\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
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
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
\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.
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.}}
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\section{Simulation of the multisplitting method}
