reach the following three objectives:
\begin{enumerate}
-\item To have a flexible configurable execution platform resolving the
-hard exercise to access to very limited but so solicited physical
-resources;
+\item To have a flexible configurable execution platform that allows us to
+ simulate asynchronous iterative algorithm for which execution of all parts of
+ the code is necessary. Using simulations before real execution is a nice
+ solution to detect the scalability problems.
+
\item to ensure the algorithm convergence with a reasonable time and
iteration number ;
\item and finally and more importantly, to find the correct combination
executing the algorithm in asynchronous mode.
\end{enumerate}
Our results have shown that in certain conditions, asynchronous mode is
-speeder up to \np[\%]{40} than executing the algorithm in synchronous mode
+speeder up to \np[\%]{40} comparing to the synchronous GMRES method
which is not negligible for solving complex practical problems with more
and more increasing size.
- Several studies have already addressed the performance execution time of
+Several studies have already addressed the performance execution time of
this class of algorithm. The work presented in this paper has
demonstrated an original solution to optimize the use of a simulation
tool to run efficiently an iterative parallel algorithm in asynchronous
mode in a grid architecture.
-\LZK{Perspectives???}
+In future works, we plan to extend our experimentations to larger scale platforms by increasing the number of computing cores and the number of clusters.
+We will also have to increase the size of the input problem which will require the use of a more powerful simulation platform. At last, we expect to compare our simulation results to real execution results on real architectures in order to experimentally validate our study.
\section*{Acknowledgment}
