Use the same column width for each table.

[hpcc2014.git] / hpcc.tex

diff --git a/hpcc.tex b/hpcc.tex
index 75c02f5940435f88e29a461abcc8fd67de023796..dd06eebc905d58734f2d3d5c16022c85850180be 100644 (file)
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -40,11 +40,6 @@
 
 \newcommand{\MI}{\mathit{MaxIter}}
 
 
 \newcommand{\MI}{\mathit{MaxIter}}
 

-\usepackage{array}
-\usepackage{color, colortbl}
-\newcolumntype{M}[1]{>{\centering\arraybackslash}m{#1}}
-\newcolumntype{Z}[1]{>{\raggedleft}m{#1}}
-

 \begin{document}
 
 \title{Simulation of Asynchronous Iterative Numerical Algorithms Using SimGrid}
 \begin{document}
 
 \title{Simulation of Asynchronous Iterative Numerical Algorithms Using SimGrid}


@@ -73,7 +68,7 @@
 
 \maketitle
 
 
 \maketitle
 

-\RC{Ordre des autheurs pas définitif.}
+\RC{Ordre des auteurs pas définitif.}

 \begin{abstract}
 In recent years, the scalability of large-scale implementation in a 
 distributed environment of algorithms becoming more and more complex has 
 \begin{abstract}
 In recent years, the scalability of large-scale implementation in a 
 distributed environment of algorithms becoming more and more complex has 


@@ -110,71 +105,81 @@ 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 \texttt{numerical iterative algorithms} executed in a distributed environment. As their name
-suggests, these algorithm solves a given problem by successive iterations ($X_{n +1} = f(X_{n})$) from an initial value
+parallel algorithms called \emph{numerical 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
 $X_{0}$ 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{}. 
+demonstrate the convergence of these algorithms \cite{BT89,Bahi07}. 

 
 

-Parallelization of such algorithms generally involved the division of the problem into several \emph{pieces} that will
+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
 be solved in parallel on multiple processing units. The latter will communicate each intermediate results before a new

-iteration starts  until the approximate solution is reached. These parallel  computations can be performed
-either in \emph{synchronous} communication mode where a new iteration begin only when all nodes communications are
-completed, either \emph{asynchronous} mode where processors can continue independently without or few synchronization
-points. 
-
-% DL : reprendre correction ici 
-Despite the effectiveness of iterative approach, a major drawback of the method is  the requirement of huge
-resources in terms of computing capacity,  storage and high speed communication network. Indeed, limited physical
-resources are blocking factors for large-scale deployment of parallel algorithms.
-
-In recent years, 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. According our knowledge, no testing of large-scale
-simulation of the class of algorithm solving to achieve real results has  been undertaken to date. We had in the scope
-of this work implemented a  program for solving large non-symmetric linear system of equations by  numerical method
-GMRES (Generalized Minimal Residual) in the simulation environment SimGrid. The simulated platform 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. In addition, it has been  permitted to show the
-effectiveness of asynchronous mode algorithm by  comparing its performance with the synchronous mode time. 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.
+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
+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
 
 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 will be presented with the settings to create
-various distributed architectures. The algorithm of  the multi-splitting method used by GMRES written with MPI
-primitives and its adaptation to SimGrid with SMPI (Simulated MPI) will be in the next section. At last, the experiments
-results carried out will be presented before the conclusion which we will announce the opening of  our future work after
-the results.
+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.

  
 \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{bcvc02:ip}). 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
 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
+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
 i.e.  without stopping current computations. This technique allows to partially overlap communications by computations
 but unfortunately, the overlapping is only partial and important idle times remain.  It is clear that, in a grid
 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
 (SIAC)} model can be compared to the previous one except that data required on another processor are sent asynchronously
 i.e.  without stopping current computations. This technique allows to partially overlap communications by computations
 but unfortunately, the overlapping is only partial and important idle times remain.  It is clear that, in a grid
 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
+\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
 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 grey blocks represent the computation phases, the white spaces the idle
+\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
 algorithms can significantly reduce overall execution times by suppressing idle times due to synchronizations especially
 in a grid computing context.
 
 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
 algorithms can significantly reduce overall execution times by suppressing idle times due to synchronizations especially
 in a grid computing context.
 

-\begin{figure}[htbp]
+\begin{figure}[!t]

   \centering
     \includegraphics[width=8cm]{AIAC.pdf}
   \centering
     \includegraphics[width=8cm]{AIAC.pdf}

-  \caption{The Asynchronous Iterations - Asynchronous Communications model } 
+  \caption{The Asynchronous Iterations~-- Asynchronous Communications model}

   \label{fig:aiac}
 \end{figure}
 
   \label{fig:aiac}
 \end{figure}
 


@@ -188,7 +193,7 @@ simulation tools to explore various platform scenarios at will and to run enormo
 very promising. Several works...
 
 In the context of AIAC algorithms, the use of simulation tools is even more relevant. Indeed, this class of applications
 very promising. Several works...
 
 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 bandwith (intra and
+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.
 
 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.
 


@@ -197,19 +202,21 @@ iterations and so to very different execution times.
 
 \section{SimGrid}
 
 
 \section{SimGrid}
 

-SimGrid~\cite{casanova+legrand+quinson.2008.simgrid,SimGrid} is a simulation
-framework to sudy the behavior of large-scale distributed systems.  As its name
+SimGrid~\cite{SimGrid,casanova+legrand+quinson.2008.simgrid} is a simulation
+framework to study the behavior of large-scale distributed systems.  As its name

 says, it emanates from the grid computing community, but is nowadays used to
 says, it emanates from the grid computing community, but is nowadays used to

-study grids, clouds, HPC or peer-to-peer systems.
-%- open source, developped since 1999, one of the major solution in the field
-%
+study grids, clouds, HPC or peer-to-peer systems.  The early versions of SimGrid
+date from 1999, but it's still actively developed and distributed as an open
+source software.  Today, it's one of the major generic tools in the field of
+simulation for large-scale distributed systems.
+

 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
 languages.  The SMPI interface supports applications written in C or Fortran,
 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
 languages.  The SMPI interface supports applications written in C or Fortran,

-with little or no modifications.
-%- implements most of MPI-2 \cite{ref} standard [CHECK]
+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
 
 %%% explain simulation
 %- simulated processes folded in one real process


@@ -223,12 +230,10 @@ with little or no modifications.
 
 %%% validation + refs
 
 
 %%% validation + refs
 

-\AG{Décrire SimGrid~\cite{casanova+legrand+quinson.2008.simgrid,SimGrid} (Arnaud)}
-

 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \section{Simulation of the multisplitting method}
 %Décrire le problème (algo) traité ainsi que le processus d'adaptation à SimGrid.
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \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. In this case, we apply a row-by-row splitting without overlapping  
+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} \\
 \[
 \left(\begin{array}{ccc}
 A_{11} & \cdots & A_{1L} \\


@@ -261,7 +266,7 @@ Y_l = B_l - \displaystyle\sum_{\substack{m=1\\ m\neq l}}^{L}A_{lm}X_m
 \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. 
 
 \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. 
 

-\begin{figure}
+\begin{figure}[!t]

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


@@ -302,7 +307,7 @@ 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.
 
 elements of the solution $x$ are exchanged by message passing using MPI
 non-blocking communication routines.
 

-\begin{figure}
+\begin{figure}[!t]

 \centering
   \includegraphics[width=60mm,keepaspectratio]{clustering}
 \caption{Example of three clusters of processors interconnected by a virtual unidirectional ring network.}
 \centering
   \includegraphics[width=60mm,keepaspectratio]{clustering}
 \caption{Example of three clusters of processors interconnected by a virtual unidirectional ring network.}


@@ -315,7 +320,20 @@ where $\MI$ is the maximum number of outer iterations and $\epsilon$ is the tole
 
 \LZK{Description du processus d'adaptation de l'algo multisplitting à SimGrid}
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 \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 
+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.
+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.
+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.

 
 
 
 
 
 


@@ -325,104 +343,154 @@ where $\MI$ is the maximum number of outer iterations and $\epsilon$ is the tole
 
 \section{Experimental results}
 
 
 \section{Experimental results}
 

-When the \emph{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: (1) at the network
-level, we found that the most critical values are the bandwidth (bw) and the
-network latency (lat). (2) Hosts power (GFlops) can also influence on the
-results. And finally, (3) 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 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, in others words, in having a \emph{speedup} less than 1
-({speedup}${}={}${execution time in synchronous mode}${}/{}${execution time in
-asynchronous mode}).
-
-A priori, obtaining a speedup less than 1 would be difficult in a local area
+When the \emph{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:  
+\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 $1$).
+\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
 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
+mode allowing to get a speedup lower than $1$. This action simulates the case of

 clusters linked with long distance network like Internet.
 
 As a first step, the algorithm was run on a network consisting of two clusters
 clusters linked with long distance network like Internet.
 
 As a first step, the algorithm was run on a network consisting of two clusters

-containing fifty hosts each, totaling one hundred hosts. Various combinations of
-the above factors have providing the results shown in Table I 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.
-
-\begin{table}[h!]
- \centering
-
- \tiny
- 
-\begin{tabular}{|Z{0.55cm}|Z{0.25cm}|Z{0.25cm}|M{0.25cm}|Z{0.25cm}|M{0.25cm}|M{0.25cm}|M{0.25cm}|M{0.25cm}|M{0.25cm}|M{0.25cm}|M{0.25cm}|M{0.25cm}|M{0.25cm}|} 
- \hline 
- \bf bw & 5 &5 & 5 & 5 & 5 & 50 & 50 & 50 & 50 & 50 & 10 & 10\\ 
- \hline
- \bf lat & 0.02 & 0.02 & 0.02 & 0.02 & 0.02 & 0.02 & 0.02 & 0.02 & 0.02 & 0.02 & 0.03 & 0.01\\ 
- \hline 
- \bf power & 1 & 1 & 1 & 1.5 & 1.5 & 1.5 & 1.5 & 1.5 & 1.5 & 1.5 & 1 & 1.5\\ \hline    \bf size & 62 & 62 & 62 & 100 & 100 & 110 & 120& 130 & 140 & 150 & 171 & 171\\ \hline
- \bf Prec/Eprec & 10$^{-5}$ & 10$^{-8}$ & 10$^{-9}$ & 10$^{-11}$ & 10$^{-11}$ & 10$^{-11}$ & 10$^{-11}$ & 10$^{-11}$ & 10$^{-11}$ & 10$^{-11}$ & 10$^{-5}$ & 10$^{-5}$\\ \hline 
- \bf speedup & 0.396 & 0.392 & 0.396 & 0.391 & 0.393 & 0.395 & 0.398 & 0.388 & 0.393 & 0.394 & 0.63 & 0.778\\ \hline 
- \end{tabular}
- \smallskip 
- \caption{2 Clusters x 50 nodes each} \label{tab1} 
-\end{table} 
+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.
+
+% use the same column width for the following three tables
+\newlength{\mytablew}\settowidth{\mytablew}{\footnotesize\np{E-11}}
+\newenvironment{mytable}[1]{% #1: number of columns for data
+  \renewcommand{\arraystretch}{1.3}%
+  \begin{tabular}{|>{\bfseries}r%
+                  |*{#1}{>{\centering\arraybackslash}p{\mytablew}|}}}{%
+    \end{tabular}}
+
+\begin{table}[!t]
+  \centering
+  \caption{$2$ clusters, each with $50$ nodes}
+  \label{tab.cluster.2x50}
+
+  \begin{mytable}{6}
+    \hline
+    bw
+    & 5         & 5         & 5         & 5         & 5         & 50 \\
+    \hline
+    lat
+    & 0.02      & 0.02      & 0.02      & 0.02      & 0.02      & 0.02 \\
+    \hline
+    power
+    & 1         & 1         & 1         & 1.5       & 1.5       & 1.5 \\
+    \hline
+    size
+    & 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} \\
+    \hline
+    speedup
+    & 0.396     & 0.392     & 0.396     & 0.391     & 0.393     & 0.395 \\
+    \hline
+  \end{mytable}
+
+  \smallskip
+
+  \begin{mytable}{6}
+    \hline
+    bw
+    & 50        & 50        & 50        & 50        & 10        & 10 \\
+    \hline
+    lat
+    & 0.02      & 0.02      & 0.02      & 0.02      & 0.03      & 0.01 \\
+    \hline
+    power
+    & 1.5       & 1.5       & 1.5       & 1.5       & 1         & 1.5 \\
+    \hline
+    size
+    & 120       & 130       & 140       & 150       & 171       & 171 \\
+    \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
+  \end{mytable}
+\end{table}

   
 Then we have changed the network configuration using three clusters containing
   
 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
 clusters. In the same way as above, a judicious choice of key parameters has

-permitted to get the results in Table II which shows the speedups less than 1 with
-a matrix size from 62 to 100 elements.
-
-\begin{table}[h!]
- \centering
-
- \tiny
- 
-\begin{tabular}{|Z{0.55cm}|Z{0.25cm}|Z{0.25cm}|M{0.25cm}|Z{0.25cm}|M{0.25cm}|M{0.25cm}|} 
- \hline 
- \bf bw & 10 &5 & 4 & 3 & 2 & 6\\ \hline
- \bf lat & 0.01 & 0.02 & 0.02 & 0.02 & 0.02 & 0.02\\ 
- \hline 
- \bf power & 1 & 1 & 1 & 1 & 1 & 1\\ \hline    
- \bf size & 62 & 100 & 100 & 100 & 100 & 171\\ \hline
- \bf Prec/Eprec & 10$^{-5}$ & 10$^{-5}$ & 10$^{-5}$ & 10$^{-5}$ & 10$^{-5}$ & 10$^{-5}$\\ \hline 
- \bf speedup & 0.997 & 0.99 & 0.93 & 0.84 & 0.78 & 0.99\\ 
- \hline 
- \end{tabular}
- \smallskip 
- \caption{3 Clusters x 33 nodes each} \label{tab2} 
-\end{table} 
+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.

 
 

+\begin{table}[!t]
+  \centering
+  \caption{$3$ clusters, each with $33$ nodes}
+  \label{tab.cluster.3x33}
+
+  \begin{mytable}{6}
+    \hline
+    bw
+    & 10       & 5        & 4        & 3        & 2        & 6 \\
+    \hline
+    lat
+    & 0.01     & 0.02     & 0.02     & 0.02     & 0.02     & 0.02 \\
+    \hline
+    power
+    & 1        & 1        & 1        & 1        & 1        & 1 \\
+    \hline
+    size
+    & 62       & 100      & 100      & 100      & 100      & 171 \\
+    \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
+  \end{mytable}
+\end{table}

 
 In a final step, results of an execution attempt to scale up the three clustered
 
 In a final step, results of an execution attempt to scale up the three clustered

-configuration but increasing by two hundreds hosts has been recorded in Table III.
-
-\begin{table}[h!]
- \centering
- \tiny
-\begin{tabular}{|M{0.55cm}|M{0.25cm}|} 
- \hline 
- \bf bw & 1\\ \hline
- \bf lat & 0.02\\ 
- \hline 
- \bf power & 1\\ 
- \hline    
- \bf size & 62\\ 
- \hline
- \bf Prec/Eprec & 10$^{-5}$\\ 
- \hline 
- \bf speedup & 0.9\\ 
- \hline 
- \end{tabular}
- \smallskip 
- \caption{3 Clusters x 66 nodes each} \label{tab3} 
-\end{table} 
+configuration but increasing by two hundreds hosts has been recorded in
+Table~\ref{tab.cluster.3x67}.
+
+\begin{table}[!t]
+  \centering
+  \caption{3 clusters, each with 66 nodes}
+  \label{tab.cluster.3x67}
+
+  \begin{mytable}{1}
+    \hline
+    bw         & 1 \\
+    \hline
+    lat        & 0.02 \\
+    \hline
+    power      & 1 \\
+    \hline
+    size       & 62 \\
+    \hline
+    Prec/Eprec & \np{E-5} \\
+    \hline
+    speedup    & 0.9 \\
+    \hline
+ \end{mytable}
+\end{table}

 
 Note that the program was run with the following parameters:
 
 
 Note that the program was run with the following parameters:
 


@@ -447,39 +515,41 @@ lat latency, \dots{}).
        \item Execution Mode: synchronous or asynchronous.
 \end{itemize}
 
        \item Execution Mode: synchronous or asynchronous.
 \end{itemize}
 

-

 \paragraph*{Interpretations and comments}
 
 After analyzing the outputs, generally, for the configuration with two or three
 \paragraph*{Interpretations and comments}
 
 After analyzing the outputs, generally, for the configuration with two or three

-clusters including one hundred hosts (Table I and II), some combinations of the
-used parameters affecting the results have given a speedup less than 1, showing
-the effectiveness of the asynchronous performance compared to the synchronous
-mode.
-
-In the case of a two clusters configuration, Table I shows that with a
-deterioration of inter cluster network set with \np[Mbits/s]{5} of 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 increasing the problem 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 \np[Mbits/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 elements.
-
-For the 3 clusters architecture including a total of 100 hosts, Table II 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 (synchronous and asynchronous) is
-achieved with an inter cluster of \np[Mbits/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 inter cluster network bandwidth from 5 to 2 Mbit/s.
+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
+asynchronous performance compared to the synchronous mode.
+
+In the case of a two clusters configuration, Table~\ref{tab.cluster.2x50} shows
+that with a deterioration of inter cluster network set with \np[Mbit/s]{5} of
+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
+increasing the problem 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
+\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
+elements.
+
+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
+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
+(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
+inter cluster network bandwidth from 5 to \np[Mbit/s]{2}.

 
 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 obtained
-with a bandwidth of \np[Mbits/s]{1} as shown in Table III.
+with 200 nodes in total. The convergence with a speedup of \np[\%]{90} was
+obtained with a bandwidth of \np[Mbit/s]{1} as shown in
+Table~\ref{tab.cluster.3x67}.

 
 \section{Conclusion}
 The experimental results on executing a parallel iterative algorithm in 
 
 \section{Conclusion}
 The experimental results on executing a parallel iterative algorithm in 


@@ -488,17 +558,15 @@ computers organized with interconnected clusters have been presented.
 Our work has demonstrated that using such a simulation tool allow us to 
 reach the following three objectives: 
 
 Our work has demonstrated that using such a simulation tool allow us to 
 reach the following three objectives: 
 

-\newcounter{numberedCntD}

 \begin{enumerate}
 \item To have a flexible configurable execution platform resolving the 
 hard exercise to access to very limited but so solicited physical 
 resources;
 \begin{enumerate}
 \item To have a flexible configurable execution platform resolving the 
 hard exercise to access to very limited but so solicited physical 
 resources;

-\item to ensure the algorithm convergence with a raisonnable time and 
+\item to ensure the algorithm convergence with a reasonable time and

 iteration number ;
 \item and finally and more importantly, to find the correct combination 
 of the cluster and network specifications permitting to save time in 
 executing the algorithm in asynchronous mode.
 iteration number ;
 \item and finally and more importantly, to find the correct combination 
 of the cluster and network specifications permitting to save time in 
 executing the algorithm in asynchronous mode.

-\setcounter{numberedCntD}{\theenumi}

 \end{enumerate}
 Our results have shown that in certain conditions, asynchronous mode is 
 speeder up to \np[\%]{40} than executing the algorithm in synchronous mode
 \end{enumerate}
 Our results have shown that in certain conditions, asynchronous mode is 
 speeder up to \np[\%]{40} than executing the algorithm in synchronous mode


@@ -513,8 +581,8 @@ mode in a grid architecture.
 
 \section*{Acknowledgment}
 
 
 \section*{Acknowledgment}
 

-
-The authors would like to thank\dots{}
+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{}}

 
 
 % trigger a \newpage just before the given reference
 
 
 % trigger a \newpage just before the given reference


@@ -532,3 +600,10 @@ The authors would like to thank\dots{}
 %%% fill-column: 80
 %%% ispell-local-dictionary: "american"
 %%% End:
 %%% fill-column: 80
 %%% ispell-local-dictionary: "american"
 %%% End:

+
+% LocalWords:  Ramamonjisoa Laiymani Arnaud Giersch Ziane Khodja Raphaël Femto
+% LocalWords:  Université Franche Comté IUT Montbéliard Maréchal Juin Inria Sud
+% LocalWords:  Ouest Vieille Talence cedex scalability experimentations HPC MPI
+% 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