From: David Laiymani <david.laiymani@univ-fcomte.fr>
Date: Thu, 7 May 2015 13:59:08 +0000 (+0200)
Subject: DL : modifs suivanr remarques Lilia
X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/rce2015.git/commitdiff_plain/1ca5149a217599c0bd011769c9fc6a2ef4fc9652?ds=sidebyside

DL : modifs suivanr remarques Lilia
---

diff --git a/paper.tex b/paper.tex
index d65672a..9a7ae98 100644
--- a/paper.tex
+++ b/paper.tex
@@ -163,10 +163,11 @@ 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
@@ -174,18 +175,23 @@ 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 [], which show that the 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.}
+\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. 
+asynchronous  multisplitting algorithm compared to the synchronous  GMRES.
 
 \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)}