correct simgrid

[rce2015.git] / paper.tex

diff --git a/paper.tex b/paper.tex
index 31eb0e89348ae7f0c3e7e0b449b81b5aedc7ff2f..4d7c9b842a58a99b89c335f37162bea9fb06be7f 100644 (file)
--- a/paper.tex
+++ b/paper.tex
@@ -77,10 +77,10 @@
 %\itshape{\journalnamelc}\footnotemark[2]}
 
 \author{Charles Emile Ramamonjisoa\affil{1},
 %\itshape{\journalnamelc}\footnotemark[2]}
 
 \author{Charles Emile Ramamonjisoa\affil{1},

-    David Laiymani\affil{1},
-    Arnaud Giersch\affil{1},
-    Lilia Ziane Khodja\affil{2} and
-    Raphaël Couturier\affil{1}
+  Lilia Ziane Khodja\affil{2},
+  David Laiymani\affil{1},
+  Raphaël Couturier\affil{1} and
+  Arnaud Giersch\affil{1}

 }
 
 \address{
 }
 
 \address{


@@ -154,7 +154,7 @@ iteration without having to wait for the data dependencies coming from its
 neighbors. Both communications and computations are \textit{asynchronous}
 inducing that there is no more idle time, due to synchronizations, between two
 iterations~\cite{bcvc06:ij}. This model presents some advantages and drawbacks
 neighbors. Both communications and computations are \textit{asynchronous}
 inducing that there is no more idle time, due to synchronizations, between two
 iterations~\cite{bcvc06:ij}. This model presents some advantages and drawbacks

-that we detail in Section~\ref{sec:asynchro} but even if the number of
+that we detail in Section~\ref{sec:asynchro}. Even if the number of

 iterations required to converge is generally  greater  than for the synchronous
 case, it appears that the asynchronous  iterative scheme  can significantly
 reduce  overall execution times by  suppressing idle  times due to
 iterations required to converge is generally  greater  than for the synchronous
 case, it appears that the asynchronous  iterative scheme  can significantly
 reduce  overall execution times by  suppressing idle  times due to


@@ -181,7 +181,7 @@ multisplitting  algorithm   with  the   GMRES  (Generalized   Minimal  RESidual)
 solver~\cite{saad86} in  synchronous mode.  The simulation  results allow  us to
 determine  which method  to choose  for a given multi-core  architecture.
 Moreover the  obtained results  on different simulated  multi-core architectures
 solver~\cite{saad86} in  synchronous mode.  The simulation  results allow  us to
 determine  which method  to choose  for a given multi-core  architecture.
 Moreover the  obtained results  on different simulated  multi-core architectures

-confirm the  real results  previously obtained  on non  simulated architectures.
+confirm the  real results  previously obtained  on real physical architectures.

 More precisely the simulated results are in accordance (i.e. with the same order
 of magnitude)  with the works  presented in~\cite{couturier15}, which  show that
 the synchronous  Krylov multisplitting method  is more efficient  than GMRES  for large
 More precisely the simulated results are in accordance (i.e. with the same order
 of magnitude)  with the works  presented in~\cite{couturier15}, which  show that
 the synchronous  Krylov multisplitting method  is more efficient  than GMRES  for large


@@ -209,7 +209,7 @@ concluding remarks and perspectives.
 Asynchronous iterative methods have been  studied for many years theoretically and
 practically. Many methods have been considered and convergence results have been
 proved. These  methods can  be used  to solve, in  parallel, fixed  point problems
 Asynchronous iterative methods have been  studied for many years theoretically and
 practically. Many methods have been considered and convergence results have been
 proved. These  methods can  be used  to solve, in  parallel, fixed  point problems

-(i.e. problems  for which  the solution is  $x^\star =f(x^\star)$.  In practice,
+(i.e. problems  for which  the solution is  $x^\star =f(x^\star)$).  In practice,

 asynchronous iteration  methods can be used  to solve, for example,  linear and
 non-linear systems of equations or optimization problems, interested readers are
 invited to read~\cite{BT89,bahi07}.
 asynchronous iteration  methods can be used  to solve, for example,  linear and
 non-linear systems of equations or optimization problems, interested readers are
 invited to read~\cite{BT89,bahi07}.


@@ -219,8 +219,8 @@ studied. Otherwise, the  application is not ensure to reach  the convergence. An
 algorithm that supports both the synchronous or the asynchronous iteration model
 requires very few modifications  to be able to be executed  in both variants. In
 practice, only  the communications and  convergence detection are  different. In
 algorithm that supports both the synchronous or the asynchronous iteration model
 requires very few modifications  to be able to be executed  in both variants. In
 practice, only  the communications and  convergence detection are  different. In

-the synchronous  mode, iterations are  synchronized whereas in  the asynchronous
-one, they are not.  It should be noticed that non-blocking communications can be
+the synchronous  mode iterations are  synchronized, whereas in  the asynchronous
+one they are not.  It should be noticed that non-blocking communications can be

 used in both  modes. Concerning the convergence  detection, synchronous variants
 can use  a global convergence procedure  which acts as a  global synchronization
 point. In the  asynchronous model, the convergence detection is  more tricky as
 used in both  modes. Concerning the convergence  detection, synchronous variants
 can use  a global convergence procedure  which acts as a  global synchronization
 point. In the  asynchronous model, the convergence detection is  more tricky as


@@ -239,65 +239,126 @@ optimize since the financial and deployment costs on large scale multi-core
 architectures are often very important. So, prior to deployment and tests it
 seems very promising to be able to simulate the behavior of asynchronous
 iterative algorithms. The problematic is then to show that the results produced
 architectures are often very important. So, prior to deployment and tests it
 seems very promising to be able to simulate the behavior of asynchronous
 iterative algorithms. The problematic is then to show that the results produced

-by simulation are in accordance with reality i.e. of the same order of
-magnitude. To our knowledge, there is no study on this problematic.
+by simulation are in accordance with reality (i.e. of the same order of
+magnitude). To our knowledge, there is no study on this problematic.

 
 \section{SimGrid}
 \label{sec:simgrid}
 
 \section{SimGrid}
 \label{sec:simgrid}

-SimGrid~\cite{SimGrid,casanova+legrand+quinson.2008.simgrid,casanova+giersch+legrand+al.2014.versatile} is a discrete event simulation framework to study the behavior of large-scale distributed computing platforms as Grids, Peer-to-Peer systems, Clouds and High Performance Computation systems. It is widely used to simulate and evaluate heuristics, prototype applications or even assess legacy MPI applications. It is still actively developed by the scientific community and distributed as an open source software.
+In the scope of this paper, we have chosen the SimGrid toolkit~\cite{SimGrid,casanova+giersch+legrand+al.2014.versatile} to simulate the behavior of parallel iterative linear solvers on different computational grid configurations. In opposite to most of the simulators which are stayed very oriented-application, the SimGrid framework is designed to study the behavior of many large-scale distributed computing platforms as Grids, Peer-to-Peer systems, Clouds or High Performance Computation systems. It is still actively developed by the scientific community and distributed as an open source software.

 
 

-%%%%%%%%%%%%%%%%%%%%%%%%%
-% SimGrid~\cite{SimGrid,casanova+legrand+quinson.2008.simgrid,casanova+giersch+legrand+al.2014.versatile}
-% is a simulation framework to study the behavior of large-scale distributed
-% systems.  As its name suggests, it emanates from the grid computing community,
-% but is nowadays used to study grids, clouds, HPC or peer-to-peer systems.  The
-% early versions of SimGrid date back from 1999, but it is still actively
-% developed and distributed as an open source software.  Today, it is 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.  SMPI is the interface that has been used for the work described in
-this paper.  The SMPI interface implements about \np[\%]{80} of the MPI 2.0
-standard~\cite{bedaride+degomme+genaud+al.2013.toward}, and supports
-applications written in C or Fortran, with little or no modifications (cf Section IV - paragraph B).
-
-Within SimGrid, the execution of a distributed application is simulated by a
-single process.  The application code is really executed, but some operations,
-like 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
-means 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 scheduling of the
-simulated processes, as well as the simulated running time of the application
-are 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 users to run relatively fast
-simulations, while still keeping accurate
-results~\cite{bedaride+degomme+genaud+al.2013.toward,
-  velho+schnorr+casanova+al.2013.validity}.  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 are unimportant for the simulation results, it is
-also possible 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 on a very large scale.
-
-The validity of simulations with SimGrid has been asserted by several studies.
-See, for example, \cite{velho+schnorr+casanova+al.2013.validity} and articles
-referenced therein for the validity of the network models.  Comparisons between
-real execution of MPI applications on the one hand, and their simulation with
-SMPI on the other hand, are presented in~\cite{guermouche+renard.2010.first,
-  clauss+stillwell+genaud+al.2011.single,
-  bedaride+degomme+genaud+al.2013.toward}.  All these works conclude that
-SimGrid is able to simulate pretty accurately the real behavior of the
-applications.
+SimGrid provides four user interfaces which can be convenient for different distributed applications~\cite{casanova+legrand+quinson.2008.simgrid}. In this paper we are interested on the SMPI user interface (Simulator MPI) which implements about \np[\%]{80} of the MPI 2.0 standard and allows minor modifications of the initial code~\cite{bedaride+degomme+genaud+al.2013.toward} (see Section~\ref{sec:04.02}). SMPI enables the direct simulation of the execution, as in the real life, of an unmodified MPI distributed application, and gets accurate results with the detailed resources consumption.
+
+SimGrid simulator uses at least three XML input files describing the computational grid resources: the number of clusters in the grid, the number of processors/cores in each cluster, the detailed description of the intra and inter networks and the list of the hosts in each cluster (see the details in Section~\ref{sec:expe}). SimGrid uses a fluid model to simulate the program execution. It allows several simulation modes which produce accurate results~\cite{bedaride+degomme+genaud+al.2013.toward,velho+schnorr+casanova+al.2013.validity}. For instance, the "in vivo" mode really executes the computation but "intercepts" the communications (the execution time is then evaluated according to the parameters of the simulated platform). It is also possible for SimGrid/SMPI to only keep the duration of large computations by skipping them. Moreover the application can be run "in vitro" mode by sharing some in-memory structures between the simulated processes and thus allowing the use of very large-scale data.
+
+The choice of SimGrid/SMPI as a simulator tool in this study has been emphasized by the results obtained by several studies to validate, in the real environments, the behavior of different network models simulated in SimGrid~\cite{velho+schnorr+casanova+al.2013.validity}. Other studies underline the comparison between the real MPI application executions and the SimGrid/SMPI ones~\cite{guermouche+renard.2010.first,clauss+stillwell+genaud+al.2011.single,bedaride+degomme+genaud+al.2013.toward}. These works show the accuracy of SimGrid simulations compared to the executions on real physical architectures.
+
+
+
+
+
+
+
+
+
+
+
+
+
+%% In the scope of this paper, the SimGrid toolkit~\cite{SimGrid,casanova+legrand+quinson.2008.simgrid,casanova+giersch+legrand+al.2014.versatile},
+%% an open source framework actively developed by its scientific community, has been chosen to simulate the behavior of iterative linear solvers in different computational grid configurations. SimGrid pretends to be non-specialized in opposite to some other simulators which stayed to be very specific oriented-application. One of the well-known SimGrid advantage is its SMPI (Simulated MPI) user interface. SMPI purpose is to execute by simulation in a similar way as in real life, an MPI distributed application and to get accurate results with the detailed resources
+%% consumption.Several studies have demonstrated the accuracy of the simulation
+%% compared with execution on real physical architectures. In addition of SMPI,
+%% Simgrid provides other API which can be convienent for different distrbuted
+%% applications: computational grid applications, High Performance Computing (HPC),
+%% P2P but also clouds applications. In this paper we use the SMPI API. It
+%% implements about \np[\%]{80} of the MPI 2.0 standard and allows minor
+%% modifications of the initial code~\cite{bedaride+degomme+genaud+al.2013.toward}
+%% (see Section~\ref{sec:04.02}).
+
+
+%%  Provided as an input to the simulator, at least $3$ XML files describe the
+%%  computational grid resources: number of clusters in the grid, number of
+%%  processors/cores in each cluster, detailed description of the intra and inter
+%%  networks and the list of the hosts in each cluster (see the details in Section~\ref{sec:expe}). Simgrid uses a fluid model to simulate the program execution.
+%%  This gives several simulation modes which produce accurate
+%%  results~\cite{bedaride+degomme+genaud+al.2013.toward,
+%%  velho+schnorr+casanova+al.2013.validity}. For instance, the "in vivo" mode
+%%  really executes the computation but "intercepts" the communications (running
+%%  time is then evaluated according to the parameters of the simulated platform).
+%%  It is also possible for SimGrid/SMPI to only keep duration of large
+%%  computations by skipping them. Moreover the application can be run "in vitro"
+%%  by sharing some in-memory structures between the simulated processes and
+%%  thus allowing the use of very large data scale.
+
+
+%% The choice of Simgrid/SMPI as a simulator tool in this study has been emphasized
+%% by the results obtained by several studies to validate, in real environments,
+%% the behavior of different network models simulated in
+%% Simgrid~\cite{velho+schnorr+casanova+al.2013.validity}. Other studies underline
+%% the comparison between real MPI executions  and SimGrid/SMPI
+%% ones\cite{guermouche+renard.2010.first, clauss+stillwell+genaud+al.2011.single,
+%% bedaride+degomme+genaud+al.2013.toward}. These works show the accuracy of
+%% SimGrid simulations.
+
+
+
+
+
+
+% SimGrid~\cite{SimGrid,casanova+legrand+quinson.2008.simgrid,casanova+giersch+legrand+al.2014.versatile} is a discrete event simulation framework to study the behavior of large-scale distributed computing platforms as Grids, Peer-to-Peer systems, Clouds and High Performance Computation systems. It is widely used to simulate and evaluate heuristics, prototype applications or even assess legacy MPI applications. It is still actively developed by the scientific community and distributed as an open source software.
+%
+% %%%%%%%%%%%%%%%%%%%%%%%%%
+% % SimGrid~\cite{SimGrid,casanova+legrand+quinson.2008.simgrid,casanova+giersch+legrand+al.2014.versatile}
+% % is a simulation framework to study the behavior of large-scale distributed
+% % systems.  As its name suggests, it emanates from the grid computing community,
+% % but is nowadays used to study grids, clouds, HPC or peer-to-peer systems.  The
+% % early versions of SimGrid date back from 1999, but it is still actively
+% % developed and distributed as an open source software.  Today, it is 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.  SMPI is the interface that has been used for the work described in
+% this paper.  The SMPI interface implements about \np[\%]{80} of the MPI 2.0
+% standard~\cite{bedaride+degomme+genaud+al.2013.toward}, and supports
+% applications written in C or Fortran, with little or no modifications (cf Section IV - paragraph B).
+%
+% Within SimGrid, the execution of a distributed application is simulated by a
+% single process.  The application code is really executed, but some operations,
+% like 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
+% means 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 scheduling of the
+% simulated processes, as well as the simulated running time of the application
+% are 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 users to run relatively fast
+% simulations, while still keeping accurate
+% results~\cite{bedaride+degomme+genaud+al.2013.toward,
+%   velho+schnorr+casanova+al.2013.validity}.  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 are unimportant for the simulation results, it is
+% also possible 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 on a very large scale.
+%
+% The validity of simulations with SimGrid has been asserted by several studies.
+% See, for example, \cite{velho+schnorr+casanova+al.2013.validity} and articles
+% referenced therein for the validity of the network models.  Comparisons between
+% real execution of MPI applications on the one hand, and their simulation with
+% SMPI on the other hand, are presented in~\cite{guermouche+renard.2010.first,
+%   clauss+stillwell+genaud+al.2011.single,
+%   bedaride+degomme+genaud+al.2013.toward}.  All these works conclude that
+% SimGrid is able to simulate pretty accurately the real behavior of the
+% applications.

 %%%%%%%%%%%%%%%%%%%%%%%%%
 
 \section{Two-stage multisplitting methods}
 %%%%%%%%%%%%%%%%%%%%%%%%%
 
 \section{Two-stage multisplitting methods}


@@ -614,8 +675,8 @@ efficient for distributed systems with high latency networks.
 \centering
 \includegraphics[width=100mm]{cluster_x_nodes_n1_x_n2.pdf}
 \caption{Various grid configurations with two networks parameters: $N1$ vs. $N2$}
 \centering
 \includegraphics[width=100mm]{cluster_x_nodes_n1_x_n2.pdf}
 \caption{Various grid configurations with two networks parameters: $N1$ vs. $N2$}

-\LZK{CE, remplacer les ``,'' des décimales par un ``.''}
-\RCE{ok}
+%\LZK{CE, remplacer les ``,'' des décimales par un ``.''}
+%\RCE{ok}

 \label{fig:02}
 \end{figure}
 
 \label{fig:02}
 \end{figure}
 


@@ -670,7 +731,15 @@ These findings may help a lot end users to setup the best and the optimal target
 \end{figure}
 
 \subsubsection{CPU power impacts on performances\\}
 \end{figure}
 
 \subsubsection{CPU power impacts on performances\\}

-Using the SimGrid simulator flexibility, we have tried to determine the impact of the CPU power of the processors in the different clusters on performances of both algorithms. We have varied the CPU power from $1$GFlops to $19$GFlops. The simulation is conducted in a grid of 2$\times$16 processors interconnected by the network $N2$ (see Table~\ref{tab:01}) to solve a 3D Poisson problem of size $150^3$. The results depicted in Figure~\ref{fig:06} confirm the performance gain, about $95\%$ for both algorithms, after improving the CPU power of processors.
+
+Using the SimGrid simulator flexibility, we have tried to determine the impact
+of the CPU power of the processors in the different clusters on performances of
+both algorithms. We have varied the CPU power from $1$GFlops to $19$GFlops. The
+simulation is conducted on a grid of $2\times16$ processors interconnected by
+the network $N2$ (see Table~\ref{tab:01}) to solve a 3D Poisson problem of size
+$150^3$. The results depicted in Figure~\ref{fig:06} confirm the performance
+gain, about $95\%$ for both algorithms, after improving the CPU power of
+processors.

 
 \begin{figure}[ht]
 \centering
 
 \begin{figure}[ht]
 \centering


@@ -679,11 +748,12 @@ Using the SimGrid simulator flexibility, we have tried to determine the impact o
 \label{fig:06}
 \end{figure}
 \ \\
 \label{fig:06}
 \end{figure}
 \ \\

+

 To conclude these series of experiments, with  SimGrid we have been able to make
 many simulations  with many parameters  variations. Doing all  these experiments
 To conclude these series of experiments, with  SimGrid we have been able to make
 many simulations  with many parameters  variations. Doing all  these experiments

-with a real platform is most of  the time not possible. Moreover the behavior of
-both GMRES and  Krylov two-stage algorithms is in accordance  with larger real
-executions on large scale supercomputers~\cite{couturier15}.
+with a real platform is most of the time not possible or very costly. Moreover
+the behavior of both GMRES and  Krylov two-stage algorithms is in accordance
+with larger real executions on large scale supercomputers~\cite{couturier15}.

 
 
 \subsection{Comparison between synchronous GMRES and asynchronous two-stage multisplitting algorithms}
 
 
 \subsection{Comparison between synchronous GMRES and asynchronous two-stage multisplitting algorithms}


@@ -696,23 +766,22 @@ classical GMRES in \textit{synchronous mode}.
 
 The  interest of  using  an asynchronous  algorithm  is that  there  is no  more
 synchronization. With  geographically distant  clusters, this may  be essential.
 
 The  interest of  using  an asynchronous  algorithm  is that  there  is no  more
 synchronization. With  geographically distant  clusters, this may  be essential.

-In  this case,  each  processor can  compute its  iteration  freely without  any
+In  this case,  each  processor can  compute its  iterations  freely without  any

 synchronization  with   the  other   processors.  Thus,  the   asynchronous  may
 theoretically reduce  the overall execution  time and can improve  the algorithm
 performance.
 
 In this section,  the SimGrid simulator is  used to compare the  behavior of the
 synchronization  with   the  other   processors.  Thus,  the   asynchronous  may
 theoretically reduce  the overall execution  time and can improve  the algorithm
 performance.
 
 In this section,  the SimGrid simulator is  used to compare the  behavior of the

-two-stage algorithm in  asynchronous mode  with GMRES  in synchronous  mode.  Several
-benchmarks have  been performed with  various combinations of the  grid resources
-(CPU, Network, matrix size, \ldots). The test  conditions are summarized
-in  Table~\ref{tab:02}. In  order to  compare  the execution  times, Table~\ref{tab:03}
-reports the  relative gain between both  algorithms. It is defined  by the ratio
-between  the   execution  time  of   GMRES  and   the  execution  time   of  the
-multisplitting.
-\LZK{Quelle table repporte les gains relatifs?? Sûrement pas Table II !!}
-\RCE{Table III avec la nouvelle numerotation}
-The  ratio  is  greater  than  one  because  the  asynchronous
-multisplitting version is faster than GMRES.
+two-stage  algorithm  in  asynchronous  mode with  GMRES  in  synchronous  mode.
+Several benchmarks  have been  performed with various  combinations of  the grid
+resources  (CPU,  Network,  matrix  size,   \ldots).  The  test  conditions  are
+summarized in Table~\ref{tab:02}.
+
+
+
+%\LZK{Quelle table repporte les gains relatifs?? Sûrement pas Table II !!}
+%\RCE{Table III avec la nouvelle numerotation}
+

 
 \begin{table}[htbp]
 \centering
 
 \begin{table}[htbp]
 \centering


@@ -721,7 +790,7 @@ multisplitting version is faster than GMRES.
  Grid architecture                       & 2$\times$50 totaling 100 processors\\
  Processors Power                        & 1 GFlops to 1.5 GFlops \\
  \multirow{2}{*}{Network inter-clusters} & $bw$=1.25 Gbits, $lat=50\mu$s \\
  Grid architecture                       & 2$\times$50 totaling 100 processors\\
  Processors Power                        & 1 GFlops to 1.5 GFlops \\
  \multirow{2}{*}{Network inter-clusters} & $bw$=1.25 Gbits, $lat=50\mu$s \\

-                                         & $bw$=5 Mbits, $lat=20ms$s\\
+                                         & $bw$=5 Mbits, $lat=20ms$\\

  Matrix size                             & from $62^3$ to $150^3$\\
  Residual error precision                & $10^{-5}$ to $10^{-9}$\\ \hline \\
  \end{tabular}
  Matrix size                             & from $62^3$ to $150^3$\\
  Residual error precision                & $10^{-5}$ to $10^{-9}$\\ \hline \\
  \end{tabular}


@@ -770,15 +839,15 @@ multisplitting version is faster than GMRES.
  \label{tab:03}
 \end{table}
 
  \label{tab:03}
 \end{table}
 

-Again,  comprehensive and  extensive tests  have been  conducted with  different
-parameters as  the CPU power, the  network parameters (bandwidth and  latency)
-and with different problem size. The  relative gains greater than $1$  between the
-two algorithms have  been captured after  each step  of the test.   In
-Table~\ref{tab:08}  are  reported the  best  grid  configurations allowing
-the  two-stage multisplitting algorithm to  be more than  $2.5$ times faster  than the
-classical  GMRES.  These  experiments also  show the  relative tolerance  of the
-multisplitting algorithm when using a low speed network as usually observed with
-geographically distant clusters through the internet.
+
+Table~\ref{tab:03} reports  the relative gains  between both algorithms.   It is
+defined by the ratio between the execution  time of GMRES and the execution time
+of the  multisplitting. The ratio is  greater than one because  the asynchronous
+multisplitting  version  is  faster  than   GMRES.  In  average,  the  two-stage
+multisplitting algorithm to  be more than $2.5$ times faster  than the classical
+GMRES.  These experiments also show the relative tolerance of the multisplitting
+algorithm when using a low speed network as usually observed with geographically
+distant clusters through the internet.

 
 
 \section{Conclusion}
 
 
 \section{Conclusion}