X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy2.git/blobdiff_plain/5522fa519361f730b1085c89c4dd726a616c77b8..43740254c86b1d4323f48f5d9d015b172a58c1c0:/mpi-energy2-extension/Heter_paper.tex?ds=sidebyside

diff --git a/mpi-energy2-extension/Heter_paper.tex b/mpi-energy2-extension/Heter_paper.tex
index 431c0c7..3e1bb46 100644
--- a/mpi-energy2-extension/Heter_paper.tex
+++ b/mpi-energy2-extension/Heter_paper.tex
@@ -942,8 +942,10 @@ The NAS parallel benchmarks are executed over these two platforms
 The overall energy consumption of all the benchmarks solving the class D instance and
 using the proposed frequency selection algorithm is measured 
 using the equation of the reduced energy consumption, equation 
-(\ref{eq:energy}). This model uses the measured dynamic and static 
-power values  showed in Table \ref{table:grid5000}. The execution
+(\ref{eq:energy}). This model uses the measured dynamic power showed in Table \ref{table:grid5000}
+
+and the static 
+power is assumed to be equal to 20\% of the dynamic power. The execution
 time is measured for all the benchmarks over these different scenarios.  
 
 The energy consumptions  and the execution times for all the benchmarks are 
@@ -1001,7 +1003,7 @@ algorithm  select smaller frequencies for the powerful nodes which
 produces less energy consumption and thus more energy saving.
 The best energy saving percentage was obtained in the one site scenario with 16 nodes, the energy consumption was on average reduced up to 30\%.
 
-\begin{figure}
+\begin{figure*}[t]
   \centering
   \subfloat[The energy reduction while executing the NAS benchmarks over different scenarios ]{%
     \includegraphics[width=.48\textwidth]{fig/eng_s.eps}\label{fig:eng_s}} \hspace{0.4cm}%
@@ -1012,7 +1014,7 @@ The best energy saving percentage was obtained in the one site scenario with 16
     \includegraphics[width=.48\textwidth]{fig/dist.eps}\label{fig:dist}}
   \label{fig:exp-res}
   \caption{The experimental results of different scenarios}
-\end{figure}
+\end{figure*}
 Figure \ref{fig:per_d} presents the performance degradation percentages for all benchmarks over the two scenarios.
 The performance degradation percentage for the benchmarks running on two sites  with
 16 or 32  nodes is on average equal to 8.3\% or 4.7\% respectively. 
@@ -1050,8 +1052,8 @@ the participating number of cores from a certain cluster is equal to 14,
 in the multi-core scenario the selected nodes is equal to  4 nodes while using 
 3 or 4 cores from each node. The platforms with one  
 core per node and  multi-cores nodes are  shown in Table \ref{table:sen-mc}. 
-The energy consumptions and execution times of running the class D of the NAS parallel 
-benchmarks over these four different scenarios are presented 
+The energy consumptions and execution times of running  class D of the NAS parallel 
+benchmarks over these two different scenarios are presented 
 in  figures \ref{fig:eng-cons-mc} and \ref{fig:time-mc} respectively.
 
 \begin{table}[]
@@ -1112,7 +1114,7 @@ in figure \ref{fig:dist-mc}. These  tradeoff distance between energy consumption
 
 
 
-\begin{figure}
+\begin{figure*}[t]
   \centering
     \subfloat[The energy saving of running NAS benchmarks over one core and multicores scenarios]{%
     \includegraphics[width=.48\textwidth]{fig/eng_s_mc.eps}\label{fig:eng-s-mc}} \hspace{0.4cm}%
@@ -1123,11 +1125,11 @@ in figure \ref{fig:dist-mc}. These  tradeoff distance between energy consumption
     \includegraphics[width=.48\textwidth]{fig/dist_mc.eps}\label{fig:dist-mc}}
   \label{fig:exp-res}
   \caption{The experimental results of one core and multi-cores scenarios}
-\end{figure}
+\end{figure*}
 
 
 
-\subsection{Experiments with different static and dynamic powers consumption scenarios}
+\subsection{Experiments with different static power scenarios}
 \label{sec.pow_sen}
 
 In section \ref{sec.grid5000}, since it was not possible to measure the static power consumed by a CPU,   the static power was assumed to be equal to 20\% of the measured dynamic power. This power is consumed during the whole execution time, during computation and communication times. Therefore, when the DVFS operations are applied by the scaling algorithm and the CPUs' frequencies lowered, the execution time might increase and consequently the consumed static energy will be increased too. 
@@ -1203,7 +1205,7 @@ The experimental results, the energy saving, performance degradation and tradeof
 presented in the figures \ref{fig:edp-eng}, \ref{fig:edp-perf} and \ref{fig:edp-dist} respectively.
 
 
-\begin{figure}
+\begin{figure*}[t]
   \centering
   \subfloat[The energy reduction induced by the Maxdist method and the EDP method]{%
     \includegraphics[width=.48\textwidth]{fig/edp_eng}\label{fig:edp-eng}} \hspace{0.4cm}%
@@ -1213,7 +1215,7 @@ presented in the figures \ref{fig:edp-eng}, \ref{fig:edp-perf} and \ref{fig:edp-
     \includegraphics[width=.48\textwidth]{fig/edp_dist}\label{fig:edp-dist}}
   \label{fig:edp-comparison}
   \caption{The comparison results}
-\end{figure}
+\end{figure*}
 
 As shown in these figures, the proposed frequencies selection algorithm, Maxdist, outperforms the EDP algorithm in terms of energy consumption reduction and performance for all of the benchmarks executed over the two scenarios. 
 The proposed algorithm gives better results than EDP  because it