\item Hosts processors power (GFlops) can also influence on the results.
\item Finally, when submitting job batches for execution, the arguments values
passed to the program like the maximum number of iterations or the precision are critical. They allow us to ensure not only the convergence of the
- algorithm but also to get the main objective in getting an execution time in asynchronous communication less than in
- synchronous mode (i.e. GMRES).
+ algorithm but also to get the main objective in getting an execution time with the asynchronous multisplitting less than with synchronous GMRES.
\end{itemize}
The ratio between the simulated execution time of synchronous GMRES algorithm
\begin{table}[!t]
\centering
- \caption{2 clusters, each with 50 nodes}
+ \caption{Relative gain of the multisplitting algorithm compared to GMRES for
+ different configurations with 2 clusters, each one composed of 50 nodes.}
\label{tab.cluster.2x50}
\begin{mytable}{5}
After analyzing the outputs, generally, for the two clusters including one hundred hosts configuration (Tables~\ref{tab.cluster.2x50}), some combinations of parameters affecting
the results have given a relative gain more than 2.5, showing the effectiveness of the
-asynchronous performance compared to the synchronous mode.
+asynchronous multiplsitting compared to GMRES with two distant clusters.
With these settings, Table~\ref{tab.cluster.2x50} shows
-that after a deterioration of inter cluster network with a bandwidth of \np[Mbit/s]{5} and a latency in order of one hundredth of millisecond and a processor power
+that after setting the bandwidth of the inter cluster network to \np[Mbit/s]{5} and a latency in order of one hundredth of millisecond and a processor power
of one GFlops, an efficiency of about \np[\%]{40} is
obtained in asynchronous mode for a matrix size of 62 elements. It is noticed that the result remains
stable even we vary the residual error precision from \np{E-5} to \np{E-9}. By
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???}
+For our futur 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}
