X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/rce2015.git/blobdiff_plain/c072d1c2f72b8178cf60704c83e7290ee013bc90..ca1429f05161a13a6c9cc1eb4a62dcb8217c06d2:/paper.tex?ds=inline

diff --git a/paper.tex b/paper.tex
index 8bdc8f3..ab8f9ab 100644
--- a/paper.tex
+++ b/paper.tex
@@ -549,9 +549,8 @@ Grid architecture                       & 2$\times$16, 4$\times$8, 4$\times$16 a
 \end{center}
 \end{table}
 
-\subsubsection{Simulations for various grid architectures and scaling-up matrix sizes}
-\  \\
-% environment
+\subsubsection{Simulations for various grid architectures and scaling-up matrix sizes\\}
+
 In  this  section,  we  analyze   the  simulations  conducted  on  various  grid
 configurations and for different sizes of the 3D Poisson problem. The parameters
 of    the    network    between    clusters    is    fixed    to    $N2$    (see
@@ -573,7 +572,7 @@ The execution times between both algorithms is significant with different grid a
 \label{fig:01}
 \end{figure}
 
-\subsubsection{Simulations for two different inter-clusters network speeds \\}
+\subsubsection{Simulations for two different inter-clusters network speeds\\}
 
 In this section, the experiments  compare the  behavior of  the algorithms  running on a
 speeder inter-cluster  network (N2) and  also on  a less performant  network (N1) respectively defined in the test conditions Table~\ref{tab:02}.
@@ -610,8 +609,8 @@ the  network speed  drops down (variation of 12.5\%), the  difference between  t
 
 
 
-\subsubsection{Network latency impacts on performance}
-\ \\
+\subsubsection{Network latency impacts on performance\\}
+
 \begin{table} [ht!]
 \centering
 \begin{tabular}{r c }
@@ -642,8 +641,8 @@ between the two algorithms  varies from 2.2 to 1.5 times  with a network latency
 decreasing from $8.10^{-6}$ to $6.10^{-5}$ second.
 
 
-\subsubsection{Network bandwidth impacts on performance}
-\ \\
+\subsubsection{Network bandwidth impacts on performance\\}
+
 \begin{table} [ht!]
 \centering
 \begin{tabular}{r c }
@@ -675,8 +674,8 @@ Figure~\ref{fig:04}). However,  in this  case, the Krylov  multisplitting method
 presents a better  performance in the considered bandwidth interval  with a gain
 of $40\%$ which is only around $24\%$ for the classical GMRES.
 
-\subsubsection{Input matrix size impacts on performance}
-\ \\
+\subsubsection{Input matrix size impacts on performance\\}
+
 \begin{table} [ht!]
 \centering
 \begin{tabular}{r c }