X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/rce2015.git/blobdiff_plain/f98e8137319ffebaec402d37b163f962c34a82b9..533d18cef01dbe327cc5021d722f927a4eec55ff:/paper.tex

diff --git a/paper.tex b/paper.tex
index 9e09b31..9bd9b8b 100644
--- a/paper.tex
+++ b/paper.tex
@@ -94,7 +94,7 @@ analysis of simulated grid-enabled numerical iterative algorithms}
   Email:~\email{l.zianekhodja@ulg.ac.be}
 }
 
-\begin{abstract}   The behavior of multi-core applications is always a challenge
+\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
@@ -102,19 +102,23 @@ 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
+help developers 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
+%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.
+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 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.
+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.
+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.
 
 \end{abstract}
 
@@ -165,10 +169,11 @@ application  on   a  given   multi-core  architecture.  Finding   good  resource
 allocations policies under  varying CPU power, network speeds and  loads is very
 challenging and  labor intensive~\cite{Calheiros:2011:CTM:1951445.1951450}. This
 problematic is  even more difficult  for the  asynchronous scheme where  a small
-parameter variation of the execution platform can lead to very different numbers
-of iterations to reach the converge and so to very different execution times. In
-this challenging context we think that the  use of a simulation tool can greatly
-leverage the possibility of testing various platform scenarios.
+parameter variation of the execution platform and of the application data can
+lead to very different numbers of iterations to reach the converge and so to
+very different execution times. In this challenging context we think that the
+use of a simulation tool can greatly leverage the possibility of testing various
+platform scenarios.
 
 The main contribution of this paper is to show that the use of a simulation tool
 (i.e. the SimGrid toolkit~\cite{SimGrid}) in the context of real  parallel
@@ -176,20 +181,31 @@ applications (i.e. large linear system solvers) can help developers to better
 tune their application for a given multi-core architecture. To show the validity
 of this approach we first compare the simulated execution of the multisplitting
 algorithm  with  the  GMRES   (Generalized   Minimal  Residual)
-solver~\cite{saad86} in synchronous mode. 
+solver~\cite{saad86} in synchronous mode. The simulation results allow us to
+determine which method to choose given a specified multi-core architecture.
 
 \LZK{Pas trop convainquant comme argument pour valider l'approche de simulation. \\On peut dire par exemple: on a pu simuler différents algos itératifs à large échelle (le plus connu GMRES et deux variantes de multisplitting) et la simulation nous a permis (sans avoir le vrai matériel) de déterminer quelle serait la meilleure solution pour une telle configuration de l'archi ou vice versa.\\A revoir...}
+\DL{OK : ajout d'une phrase précisant tout cela}
 
-The obtained results on different
-simulated multi-core architectures confirm the real results previously obtained
-on non simulated architectures.  
+Moreover the obtained results on different simulated multi-core architectures
+confirm the real results previously obtained on non simulated architectures.
+More precisely the simulated results are in accordance (i.e. with the same order
+of magnitude) with the works presented in~\cite{couturier15}, which show that the synchronous
+multisplitting method is more efficient than GMRES for large scale clusters.
 
-\LZK{Il n y a pas dans la partie expé cette comparaison et confirmation des résultats entre la simulation et l'exécution réelle des algos sur les vrais clusters.\\ Sinon on pourrait ajouter dans la partie expé une référence vers le journal supercomput de krylov multi pour confirmer que cette méthode est meilleure que GMRES sur les clusters large échelle.}
+\LZK{Il n y a pas dans la partie expé cette comparaison et confirmation des
+résultats entre la simulation et l'exécution réelle des algos sur les vrais
+clusters.\\ Sinon on pourrait ajouter dans la partie expé une référence vers le
+journal supercomput de krylov multi pour confirmer que cette méthode est
+meilleure que GMRES sur les clusters large échelle.} \DL{OK ajout d'une phrase.
+Par contre je n'ai pas la ref. Merci de la mettre}
 
-We also confirm  the efficiency  of the
-asynchronous  multisplitting algorithm  compared to the synchronous  GMRES. 
+Simulated results  also confirm  the efficiency  of the asynchronous
+multisplitting algorithm compared to the synchronous GMRES especially in case of
+geographically distant clusters.
 
 \LZK{P.S.: Pour tout le papier, le principal objectif n'est pas de faire des comparaisons entre des méthodes itératives!!\\Sinon, les deux algorithmes Krylov multisplitting synchrone et multisplitting asynchrone sont plus efficaces que GMRES sur des clusters à large échelle.\\Et préciser, si c'est vraiment le cas, que le multisplitting asynchrone est plus efficace et adapté aux clusters distants par rapport aux deux autres algos (je n'ai pas encore lu la partie expé)}
+\DL{Tu as raison on s'est posé la question de garder ou non cette partie des résultats. On a décidé de la garder pour avoir plus de chose à montrer. J'ai essayer de clarifier un peu}
 
 In
 this way and with a simple computing architecture (a laptop) SimGrid allows us
@@ -886,9 +902,7 @@ This work is partially funded by the Labex ACTION program (contract ANR-11-LABX-
 
 \bibliographystyle{wileyj}
 \bibliography{biblio}
-\AG{Warning bibtex à corriger (%
-  \texttt{empty booktitle in Bru95}%
-).}
+
 
 \end{document}