-Here, it is important to note that the AIAC QM algorithm offers a gain
-of about $50\%$ on the execution time, that is to say that the
-application takes half of the execution time than without mapping.
-
-\subsubsection{Parameters variation}
-\label{sec:xpvariation}
-
-After having evaluated mapping algorithms on the heterogeneity of
-distributed clusters, we now propose to change the parameters of AIAC
-QM and F-EC algorithms, in order to determine which values are the
-most accurate.
-
-To do these experiments, we used an architecture composed of 122
-computing nodes representing 506 computing cores, spread over 5
-clusters in 5 sites. In this architecture we used bi-cores (2
-clusters), quadri-cores (2 clusters) and bi-quadri-cores (1 cluster)
-machines. Its heterogeneity degree value is 4.98.
-%, which means that
-%computing nodes power is very heterogeneous.
-
-The parameters of each algorithm, $f$ (the search factor) for
-AIAC QM and $\delta$ (the amount of local dependencies) for F-EC,
-varied both with values $10\%$, $50\%$ and $90\%$. We used the CG
-multi-splitting application on 64 computing nodes. The results of
-these experiments are given in Table \ref{tab:expparams}. Results
-exposed in this table represent the gains in execution time provided
-by each algorithm with different parameters values.
-
-%\vspace{0.2cm}
-\renewcommand{\arraystretch}{1.5}
-
-\begin{table}[h!]
- \centering
- \begin{tabular}[h!]{|c||c|c|c|}
- \hline
- Parameters& $10\%$ & $50\%$ & $90\%$ \\
- \hline
- \hline
-% Simple & \multicolumn{3}{c|}{\textcolor{blue}{$39\%$}}\\
- SMa & \multicolumn{3}{c|}{$30\%$}\\
- \hline
-% AIAC QM & $35\%$ & \textcolor{blue}{$41\%$} & $35\%$ \\
- AIAC QM & $30\%$ & $32\%$ & $30\%$ \\
- \hline
-% F-EC & $39\%$ & $35\%$ & \textcolor{blue}{$45\%$} \\
- F-EC & $40\%$ & $37\%$ & $45\%$ \\
- \hline
- \end{tabular}
-% \caption{Parameters variations using a $500'000$ problem size on an
-% architecture of 5.37 heterogeneity degree}
- \caption{Gains in execution time with mapping algorithms parameters
- variations using the class E of the CG application using 64
- computing nodes}
-% \vspace*{-0.4cm}
- \label{tab:expparams}
-% \vspace*{-0.9cm}
-\end{table}
-
-%First of all, we can see that the Simple mapping provides the same
-%order of performances, as shown in the precedent section, so it is
-%not affected by the heterogeneity degree. Secondly,
-For the AIAC QM algorithm, we can note that the best value for its
-parameter $f$ is about $50\%$, but its impact is not big enough to
-indicate a specific configuration.
-% With a low heterogeneity degree, this mapping algorithm provides a
-% good performances improvement.
-Finally, and this is not surprising, the F-EC algorithm is more
-efficient with a factor $\delta$ near $100\%$, which directly comes
-from its aim. But we can see that it is more efficient to have a
-factor around $10\%$ than having one around $50\%$.
-
-We can note here, with a lower heterogeneity degree than in previous
-experiments, gains are lower and the difference between AIAC QM and
-F-EC (with parameters at $50\%$) is lower. It can be explained as the
-fact that more the heterogeneity degree tends to 0 more computing
-nodes are the same, so a mapping solution will not be efficient,
-except one only optimizing network latency.
+% Here, it is important to note that the AIAC QM algorithm offers a gain
+% of about $50\%$ on the execution time, that is to say that the
+% application takes half of the execution time than without mapping.
+
+% \subsubsection{Parameters variation}
+% \label{sec:xpvariation}
+
+% After having evaluated mapping algorithms on the heterogeneity of
+% distributed clusters, we now propose to change the parameters of AIAC
+% QM and F-EC algorithms, in order to determine which values are the
+% most accurate.
+
+% To do these experiments, we used an architecture composed of 122
+% computing nodes representing 506 computing cores, spread over 5
+% clusters in 5 sites. In this architecture we used bi-cores (2
+% clusters), quad-cores (2 clusters) and bi-quad-cores (1 cluster)
+% machines. Its heterogeneity degree value is 4.98.
+% %, which means that
+% %computing nodes power is very heterogeneous.
+
+% The parameters of each algorithm, $f$ (the search factor) for
+% AIAC QM and $\delta$ (the amount of local dependencies) for F-EC,
+% varied both with values $10\%$, $50\%$ and $90\%$. We used the CG
+% multi-splitting application on 64 computing nodes. The results of
+% these experiments are given in Table \ref{tab:expparams}. Results
+% reported in this table represent the gains in execution time provided
+% by each algorithm with different parameters values.
+
+% %\vspace{0.2cm}
+% \renewcommand{\arraystretch}{1.5}
+
+% \begin{table}[h!]
+% \centering
+% \begin{tabular}[h!]{|c||c|c|c|}
+% \hline
+% Parameters& $10\%$ & $50\%$ & $90\%$ \\
+% \hline
+% \hline
+% % Simple & \multicolumn{3}{c|}{\textcolor{blue}{$39\%$}}\\
+% SMa & \multicolumn{3}{c|}{$30\%$}\\
+% \hline
+% % AIAC QM & $35\%$ & \textcolor{blue}{$41\%$} & $35\%$ \\
+% AIAC QM & $30\%$ & $32\%$ & $30\%$ \\
+% \hline
+% % F-EC & $39\%$ & $35\%$ & \textcolor{blue}{$45\%$} \\
+% F-EC & $40\%$ & $37\%$ & $45\%$ \\
+% \hline
+% \end{tabular}
+% % \caption{Parameters variations using a $500'000$ problem size on an
+% % architecture of 5.37 heterogeneity degree}
+% \caption{Gains in execution time with mapping algorithms parameters
+% variations using the class E of the CG application using 64
+% computing nodes}
+% % \vspace*{-0.4cm}
+% \label{tab:expparams}
+% % \vspace*{-0.9cm}
+% \end{table}
+
+% %First of all, we can see that the Simple mapping provides the same
+% %order of performance, as shown in the precedent section, so it is
+% %not affected by the heterogeneity degree. Secondly,
+% For the AIAC QM algorithm, we can note that the best value for its
+% parameter $f$ is about $50\%$, but its impact is not big enough to
+% indicate a specific configuration.
+% % With a low heterogeneity degree, this mapping algorithm provides a
+% % good performance improvement.
+% Finally, and this is not surprising, the F-EC algorithm is more
+% efficient with a factor $\delta$ near $100\%$, which directly comes
+% from its aim. But we can see that it is more efficient to have a
+% factor around $10\%$ than having one around $50\%$.
+
+% We can note here, with a lower heterogeneity degree than in previous
+% experiments, gains are lower and the difference between AIAC QM and
+% F-EC (with parameters at $50\%$) is lower. It can be explained as the
+% fact that more the heterogeneity degree tends to 0 more computing
+% nodes are the same, so a mapping solution will not be efficient,
+% except one only optimizing network latency.
