From: David Laiymani <david.laiymani@univ-fcomte.fr>
Date: Wed, 6 May 2015 14:36:08 +0000 (+0200)
Subject: DL : expé encore
X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/rce2015.git/commitdiff_plain/c6459f3420747227cfe477c0e22f08499ee3e4de?ds=inline;hp=cb07dd536bdfe2ca24b31fdd3d95195cde19513b

DL : expé encore
---

diff --git a/paper.tex b/paper.tex
index c198158..0dc584b 100644
--- a/paper.tex
+++ b/paper.tex
@@ -45,6 +45,8 @@
   \todo[color=blue!10,#1]{\sffamily\textbf{LZK:} #2}\xspace}
 \newcommand{\RCE}[2][inline]{%
   \todo[color=yellow!10,#1]{\sffamily\textbf{RCE:} #2}\xspace}
+\newcommand{\DL}[2][inline]{%
+    \todo[color=pink!10,#1]{\sffamily\textbf{DL:} #2}\xspace}
 
 \algnewcommand\algorithmicinput{\textbf{Input:}}
 \algnewcommand\Input{\item[\algorithmicinput]}
@@ -620,7 +622,7 @@ The results  of increasing  the network  bandwidth show  the improvement  of the
 performance  for   both  algorithms   by  reducing   the  execution   time  (see
 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 classical GMRES.
+of $40\%$ which is only around $24\%$ for the classical GMRES.
 
 \subsubsection{Input matrix size impacts on performance}
 \ \\
@@ -632,27 +634,27 @@ of 40\% which is only around 24\% for classical GMRES.
  Network & N2 : bw=1Gbs - lat=5.10$^{-5}$ \\
  Input matrix size & N$_{x}$ = From 40 to 200\\ \hline
  \end{tabular}
-\caption{Input matrix size impact}
+\caption{Input matrix size impacts}
 \end{figure}
 
 
 \begin{figure} [ht!]
 \centering
 \includegraphics[width=100mm]{pb_size_impact_on_execution_time.pdf}
-\caption{Problem size impact on execution time}
+\caption{Problem size impacts on execution time}
 \label{fig:05}
 \end{figure}
 
-In these experiments, the input matrix size  has been set from N$_{x}$ = N$_{y}$
-= N$_{z}$ = 40 to 200 side elements  that is from 40$^{3}$ = 64.000 to 200$^{3}$
-= 8,000,000  points. Obviously, as  shown in Figure~\ref{fig:05},  the execution
+In these experiments, the input matrix size  has been set from $N_{x} = N_{y}
+= N_{z} = 40$ to $200$ side elements  that is from $40^{3} = 64.000$ to $200^{3}
+= 8,000,000$  points. Obviously, as  shown in Figure~\ref{fig:05},  the execution
 time for  both algorithms increases when  the input matrix size  also increases.
 But the interesting results are:
 \begin{enumerate}
-  \item the drastic increase (300 times) \RC{Je ne vois pas cela sur la figure}
+  \item the drastic increase ($300$ times) \RC{Je ne vois pas cela sur la figure}
 of the  number of  iterations needed  to reach the  convergence for  the classical
-GMRES algorithm when  the matrix size go beyond N$_{x}$=150;
-\item the  classical GMRES execution time  is almost the double  for N$_{x}$=140
+GMRES algorithm when  the matrix size go beyond $N_{x}=150$;
+\item the  classical GMRES execution time  is almost the double  for $N_{x}=140$
   compared with the Krylov multisplitting method.
 \end{enumerate}
 
@@ -661,7 +663,7 @@ targeted environment for the application deployment when focusing on the problem
 size scale up.  It  should be noticed that the same test has  been done with the
 grid 2x16 leading to the same conclusion.
 
-\subsubsection{CPU Power impact on performance}
+\subsubsection{CPU Power impacts on performance}
 
 \begin{figure} [ht!]
 \centering
@@ -671,22 +673,26 @@ grid 2x16 leading to the same conclusion.
  Network & N2 : bw=1Gbs - lat=5.10$^{-5}$ \\ %\hline
  Input matrix size & N$_{x}$ = 150 x 150 x 150\\ \hline
  \end{tabular}
-\caption{CPU Power impact}
+\caption{CPU Power impacts}
 \end{figure}
 
 \begin{figure} [ht!]
 \centering
 \includegraphics[width=100mm]{cpu_power_impact_on_execution_time.pdf}
-\caption{CPU Power impact on execution time}
+\caption{CPU Power impacts on execution time}
 \label{fig:06}
 \end{figure}
 
 Using the Simgrid  simulator flexibility, we have tried to  determine the impact
 on the  algorithms performance in  varying the CPU  power of the  clusters nodes
-from 1  to 19 GFlops.  The outputs  depicted in Figure~\ref{fig:06}  confirm the
-performance gain,  around 95\% for  both of the  two methods, after  adding more
+from $1$ to $19$ GFlops.  The outputs  depicted in Figure~\ref{fig:06}  confirm the
+performance gain,  around $95\%$ for  both of the  two methods, after  adding more
 powerful CPU.
 
+\DL{il faut une conclusion sur ces tests : ils confirment les résultats déjà
+obtenus en grandeur réelle. Donc c'est une aide précieuse pour les dev. Pas
+besoin de déployer sur une archi réelle}
+
 \subsection{Comparing GMRES in native synchronous mode and the multisplitting algorithm in asynchronous mode}
 
 The previous paragraphs  put in evidence the interests to  simulate the behavior