X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/c3bfcd6a7a6c9b0dcb1bcdc52facec1fe257f658..61b2ca8a9fc3fc1c52c4b66234796415d8b47e3f:/hpcc.tex

diff --git a/hpcc.tex b/hpcc.tex
index c2f5a1b..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
@@ -254,7 +257,7 @@ like the communications are intercepted, and their running time is computed
 according to the characteristics of the simulated execution platform.  The
 description of this target platform is given as an input for the execution, by
 the mean of an XML file.  It describes the properties of the platform, such as
-the computing node with their computing power, the interconnection links with
+the computing nodes with their computing power, the interconnection links with
 their bandwidth and latency, and the routing strategy.  The simulated running
 time of the application is computed according to these properties.
 
@@ -378,9 +381,12 @@ Note here that the use of SMPI functions optimizer for memory footprint and CPU
 As mentioned, upon this adaptation, the algorithm is executed as in the real life in the simulated environment after the following minor changes. First, all declared 
 global variables have been moved to local variables for each subroutine. In fact, global variables generate side effects arising from the concurrent access of 
 shared memory used by threads simulating each computing unit in the SimGrid architecture. Second, the alignment of certain types of variables such as ``long int'' had
-also to be reviewed. Finally, some compilation errors on MPI\_Waitall and MPI\_Finalize primitives have been fixed with the latest version of SimGrid.
+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}
@@ -399,8 +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
@@ -447,7 +452,8 @@ containing 50 hosts each, totaling 100 hosts. Various combinations of the above
 factors have providing the results shown in Table~\ref{tab.cluster.2x50} with a
 matrix size ranging from $N_x = N_y = N_z = \text{62}$ to 171 elements or from
 $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{171}^\text{3} =
-\text{\np{5211000}}$ entries.
+\text{\np{5000211}}$ entries.
+\AG{Expliquer comment lire les tableaux.}
 
 % use the same column width for the following three tables
 \newlength{\mytablew}\settowidth{\mytablew}{\footnotesize\np{E-11}}
@@ -538,7 +544,7 @@ relative gains greater than 1 with a matrix size from 62 to 100 elements.
     & \np{E-5} & \np{E-5} & \np{E-5} & \np{E-5} & \np{E-5} & \np{E-5} \\
     \hline
     Relative gain
-    & 1.003    & 1,01     & 1,08     & 0.19     & 1.28     & 1.01 \\
+    & 1.003    & 1.01     & 1.08     & 1.19     & 1.28     & 1.01 \\
     \hline
   \end{mytable}
 \end{table}
@@ -573,6 +579,7 @@ Note that the program was run with the following parameters:
 
 \paragraph*{SMPI parameters}
 
+~\\{}\AG{Donner un peu plus de précisions (plateforme en particulier).}
 \begin{itemize}
 	\item HOSTFILE: Hosts file description.
 	\item PLATFORM: file description of the platform architecture : clusters (CPU power,
@@ -588,11 +595,8 @@ lat latency, \dots{}).
 	\item Maximum number of internal and external iterations;
 	\item Internal and external precisions;
 	\item Matrix size $N_x$, $N_y$ and $N_z$;
-%<<<<<<< HEAD
 	\item Matrix diagonal value: \np{6.0};
-	\item Matrix Off-diagonal value: \np{-1.0};
-%=======
-%>>>>>>> 5fb6769d88c1720b6480a28521119ef010462fa6
+	\item Matrix off-diagonal value: \np{-1.0};
 	\item Execution Mode: synchronous or asynchronous.
 \end{itemize}
 
@@ -631,8 +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