X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/rce2015.git/blobdiff_plain/e139a43b147ddae98f95e987560caadfa4c845c8..d4ade70c19ff4980df5662db589ac0defced4391:/paper.tex?ds=inline

diff --git a/paper.tex b/paper.tex
index 0d24694..c8b8918 100644
--- a/paper.tex
+++ b/paper.tex
@@ -136,7 +136,7 @@ are often very important. So, in this context it is difficult to optimize a
 given application for a given  architecture. In this way and in order to reduce
 the access cost to these computing resources it seems very interesting to use a
 simulation environment.  The advantages are numerous: development life cycle,
-code debugging, ability to obtain results quickly~\ldots. In counterpart, the simulation results need to be consistent with the real ones.
+code debugging, ability to obtain results quickly\dots{} In counterpart, the simulation results need to be consistent with the real ones.
 
 In this paper we focus on a class of highly efficient parallel algorithms called
 \emph{iterative algorithms}. The parallel scheme of iterative methods is quite
@@ -237,7 +237,7 @@ for the asynchronous scheme (this number depends depends on  the delay of the
 messages). Note that, it is not the case in the synchronous mode where the
 number of iterations is the same than in the sequential mode. In this way, the
 set of the parameters  of the  platform (number  of nodes,  power of nodes,
-inter and  intra clusters  bandwidth  and  latency \ldots) and  of  the
+inter and  intra clusters  bandwidth  and  latency, \ldots) and  of  the
 application can drastically change the number of iterations required to get the
 convergence. It follows that asynchronous iterative algorithms are difficult to
 optimize since the financial and deployment costs on large scale multi-core
@@ -522,7 +522,8 @@ architectures and scaling up the input matrix size}
  Input matrix size & N$_{x}$ x N$_{y}$ x N$_{z}$ =150 x 150 x 150\\ %\hline
  - &  N$_{x}$ x N$_{y}$ x N$_{z}$  =170 x 170 x 170    \\ \hline
  \end{tabular}
-\caption{Test conditions: various grid configurations with the input matix size N$_{x}$=150 or N$_{x}$=170 \RC{N2 n'est pas dÃ©fini..}\RC{Nx est dÃ©fini, Ny? Nz?}}
+\caption{Test conditions: various grid configurations with the input matix size N$_{x}$=150 or N$_{x}$=170 \RC{N2 n'est pas dÃ©fini..}\RC{Nx est dÃ©fini, Ny? Nz?}
+\AG{La lettre 'x' n'est pas le symbole de la multiplication. Utiliser \texttt{\textbackslash times}.  Idem dans le texte, les figures, etc.}}
 \label{tab:01}
 \end{center}
 \end{table}
@@ -545,7 +546,8 @@ multisplitting method.
   \begin{center}
     \includegraphics[width=100mm]{cluster_x_nodes_nx_150_and_nx_170.pdf}
   \end{center}
-  \caption{Various grid configurations with the input matrix size N$_{x}$=150 and N$_{x}$=170\RC{idem}}
+  \caption{Various grid configurations with the input matrix size N$_{x}$=150 and N$_{x}$=170\RC{idem}
+\AG{Utiliser le point comme sÃ©parateur dÃ©cimal et non la virgule.  Idem dans les autres figures.}}
   \label{fig:01}
 \end{figure}
 
@@ -587,7 +589,8 @@ the  network speed  drops down (variation of 12.5\%), the  difference between  t
 \begin{figure} [ht!]
 \centering
 \includegraphics[width=100mm]{cluster_x_nodes_n1_x_n2.pdf}
-\caption{Grid 2x16 and 4x8 with networks N1 vs N2}
+\caption{Grid 2x16 and 4x8 with networks N1 vs N2
+\AG{\np{8E-6}, \np{5E-6} au lieu de 8E-6, 5E-6}}
 \label{fig:02}
 \end{figure}
 %\end{wrapfigure}
@@ -612,7 +615,8 @@ the  network speed  drops down (variation of 12.5\%), the  difference between  t
 \begin{figure} [ht!]
 \centering
 \includegraphics[width=100mm]{network_latency_impact_on_execution_time.pdf}
-\caption{Network latency impacts on execution time}
+\caption{Network latency impacts on execution time
+\AG{\np{E-6}}}
 \label{fig:03}
 \end{figure}
 
@@ -646,7 +650,8 @@ magnitude with a latency of $8.10^{-6}$.
 \begin{figure} [ht!]
 \centering
 \includegraphics[width=100mm]{network_bandwith_impact_on_execution_time.pdf}
-\caption{Network bandwith impacts on execution time}
+\caption{Network bandwith impacts on execution time
+\AG{``Execution time'' avec un 't' minuscule}. Idem autres figures.}
 \label{fig:04}
 \end{figure}
 
@@ -814,7 +819,8 @@ geographically distant clusters through the internet.
     \hline
   \end{mytable}
 %\end{table}
- \caption{Relative gain of the multisplitting algorithm compared with the classical GMRES}
+ \caption{Relative gain of the multisplitting algorithm compared with the classical GMRES
+\AG{C'est un tableau, pas une figure}}
  \label{fig:07}
 \end{figure}
 
@@ -829,6 +835,10 @@ This work is partially funded by the Labex ACTION program (contract ANR-11-LABX-
 
 \bibliographystyle{wileyj}
 \bibliography{biblio}
+\AG{Des warnings bibtex Ã  corriger (%
+  \texttt{entry type for "SimGrid" isn't style-file defined},
+  \texttt{empty booktitle in Bru95}%
+).}
 
 \end{document}
 
