X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/rce2015.git/blobdiff_plain/9793696f84bb746d5af3969fb0bc9105f9cb3a99..ba83d1e1dca7d4eaed2a24aa284fc7735dd5fc07:/paper.tex?ds=sidebyside

diff --git a/paper.tex b/paper.tex
index fa447f6..cda1fdd 100644
--- a/paper.tex
+++ b/paper.tex
@@ -94,28 +94,27 @@
   Email:~\email{l.zianekhodja@ulg.ac.be}
 }
 
-\begin{abstract}   The behavior of multi-core applications is always a challenge
-to predict, especially with a new architecture for which no experiment has been
-performed. With some applications, it is difficult, if not impossible, to build
-accurate performance models. That is why another solution is to use a simulation
-tool which allows us to change many parameters of the architecture (network
-bandwidth, latency, number of processors) and to simulate the execution of such
-applications. The main contribution of this paper is to show that the use of a
-simulation tool (here we have decided to use the SimGrid toolkit) can really
-help developpers to better tune their applications for a given multi-core
-architecture.
-
-In particular we focus our attention on two parallel iterative algorithms based
-on the  Multisplitting algorithm  and we  compare them  to the  GMRES algorithm.
-These algorithms  are used to  solve linear  systems. Two different  variants of
-the Multisplitting are studied: one  using synchronoous  iterations and  another
-one  with asynchronous iterations. For each algorithm we have simulated
-different architecture parameters to evaluate their influence on the overall
-execution time.  The obtain simulated results confirm the real results
-previously obtained on different real multi-core architectures and also confirm
-the efficiency of the asynchronous multisplitting algorithm compared to the
-synchronous GMRES method.
-
+\begin{abstract} %% The behavior of multi-core applications is always a challenge
+%% to predict, especially with a new architecture for which no experiment has been
+%% performed. With some applications, it is difficult, if not impossible, to build
+%% accurate performance models. That is why another solution is to use a simulation
+%% tool which allows us to change many parameters of the architecture (network
+%% bandwidth, latency, number of processors) and to simulate the execution of such
+%% applications. The main contribution of this paper is to show that the use of a
+%% simulation tool (here we have decided to use the SimGrid toolkit) can really
+%% help developers to better tune their applications for a given multi-core
+%% architecture.
+
+%% In this paper we focus our attention on the simulation of iterative algorithms to solve sparse linear systems on large clusters. We study the behavior of the widely used GMRES algorithm and two different variants of the Multisplitting algorithms: one using synchronous iterations and another one with asynchronous iterations.  
+%% For each algorithm we have simulated
+%% different architecture parameters to evaluate their influence on the overall
+%% execution time. 
+%% The simulations confirm the real results previously obtained on different real multi-core architectures and also confirm the efficiency of the asynchronous Multisplitting algorithm on distant clusters compared to the synchronous GMRES algorithm.
+
+
+The behavior of multi-core applications is always a challenge to predict, especially with a new architecture for which no experiment has been performed. With some applications, it is difficult, if not impossible, to build accurate performance models. That is why another solution is to use a simulation tool which allows us to change many parameters of the architecture (network bandwidth, latency, number of processors) and to simulate the execution of such applications. 
+
+In this paper we focus on the simulation of iterative algorithms to solve sparse linear systems. We study the behavior of the GMRES algorithm and two different variants of the Multisplitting algorithms: using synchronous or asynchronous iterations. For each algorithm we have simulated different architecture parameters to evaluate their influence on the overall execution time. The simulations confirm the real results previously obtained on different real multi-core architectures and also confirm the efficiency of the asynchronous Multisplitting algorithm on distant clusters compared to the GMRES algorithm.
 \end{abstract}
 
 %\keywords{Algorithm; distributed; iterative; asynchronous; simulation; simgrid;
@@ -826,7 +825,7 @@ Again,  comprehensive and  extensive tests  have been  conducted with  different
 parameters as  the CPU power, the  network parameters (bandwidth and  latency)
 and with different problem size. The  relative gains greater than $1$  between the
 two algorithms have  been captured after  each step  of the test.   In
-Figure~\ref{fig:07}  are  reported the  best  grid  configurations allowing
+Table~\ref{tab:08}  are  reported the  best  grid  configurations allowing
 the  multisplitting method to  be more than  $2.5$ times faster  than the
 classical  GMRES.  These  experiments also  show the  relative tolerance  of the
 multisplitting algorithm when using a low speed network as usually observed with
@@ -841,7 +840,7 @@ geographically distant clusters through the internet.
     \end{tabular}}
 
 
-\begin{figure}[!t]
+\begin{table}[!t]
 \centering
 %\begin{table}
 %  \caption{Relative gain of the multisplitting algorithm compared with the classical GMRES}
@@ -868,10 +867,9 @@ geographically distant clusters through the internet.
     \hline
   \end{mytable}
 %\end{table}
- \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}
+ \caption{Relative gain of the multisplitting algorithm compared with the classical GMRES}
+ \label{tab:08}
+\end{table}
 
 
 \section{Conclusion}