DL : expé suite et fin

diff --git a/paper.tex b/paper.tex
index 31eb0e89348ae7f0c3e7e0b449b81b5aedc7ff2f..de34cb654bd735c79c91c4df6a3d0cba5d9c8ac5 100644 (file)
--- a/paper.tex
+++ b/paper.tex
@@ -670,7 +670,15 @@ 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 +687,12 @@ 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}


@@ -696,7 +705,7 @@ 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.
 synchronization  with   the  other   processors.  Thus,  the   asynchronous  may
 theoretically reduce  the overall execution  time and can improve  the algorithm
 performance.


@@ -705,8 +714,8 @@ 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
 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
+in  Table~\ref{tab:02}. In  order to  compare  the execution  times. 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.
 \LZK{Quelle table repporte les gains relatifs?? Sûrement pas Table II !!}
 between  the   execution  time  of   GMRES  and   the  execution  time   of  the
 multisplitting.
 \LZK{Quelle table repporte les gains relatifs?? Sûrement pas Table II !!}


@@ -721,7 +730,7 @@ 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}