~\ref{algo:01}). The shared vector
-elements of the solution $x$ are exchanged by message passing using MPI
-non-blocking communication routines.
+cluster of processors. For all $\ell,m\in\{1,\ldots,L\}$, the matrices and
+vectors with the subscript $\ell$ represent the local data for cluster $\ell$,
+while $\{A_{\ell m}\}_{m\neq \ell}$ are off-diagonal matrices of sparse matrix
+$A$ and $\{X_m\}_{m\neq \ell}$ contain vector elements of solution $x$ shared
+with neighboring clusters. At every outer iteration $k$, asynchronous
+communications are performed between processors of the local cluster and those
+of distant clusters (lines~\ref{algo:01:send} and~\ref{algo:01:recv} in
+Figure~\ref{algo:01}). The shared vector elements of the solution $x$ are
+exchanged by message passing using MPI non-blocking communication routines.

 
 \begin{figure}[!t]
 \centering
 
 \begin{figure}[!t]
 \centering


@@ -314,63 +362,120 @@ non-blocking communication routines.
 \label{fig:4.1}
 \end{figure}
 
 \label{fig:4.1}
 \end{figure}
 

-The global convergence of the asynchronous multisplitting solver is detected when the clusters of processors have all converged locally. We implemented the global convergence detection process as follows. On each cluster a master processor is designated (for example the processor with rank $1$) and masters of all clusters are interconnected by a virtual unidirectional ring network (see Figure~\ref{fig:4.1}). During the resolution, a Boolean token circulates around the virtual ring from a master processor to another until the global convergence is achieved. So starting from the cluster with rank $1$, each master processor $i$ sets the token to {\it True} if the local convergence is achieved or to {\it False} otherwise, and sends it to master processor $i+1$. Finally, the global convergence is detected when the master of cluster $1$ receives from the master of cluster $L$ a token set to {\it True}. In this case, the master of cluster $1$ broadcasts a stop message to masters of other clusters. In this work, the local convergence on each cluster $l$ is detected when the following condition is satisfied
-\[(k\leq \MI) \mbox{~or~} (\|X_l^k - X_l^{k+1}\|_{\infty}\leq\epsilon)\]
-where $\MI$ is the maximum number of outer iterations and $\epsilon$ is the tolerance threshold of the error computed between two successive local solution $X_l^k$ and $X_l^{k+1}$. 
+The global convergence of the asynchronous multisplitting solver is detected
+when the clusters of processors have all converged locally. We implemented the
+global convergence detection process as follows. On each cluster a master
+processor is designated (for example the processor with rank 1) and masters of
+all clusters are interconnected by a virtual unidirectional ring network (see
+Figure~\ref{fig:4.1}). During the resolution, a Boolean token circulates around
+the virtual ring from a master processor to another until the global convergence
+is achieved. So starting from the cluster with rank 1, each master processor $i$
+sets the token to \textit{True} if the local convergence is achieved or to
+\textit{False} otherwise, and sends it to master processor $i+1$. Finally, the
+global convergence is detected when the master of cluster 1 receives from the
+master of cluster $L$ a token set to \textit{True}. In this case, the master of
+cluster 1 broadcasts a stop message to masters of other clusters. In this work,
+the local convergence on each cluster $\ell$ is detected when the following
+condition is satisfied
+\begin{equation*}
+  (k\leq \MI) \text{ or } (\|X_\ell^k - X_\ell^{k+1}\|_{\infty}\leq\epsilon)
+\end{equation*}
+where $\MI$ is the maximum number of outer iterations and $\epsilon$ is the
+tolerance threshold of the error computed between two successive local solution
+$X_\ell^k$ and $X_\ell^{k+1}$.

 
 

-\LZK{Description du processus d'adaptation de l'algo multisplitting à SimGrid}

 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

-We did not encounter major blocking problems when adapting the multisplitting algorithm previously described to a simulation environment like SIMGRID unless some code 
-debugging. Indeed, apart from the review of the program sequence for asynchronous exchanges between the six neighbors of each point in a submatrix within a cluster or 
-between clusters, the algorithm was executed successfully with SMPI and provided identical outputs as those obtained with direct execution under MPI. In synchronous 
+We did not encounter major blocking problems when adapting the multisplitting algorithm previously described to a simulation environment like SimGrid unless some code 
+debugging. Indeed, apart from the review of the program sequence for asynchronous exchanges between processors within a cluster or between clusters, the algorithm was executed successfully with SMPI and provided identical outputs as those obtained with direct execution under MPI. In synchronous 

 mode, the execution of the program raised no particular issue but in asynchronous mode, the review of the sequence of MPI\_Isend, MPI\_Irecv and MPI\_Waitall instructions
 mode, the execution of the program raised no particular issue but in asynchronous mode, the review of the sequence of MPI\_Isend, MPI\_Irecv and MPI\_Waitall instructions

-and with the addition of the primitive MPI\_Test was needed to avoid a memory fault due to an infinite loop resulting from the non-convergence of the algorithm. Note here that the use of SMPI
-functions optimizer for memory footprint and CPU usage is not recommended knowing that one wants to get real results by simulation.
+and with the addition of the primitive MPI\_Test was needed to avoid a memory fault due to an infinite loop resulting from the non-convergence of the algorithm.
+\CER{On voulait en fait montrer la simplicité de l'adaptation de l'algo a SimGrid. Les problèmes rencontrés décrits dans ce paragraphe concerne surtout le mode async}\LZK{OK. J'aurais préféré avoir un peu plus de détails sur l'adaptation de la version async}
+Note here that the use of SMPI functions optimizer for memory footprint and CPU usage is not recommended knowing that one wants to get real results by simulation.

 As mentioned, upon this adaptation, the algorithm is executed as in the real life in the simulated environment after the following minor changes. First, all declared 
 global variables have been moved to local variables for each subroutine. In fact, global variables generate side effects arising from the concurrent access of 
 As mentioned, upon this adaptation, the algorithm is executed as in the real life in the simulated environment after the following minor changes. First, all declared 
 global variables have been moved to local variables for each subroutine. In fact, global variables generate side effects arising from the concurrent access of 

-shared memory used by threads simulating each computing units in the SimGrid architecture. Second, the alignment of certain types of variables such as ``long int'' had
-also to be reviewed. Finally, some compilation errors on MPI\_Waitall and MPI\_Finalize primitives have been fixed with the latest version of SimGrid.
+shared memory used by threads simulating each computing unit in the SimGrid architecture. Second, the alignment of certain types of variables such as ``long int'' had
+also to be reviewed.
+\AG{À propos de ces problèmes d'alignement, en dire plus si ça a un intérêt, ou l'enlever.}
+ Finally, some compilation errors on MPI\_Waitall and MPI\_Finalize primitives have been fixed with the latest version of SimGrid.

 In total, the initial MPI program running on the simulation environment SMPI gave after a very simple adaptation the same results as those obtained in a real 
 In total, the initial MPI program running on the simulation environment SMPI gave after a very simple adaptation the same results as those obtained in a real 

-environment. We have tested in synchronous mode with a simulated platform starting from a modest 2 or 3 clusters grid to a larger configuration like simulating 
-Grid5000 with more than 1500 hosts with 5000 cores~\cite{bolze2006grid}. Once the code debugging and adaptation were complete, the next section shows our methodology and experimental 
-results.
-
-
-
-
+environment. We have successfully executed the code in synchronous mode using parallel GMRES algorithm compared with our multisplitting algorithm in asynchronous mode after few modifications. 

 
 
 
 \section{Experimental results}
 
 
 
 
 \section{Experimental results}
 

-When the \emph{real} application runs in the simulation environment and produces the expected results, varying the input
+When the \textit{real} application runs in the simulation environment and produces the expected results, varying the input

 parameters and the program arguments allows us to compare outputs from the code execution. We have noticed from this
 study that the results depend on the following parameters:  
 parameters and the program arguments allows us to compare outputs from the code execution. We have noticed from this
 study that the results depend on the following parameters:  

-\begin{itemize} 
-\item At the network level, we found that
-the most critical values are the bandwidth (bw) and the network latency (lat).  
-\item Hosts power (GFlops) can also
-influence on the results.  
-\item Finally, when submitting job batches for execution, the arguments values passed to the
-program like the maximum number of iterations or the \emph{external} precision are critical. They allow to ensure not
-only the convergence of the algorithm but also to get the main objective of the experimentation of the simulation in
-having an execution time in asynchronous less than in synchronous mode (i.e. speed-up less than $1$).
+\begin{itemize}
+\item At the network level, we found that the most critical values are the
+  bandwidth and the network latency.
+\item Hosts power (GFlops) can also influence on the results.
+\item Finally, when submitting job batches for execution, the arguments values
+  passed to the program like the maximum number of iterations or the external
+  precision are critical. They allow to ensure not only the convergence of the
+  algorithm but also to get the main objective of the experimentation of the
+  simulation in having an execution time in asynchronous less than in
+  synchronous mode. The ratio between the execution time of asynchronous
+  compared to the synchronous mode is defined as the \emph{relative gain}. So,
+  our objective running the algorithm in SimGrid is to obtain a relative gain
+  greater than 1.
+  \AG{$t_\text{async} / t_\text{sync} > 1$, l'objectif est donc que ça dure plus
+    longtemps (que ça aille moins vite) en asynchrone qu'en synchrone ?
+    Ce n'est pas plutôt l'inverse ?}

 \end{itemize}
 
 \end{itemize}
 

-A priori, obtaining a speedup less than $1$ would be difficult in a local area
-network configuration where the synchronous mode will take advantage on the rapid
-exchange of information on such high-speed links. Thus, the methodology adopted
-was to launch the application on clustered network. In this last configuration,
-degrading the inter-cluster network performance will \emph{penalize} the synchronous
-mode allowing to get a speedup lower than $1$. This action simulates the case of
-clusters linked with long distance network like Internet.
+A priori, obtaining a relative gain greater than 1 would be difficult in a local
+area network configuration where the synchronous mode will take advantage on the
+rapid exchange of information on such high-speed links. Thus, the methodology
+adopted was to launch the application on clustered network. In this last
+configuration, degrading the inter-cluster network performance will penalize the
+synchronous mode allowing to get a relative gain greater than 1.  This action
+simulates the case of distant clusters linked with long distance network like
+Internet.
+
+\AG{Cette partie sur le poisson 3D
+  % on sait donc que ce n'est pas une plie ou une sole (/me fatigué)
+  n'est pas à sa place.  Elle devrait être placée plus tôt.}
+In this paper, we solve the 3D Poisson problem whose the mathematical model is 
+\begin{equation}
+\left\{
+\begin{array}{l}
+\nabla^2 u = f \text{~in~} \Omega \\
+u =0 \text{~on~} \Gamma =\partial\Omega
+\end{array}
+\right.
+\label{eq:02}
+\end{equation}
+where $\nabla^2$ is the Laplace operator, $f$ and $u$ are real-valued functions, and $\Omega=[0,1]^3$. The spatial discretization with a finite difference scheme reduces problem~(\ref{eq:02}) to a system of sparse linear equations. The general iteration scheme of our multisplitting method in a 3D domain using a seven point stencil could be written as 
+\begin{equation}
+\begin{array}{ll}
+u^{k+1}(x,y,z)= & u^k(x,y,z) - \frac{1}{6}\times\\
+               & (u^k(x-1,y,z) + u^k(x+1,y,z) + \\
+               & u^k(x,y-1,z) + u^k(x,y+1,z) + \\
+               & u^k(x,y,z-1) + u^k(x,y,z+1)),
+\end{array}
+\label{eq:03}
+\end{equation} 
+where the iteration matrix $A$ of size $N_x\times N_y\times N_z$ of the discretized linear system is sparse, symmetric and positive definite. 
+
+The parallel solving of the 3D Poisson problem with our multisplitting method requires a data partitioning of the problem between clusters and between processors within a cluster. We have chosen the 3D partitioning instead of the row-by-row partitioning in order to reduce the data exchanges at sub-domain boundaries. Figure~\ref{fig:4.2} shows an example of the data partitioning of the 3D Poisson problem between two clusters of processors, where each sub-problem is assigned to a processor. In this context, a processor has at most six neighbors within a cluster or in distant clusters with which it shares data at sub-domain boundaries. 
+
+\begin{figure}[!t]
+\centering
+  \includegraphics[width=80mm,keepaspectratio]{partition}
+\caption{Example of the 3D data partitioning between two clusters of processors.}
+\label{fig:4.2}
+\end{figure}
+

 
 As a first step, the algorithm was run on a network consisting of two clusters
 
 As a first step, the algorithm was run on a network consisting of two clusters

-containing $50$ hosts each, totaling $100$ hosts. Various combinations of
-the above factors have providing the results shown in
-Table~\ref{tab.cluster.2x50} with a matrix size ranging from $N_x = N_y = N_z =
-62 \text{ to } 171$ elements or from $62^{3} = \np{238328}$ to $171^{3} =
-\np{5211000}$ entries.
+containing 50 hosts each, totaling 100 hosts. Various combinations of the above
+factors have provided the results shown in Table~\ref{tab.cluster.2x50} with a
+matrix size ranging from $N_x = N_y = N_z = \text{62}$ to 171 elements or from
+$\text{62}^\text{3} = \text{\np{238328}}$ to $\text{171}^\text{3} =
+\text{\np{5000211}}$ entries.
+\AG{Expliquer comment lire les tableaux.}

 
 % use the same column width for the following three tables
 \newlength{\mytablew}\settowidth{\mytablew}{\footnotesize\np{E-11}}
 
 % use the same column width for the following three tables
 \newlength{\mytablew}\settowidth{\mytablew}{\footnotesize\np{E-11}}


@@ -382,15 +487,15 @@ Table~\ref{tab.cluster.2x50} with a matrix size ranging from $N_x = N_y = N_z =
 
 \begin{table}[!t]
   \centering
 
 \begin{table}[!t]
   \centering

-  \caption{$2$ clusters, each with $50$ nodes}
+  \caption{2 clusters, each with 50 nodes}

   \label{tab.cluster.2x50}
 
   \begin{mytable}{6}
     \hline
   \label{tab.cluster.2x50}
 
   \begin{mytable}{6}
     \hline

-    bw
+    bandwidth

     & 5         & 5         & 5         & 5         & 5         & 50 \\
     \hline
     & 5         & 5         & 5         & 5         & 5         & 50 \\
     \hline

-    lat
+    latency

     & 0.02      & 0.02      & 0.02      & 0.02      & 0.02      & 0.02 \\
     \hline
     power
     & 0.02      & 0.02      & 0.02      & 0.02      & 0.02      & 0.02 \\
     \hline
     power


@@ -400,21 +505,22 @@ Table~\ref{tab.cluster.2x50} with a matrix size ranging from $N_x = N_y = N_z =
     & 62        & 62        & 62        & 100       & 100       & 110 \\
     \hline
     Prec/Eprec
     & 62        & 62        & 62        & 100       & 100       & 110 \\
     \hline
     Prec/Eprec

-    & \np{E-5}  & \np{E-8}  & \np{E-9}  & \np{E-11} & \np{E-11} & \np{E-11} \\
+    & \np{E-5}   & \np{E-8}  & \np{E-9}  & \np{E-11} & \np{E-11} & \np{E-11} \\
+    \hline

     \hline
     \hline

-    speedup
-    & 0.396     & 0.392     & 0.396     & 0.391     & 0.393     & 0.395 \\
+    Relative gain
+    & 2.52     & 2.55     & 2.52     & 2.57     & 2.54     & 2.53 \\

     \hline
   \end{mytable}
 
     \hline
   \end{mytable}
 

-  \smallskip
+  \bigskip

 
   \begin{mytable}{6}
     \hline
 
   \begin{mytable}{6}
     \hline

-    bw
+    bandwidth

     & 50        & 50        & 50        & 50        & 10        & 10 \\
     \hline
     & 50        & 50        & 50        & 50        & 10        & 10 \\
     \hline

-    lat
+    latency

     & 0.02      & 0.02      & 0.02      & 0.02      & 0.03      & 0.01 \\
     \hline
     power
     & 0.02      & 0.02      & 0.02      & 0.02      & 0.03      & 0.01 \\
     \hline
     power


@@ -426,29 +532,30 @@ Table~\ref{tab.cluster.2x50} with a matrix size ranging from $N_x = N_y = N_z =
     Prec/Eprec
     & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-5}  & \np{E-5} \\
     \hline
     Prec/Eprec
     & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-5}  & \np{E-5} \\
     \hline

-    speedup
-    & 0.398     & 0.388     & 0.393     & 0.394     & 0.63      & 0.778 \\
+    \hline
+    Relative gain
+    & 2.51     & 2.58     & 2.55     & 2.54     & 1.59      & 1.29 \\

     \hline
   \end{mytable}
 \end{table}
   
 Then we have changed the network configuration using three clusters containing
     \hline
   \end{mytable}
 \end{table}
   
 Then we have changed the network configuration using three clusters containing

-respectively $33$, $33$ and $34$ hosts, or again by on hundred hosts for all the
+respectively 33, 33 and 34 hosts, or again by on hundred hosts for all the

 clusters. In the same way as above, a judicious choice of key parameters has
 permitted to get the results in Table~\ref{tab.cluster.3x33} which shows the
 clusters. In the same way as above, a judicious choice of key parameters has
 permitted to get the results in Table~\ref{tab.cluster.3x33} which shows the

-speedups less than $1$ with a matrix size from $62$ to $100$ elements.
+relative gains greater than 1 with a matrix size from 62 to 100 elements.

 
 \begin{table}[!t]
   \centering
 
 \begin{table}[!t]
   \centering

-  \caption{$3$ clusters, each with $33$ nodes}
+  \caption{3 clusters, each with 33 nodes}

   \label{tab.cluster.3x33}
 
   \begin{mytable}{6}
     \hline
   \label{tab.cluster.3x33}
 
   \begin{mytable}{6}
     \hline

-    bw
+    bandwidth

     & 10       & 5        & 4        & 3        & 2        & 6 \\
     \hline
     & 10       & 5        & 4        & 3        & 2        & 6 \\
     \hline

-    lat
+    latency

     & 0.01     & 0.02     & 0.02     & 0.02     & 0.02     & 0.02 \\
     \hline
     power
     & 0.01     & 0.02     & 0.02     & 0.02     & 0.02     & 0.02 \\
     \hline
     power


@@ -460,8 +567,9 @@ speedups less than $1$ with a matrix size from $62$ to $100$ elements.
     Prec/Eprec
     & \np{E-5} & \np{E-5} & \np{E-5} & \np{E-5} & \np{E-5} & \np{E-5} \\
     \hline
     Prec/Eprec
     & \np{E-5} & \np{E-5} & \np{E-5} & \np{E-5} & \np{E-5} & \np{E-5} \\
     \hline

-    speedup
-    & 0.997    & 0.99     & 0.93     & 0.84     & 0.78     & 0.99 \\
+    \hline
+    Relative gain
+    & 1.003    & 1.01     & 1.08     & 1.19     & 1.28     & 1.01 \\

     \hline
   \end{mytable}
 \end{table}
     \hline
   \end{mytable}
 \end{table}


@@ -477,9 +585,9 @@ Table~\ref{tab.cluster.3x67}.
 
   \begin{mytable}{1}
     \hline
 
   \begin{mytable}{1}
     \hline

-    bw         & 1 \\
+    bandwidth  & 1 \\

     \hline
     \hline

-    lat        & 0.02 \\
+    latency    & 0.02 \\

     \hline
     power      & 1 \\
     \hline
     \hline
     power      & 1 \\
     \hline


@@ -487,20 +595,22 @@ Table~\ref{tab.cluster.3x67}.
     \hline
     Prec/Eprec & \np{E-5} \\
     \hline
     \hline
     Prec/Eprec & \np{E-5} \\
     \hline

-    speedup    & 0.9 \\

     \hline
     \hline

- \end{mytable}
+    Relative gain    & 1.11 \\
+    \hline
+  \end{mytable}

 \end{table}
 
 Note that the program was run with the following parameters:
 
 \paragraph*{SMPI parameters}
 
 \end{table}
 
 Note that the program was run with the following parameters:
 
 \paragraph*{SMPI parameters}
 

+~\\{}\AG{Donner un peu plus de précisions (plateforme en particulier).}

 \begin{itemize}
 \begin{itemize}

-       \item HOSTFILE: Hosts file description.
-       \item PLATFORM: file description of the platform architecture : clusters (CPU power,
-\dots{}), intra cluster network description, inter cluster network (bandwidth bw,
-lat latency, \dots{}).
+\item HOSTFILE: Hosts file description.
+\item PLATFORM: file description of the platform architecture : clusters (CPU
+  power, \dots{}), intra cluster network description, inter cluster network
+  (bandwidth, latency, \dots{}).

 \end{itemize}
 
 
 \end{itemize}
 
 


@@ -512,6 +622,7 @@ lat latency, \dots{}).
        \item Internal and external precisions;
        \item Matrix size $N_x$, $N_y$ and $N_z$;
        \item Matrix diagonal value: \np{6.0};
        \item Internal and external precisions;
        \item Matrix size $N_x$, $N_y$ and $N_z$;
        \item Matrix diagonal value: \np{6.0};

+       \item Matrix off-diagonal value: \np{-1.0};

        \item Execution Mode: synchronous or asynchronous.
 \end{itemize}
 
        \item Execution Mode: synchronous or asynchronous.
 \end{itemize}
 


@@ -520,7 +631,7 @@ lat latency, \dots{}).
 After analyzing the outputs, generally, for the configuration with two or three
 clusters including one hundred hosts (Tables~\ref{tab.cluster.2x50}
 and~\ref{tab.cluster.3x33}), some combinations of the used parameters affecting
 After analyzing the outputs, generally, for the configuration with two or three
 clusters including one hundred hosts (Tables~\ref{tab.cluster.2x50}
 and~\ref{tab.cluster.3x33}), some combinations of the used parameters affecting

-the results have given a speedup less than 1, showing the effectiveness of the
+the results have given a relative gain more than 2.5, showing the effectiveness of the

 asynchronous performance compared to the synchronous mode.
 
 In the case of a two clusters configuration, Table~\ref{tab.cluster.2x50} shows
 asynchronous performance compared to the synchronous mode.
 
 In the case of a two clusters configuration, Table~\ref{tab.cluster.2x50} shows


@@ -529,28 +640,33 @@ bandwidth, a latency in order of a hundredth of a millisecond and a system power
 of one GFlops, an efficiency of about \np[\%]{40} in asynchronous mode is
 obtained for a matrix size of 62 elements. It is noticed that the result remains
 stable even if we vary the external precision from \np{E-5} to \np{E-9}. By
 of one GFlops, an efficiency of about \np[\%]{40} in asynchronous mode is
 obtained for a matrix size of 62 elements. It is noticed that the result remains
 stable even if we vary the external precision from \np{E-5} to \np{E-9}. By

-increasing the problem size up to $100$ elements, it was necessary to increase the
+increasing the matrix size up to 100 elements, it was necessary to increase the

 CPU power of \np[\%]{50} to \np[GFlops]{1.5} for a convergence of the algorithm
 with the same order of asynchronous mode efficiency.  Maintaining such a system
 power but this time, increasing network throughput inter cluster up to
 CPU power of \np[\%]{50} to \np[GFlops]{1.5} for a convergence of the algorithm
 with the same order of asynchronous mode efficiency.  Maintaining such a system
 power but this time, increasing network throughput inter cluster up to

-\np[Mbit/s]{50}, the result of efficiency of about \np[\%]{40} is obtained with
-high external precision of \np{E-11} for a matrix size from $110$ to $150$ side
+\np[Mbit/s]{50}, the result of efficiency with a relative gain of 1.5\AG[]{2.5 ?} is obtained with
+high external precision of \np{E-11} for a matrix size from 110 to 150 side

 elements.
 
 elements.
 

-For the $3$ clusters architecture including a total of 100 hosts,
+For the 3 clusters architecture including a total of 100 hosts,

 Table~\ref{tab.cluster.3x33} shows that it was difficult to have a combination
 Table~\ref{tab.cluster.3x33} shows that it was difficult to have a combination

-which gives an efficiency of asynchronous below \np[\%]{80}. Indeed, for a
-matrix size of $62$ elements, equality between the performance of the two modes
+which gives a relative gain of asynchronous mode more than 1.2. Indeed, for a
+matrix size of 62 elements, equality between the performance of the two modes

 (synchronous and asynchronous) is achieved with an inter cluster of
 (synchronous and asynchronous) is achieved with an inter cluster of

-\np[Mbit/s]{10} and a latency of \np[ms]{E-1}. To challenge an efficiency by
-\np[\%]{78} with a matrix size of $100$ points, it was necessary to degrade the
+\np[Mbit/s]{10} and a latency of \np[ms]{E-1}. To challenge an efficiency greater than 1.2 with a matrix size of 100 points, it was necessary to degrade the

 inter cluster network bandwidth from 5 to \np[Mbit/s]{2}.
 inter cluster network bandwidth from 5 to \np[Mbit/s]{2}.

+\AG{Conclusion, on prend une plateforme pourrie pour avoir un bon ratio sync/async ???
+  Quelle est la perte de perfs en faisant ça ?}

 
 A last attempt was made for a configuration of three clusters but more powerful
 
 A last attempt was made for a configuration of three clusters but more powerful

-with 200 nodes in total. The convergence with a speedup of \np[\%]{90} was
+with 200 nodes in total. The convergence with a relative gain around 1.1 was

 obtained with a bandwidth of \np[Mbit/s]{1} as shown in
 Table~\ref{tab.cluster.3x67}.
 
 obtained with a bandwidth of \np[Mbit/s]{1} as shown in
 Table~\ref{tab.cluster.3x67}.
 

+\RC{Est ce qu'on sait expliquer pourquoi il y a une telle différence entre les résultats avec 2 et 3 clusters... Avec 3 clusters, ils sont pas très bons... Je me demande s'il ne faut pas les enlever...}
+\RC{En fait je pense avoir la réponse à ma remarque... On voit avec les 2 clusters que le gain est d'autant plus grand qu'on choisit une bonne précision. Donc, plusieurs solutions, lancer rapidement un long test pour confirmer ca, ou enlever des tests... ou on ne change rien :-)}
+\LZK{Ma question est: le bandwidth et latency sont ceux inter-clusters ou pour les deux inter et intra cluster??}
+

 \section{Conclusion}
 The experimental results on executing a parallel iterative algorithm in 
 asynchronous mode on an environment simulating a large scale of virtual 
 \section{Conclusion}
 The experimental results on executing a parallel iterative algorithm in 
 asynchronous mode on an environment simulating a large scale of virtual 


@@ -579,12 +695,13 @@ demonstrated an original solution to optimize the use of a simulation
 tool to run efficiently an iterative parallel algorithm in asynchronous 
 mode in a grid architecture. 
 
 tool to run efficiently an iterative parallel algorithm in asynchronous 
 mode in a grid architecture. 
 

+\LZK{Perspectives???}
+

 \section*{Acknowledgment}
 
 This work is partially funded by the Labex ACTION program (contract ANR-11-LABX-01-01).
 \todo[inline]{The authors would like to thank\dots{}}
 
 \section*{Acknowledgment}
 
 This work is partially funded by the Labex ACTION program (contract ANR-11-LABX-01-01).
 \todo[inline]{The authors would like to thank\dots{}}
 

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@@ -592,6 +709,8 @@ This work is partially funded by the Labex ACTION program (contract ANR-11-LABX-
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@@ -607,3 +726,5 @@ This work is partially funded by the Labex ACTION program (contract ANR-11-LABX-
 % LocalWords:  Parallelization AIAC GMRES multi SMPI SISC SIAC SimDAG DAGs Lua
 % LocalWords:  Fortran GFlops priori Mbit de du fcomte multisplitting scalable
 % LocalWords:  SimGrid Belfort parallelize Labex ANR LABX IEEEabrv hpccBib
 % LocalWords:  Parallelization AIAC GMRES multi SMPI SISC SIAC SimDAG DAGs Lua
 % LocalWords:  Fortran GFlops priori Mbit de du fcomte multisplitting scalable
 % LocalWords:  SimGrid Belfort parallelize Labex ANR LABX IEEEabrv hpccBib

+% LocalWords:  intra durations nonsingular Waitall discretization discretized
+% LocalWords:  InnerSolver Isend Irecv