\section{Conclusion}
The simulation of the execution of parallel asynchronous iterative algorithms on large scale clusters has been presented.
In this work, we show that SIMGRID is an efficient simulation tool that allows us to
-reach the following three objectives:
+reach the following two objectives:
\begin{enumerate}
\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
-of the cluster and network specifications permitting to save time in
-executing the algorithm in asynchronous mode.
+ simulate algorithms for which execution of all parts of
+ the code is necessary. Using simulations before real executions is a nice
+ solution to detect potential scalability problems.
+
+\item To test the combination of the cluster and network specifications permitting to execute an asynchronous algorithm faster than a synchronous one.
\end{enumerate}
-Our results have shown that in certain conditions, asynchronous mode is
-speeder up to \np[\%]{40} comparing to the synchronous GMRES method
+Our results have shown that with two distant clusters, the asynchronous multisplitting is faster to \np[\%]{40} compared to the synchronous GMRES method
which is not negligible for solving complex practical problems with more
and more increasing size.
