neighbors, it is more efficient to save on near nodes than on far
ones. In the synchronous model, both heterogeneity and locality must
be taken into account in a balanced way. In the asynchronous model,
-since no synchronizations occurs, the heterogeneity issue is less
+since no synchronization occurs, the heterogeneity issue is less
important.
\caption{Gains in time of the execution of the class E of the CG
application on Arc1.1 using 64 nodes}
\label{tab:exph1E}
- \vspace*{-0.3cm}
+ \vspace*{-0.4cm}
\end{table}
%\vspace{-0.5cm}
\caption{Gains in time of the execution of the class F of the CG
application on Arc1.2 using 128 nodes}
\label{tab:exph1F}
- \vspace*{-0.3cm}
+ \vspace*{-0.4cm}
\end{table}
At first, we can see that the Simple Mapping algorithm, though it is
\caption{Gains in time of the execution of the class E of the CG
application on Arc2.1 using 64 nodes}
\label{tab:exph2E}
- \vspace*{-0.3cm}
+ \vspace*{-0.4cm}
\end{table}
\renewcommand{\arraystretch}{1.5}
\caption{Gains in time of the execution of the class F of the CG
application on Arc2.2 using 128 nodes}
\label{tab:exph2F}
- \vspace*{-0.3cm}
+ \vspace*{-0.4cm}
\end{table}
To begin with, these experiments confirm that a mapping algorithm is
in general on the same cluster for communication efficiency reasons),
an important part of the application is lost and we cannot restart
this part; so the whole application fails. A trade-off should be done
-by having some saving nodes in external clusters.
+by having some saving nodes in external clusters.\\
% \subsubsection*{Acknowledgements}