From: lilia <lilia@mondomaine.fr>
Date: Fri, 25 Apr 2014 18:06:28 +0000 (+0200)
Subject: 25-04-2014
X-Git-Tag: hpcc2014_submission~62
X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/commitdiff_plain/61b2ca8a9fc3fc1c52c4b66234796415d8b47e3f?ds=inline;hp=--cc

25-04-2014
---

61b2ca8a9fc3fc1c52c4b66234796415d8b47e3f
diff --git a/hpcc.tex b/hpcc.tex
index 28d08c0..6cc2be1 100644
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -41,6 +41,7 @@
 \algnewcommand\Output{\item[\algorithmicoutput]}
 
 \newcommand{\MI}{\mathit{MaxIter}}
+\newcommand{\Time}[1]{\mathit{Time}_\mathit{#1}}
 
 \begin{document}
 
@@ -97,6 +98,8 @@ a residual precision up to \np{E-11}. Such successful results open
 perspectives on experimentations for running the algorithm on a 
 simulated large scale growing environment and with larger problem size. 
 
+\LZK{Long\ldots}
+
 % no keywords for IEEE conferences
 % Keywords: Algorithm distributed iterative asynchronous simulation SimGrid
 \end{abstract}
@@ -172,7 +175,7 @@ asynchronous mode.
 
 This article is structured as follows: after this introduction, the next  section will give a brief description of
 iterative asynchronous model.  Then, the simulation framework SimGrid is presented with the settings to create various
-distributed architectures. The algorithm of  the multisplitting method used by GMRES written with MPI primitives and
+distributed architectures. The algorithm of  the multisplitting method used by GMRES \LZK{??? GMRES n'utilise pas la méthode de multisplitting! Sinon ne doit on pas expliquer le choix d'une méthode de multisplitting?} written with MPI primitives and
 its adaptation to SimGrid with SMPI (Simulated MPI) is detailed in the next section. At last, the experiments results
 carried out will be presented before some concluding remarks and future works.
  
@@ -196,7 +199,7 @@ times and the arrows the communications.
 With this algorithmic model, the number of iterations required before the
 convergence is generally greater than for the two former classes. But, and as detailed in~\cite{bcvc06:ij}, AIAC
 algorithms can significantly reduce overall execution times by suppressing idle times due to synchronizations especially
-in a grid computing context.
+in a grid computing context.\LZK{Répétition par rapport à l'intro}
 
 \begin{figure}[!t]
   \centering
@@ -382,7 +385,8 @@ also to be reviewed.
 \AG{À propos de ces problèmes d'alignement, en dire plus si ça a un intérêt, ou l'enlever.}
  Finally, some compilation errors on MPI\_Waitall and MPI\_Finalize primitives have been fixed with the latest version of SimGrid.
 In total, the initial MPI program running on the simulation environment SMPI gave after a very simple adaptation the same results as those obtained in a real 
-environment. We have successfully executed the code in synchronous mode using GMRES algorithm compared with a multisplitting method in asynchrnous mode after few modification. 
+environment. We have successfully executed the code in synchronous mode using parallel GMRES algorithm compared with our multisplitting algorithm in asynchronous mode after few modifications. 
+
 
 
 \section{Experimental results}
@@ -401,8 +405,7 @@ study that the results depend on the following parameters:
   experimentation of the simulation in having an execution time in asynchronous
   less than in synchronous mode. The ratio between the execution time of asynchronous compared to the synchronous mode is defined as the "relative gain". So, our objective running the algorithm in SimGrid is to obtain a relative gain greater than 1.
 \end{itemize}
-\LZK{Propositions pour remplacer le terme ``speedup'': acceleration ratio ou relative gain}
-\CER{C'est fait. En conséquence, les tableaux et les commentaires ont été aussi modifiés}
+
 A priori, obtaining a relative gain greater than 1 would be difficult in a local area
 network configuration where the synchronous mode will take advantage on the
 rapid exchange of information on such high-speed links. Thus, the methodology
@@ -593,7 +596,7 @@ lat latency, \dots{}).
 	\item Internal and external precisions;
 	\item Matrix size $N_x$, $N_y$ and $N_z$;
 	\item Matrix diagonal value: \np{6.0};
-	\item Matrix Off-diagonal value: \np{-1.0};
+	\item Matrix off-diagonal value: \np{-1.0};
 	\item Execution Mode: synchronous or asynchronous.
 \end{itemize}
 
@@ -632,10 +635,8 @@ with 200 nodes in total. The convergence with a relative gain around 1.1 was
 obtained with a bandwidth of \np[Mbit/s]{1} as shown in
 Table~\ref{tab.cluster.3x67}.
 
-\LZK{Dans le papier, on compare les deux versions synchrone et asycnhrone du multisplitting. Y a t il des résultats pour comparer gmres parallèle classique avec multisplitting asynchrone? Ca permettra de montrer l'intérêt du multisplitting asynchrone sur des clusters distants}
-\CER{En fait, les résultats ont été obtenus en comparant les temps d'exécution entre l'algo classique GMRES en mode synchrone avec le multisplitting en mode asynchrone, le tout sur un environnement de clusters distants}
-
 \RC{Est ce qu'on sait expliquer pourquoi il y a une telle différence entre les résultats avec 2 et 3 clusters... Avec 3 clusters, ils sont pas très bons... Je me demande s'il ne faut pas les enlever...}
+\LZK{Ma question est: le bw et lat sont ceux inter-clusters ou pour les deux inter et intra cluster??}
 
 \section{Conclusion}
 The experimental results on executing a parallel iterative algorithm in