From: Arnaud Giersch <arnaud.giersch@univ-fcomte.fr>
Date: Thu, 24 Apr 2014 16:11:19 +0000 (+0200)
Subject: Some remarks.
X-Git-Tag: hpcc2014_submission~71
X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/commitdiff_plain/3357d4486bff13d056f39c12f3852ef9c3dbe45b?ds=sidebyside;hp=f1a651fe24fb4fd4daecc0894f43933d1189330b

Some remarks.
---

diff --git a/hpcc.tex b/hpcc.tex
index aa6eb7d..d376194 100644
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -72,6 +72,7 @@
 
 \RC{Ordre des auteurs pas définitif.}
 \begin{abstract}
+\AG{L'abstract est AMHA incompréhensible et ne donne pas envie de lire la suite.}
 In recent years, the scalability of large-scale implementation in a 
 distributed environment of algorithms becoming more and more complex has 
 always been hampered by the limits of physical computing resources 
@@ -155,7 +156,7 @@ linear system of equations by numerical method GMRES (Generalized
 Minimal Residual) \cite{ref1}. We show, that with minor modifications of the
 initial MPI code, the SimGrid toolkit allows us to perform a test campaign of a
 real AIAC application on different computing architectures. The simulated
-results we obtained are in line with real results exposed in ??\AG[]{??}.
+results we obtained are in line with real results exposed in ??\AG[]{ref?}.
 SimGrid had allowed us to launch the application from a modest computing
 infrastructure by simulating different distributed architectures composed by
 clusters nodes interconnected by variable speed networks.  With selected
@@ -165,6 +166,9 @@ in the simulated environment, the experimental results have demonstrated not
 only the algorithm convergence within a reasonable time compared with the
 physical environment performance, but also a time saving of up to \np[\%]{40} in
 asynchronous mode.
+\AG{Il faudrait revoir la phrase précédente (couper en deux?).  Là, on peut
+  avoir l'impression que le gain de \np[\%]{40} est entre une exécution réelle
+  et une exécution simulée!}
 
 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
@@ -187,7 +191,9 @@ times generated by synchronizations are very penalizing. One way to overcome thi
 \textit{Asynchronous Iterations~-- Asynchronous Communications (AIAC)} model. Here, local computations do not need to
 wait for required data. Processors can then perform their iterations with the data present at that time. Figure~\ref{fig:aiac}
 illustrates this model where the gray blocks represent the computation phases, the white spaces the idle
-times and the arrows the communications. With this algorithmic model, the number of iterations required before the
+times and the arrows the communications.
+\AG{There are no ``white spaces'' on the figure.}
+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.
@@ -252,8 +258,11 @@ 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.
 
+%%% TODO: add some words+refs about SimGrid's accuracy and scalability.}
+
 \AG{Faut-il ajouter quelque-chose ?} 
-\CER{Comme tu as décrit la plateforme d'exécution, on peut ajouter éventuellement le fichier XML contenant des hosts dans les clusters formant la grille} 
+\CER{Comme tu as décrit la plateforme d'exécution, on peut ajouter éventuellement le fichier XML contenant des hosts dans les clusters formant la grille
+  \AG{Bof.}}
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \section{Simulation of the multisplitting method}