\begin{figure}
\centering
\subfloat[Homogeneous platform]{%
- \includegraphics[width=.33\textwidth]{fig/homo}\label{fig:r1}}%
+ \includegraphics[width=.30\textwidth]{fig/homo}\label{fig:r1}}%
\subfloat[Heterogeneous platform]{%
- \includegraphics[width=.33\textwidth]{fig/heter}\label{fig:r2}}
+ \includegraphics[width=.30\textwidth]{fig/heter}\label{fig:r2}}
\label{fig:rel}
\caption{The energy and performance relation}
\end{figure}
\begin{figure}
\centering
\subfloat[Energy saving]{%
- \includegraphics[width=.33\textwidth]{fig/energy}\label{fig:energy}}%
+ \includegraphics[width=.30\textwidth]{fig/energy}\label{fig:energy}}%
\subfloat[Performance degradation ]{%
- \includegraphics[width=.33\textwidth]{fig/per_deg}\label{fig:per_deg}}
+ \includegraphics[width=.30\textwidth]{fig/per_deg}\label{fig:per_deg}}
\label{fig:avg}
\caption{The energy and performance for all NAS benchmarks running with difference number of nodes}
\end{figure}
\includegraphics[width=.30\textwidth]{fig/sen_comp}\label{fig:sen_comp}}%
\subfloat[Comparison the selected frequency scaling factors of MG benchmark class C running on 8 nodes]{%
- \includegraphics[width=.34\textwidth]{fig/three_scenarios}\label{fig:scales_comp}}
+ \includegraphics[width=.30\textwidth]{fig/three_scenarios}\label{fig:scales_comp}}
\label{fig:comp}
\caption{The comparison of the three power scenarios}
\end{figure}
initial frequencies computed using the equation (\ref{eq:Fint}). The resulting algorithm is an exhaustive search algorithm that minimizes the EDP and has the initial frequencies values as an upper bound.
Both algorithms were applied to the parallel NAS benchmarks to compare their efficiency. Table \ref{table:compare_EDP} presents the results of comparing the execution times and the energy consumptions for both versions of the NAS benchmarks while running the class C of each benchmark over 8 or 9 heterogeneous nodes. \textcolor{red}{The results show that our algorithm gives better energy savings than Spiliopoulos et al. algorithm,
-on average it is up to 17\% higher for energy saving compared to their algorithm. The average of performance degradation percentage using our method is higher on average by 3.82\%.}
+on average it is up to 17\% higher for energy saving compared to their algorithm. The average of performance degradation percentage using our method is higher on average by 3.82\%. The positive values for energy saving and distance are mean that our method outperform Spiliopoulos et al. method, while the inverse is happen for the negative values. The negative values for performance degradation percentage are mean our method is has the less delay in time, while the positive values mean the inverse. }
For all benchmarks, our algorithm outperforms
Spiliopoulos et al. algorithm in term of energy and performance tradeoff \textcolor{red}{(on average it has up to 21\% of distance)}, see figure (\ref{fig:compare_EDP}) because it maximizes the distance between the energy saving and the performance degradation values while giving the same weight for both metrics.
