X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy2.git/blobdiff_plain/43740254c86b1d4323f48f5d9d015b172a58c1c0..bf4693302ab924b37253fdb2079ad75a8808a987:/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 3e1bb46..88731ec 100644
--- a/mpi-energy2-extension/Heter_paper.tex
+++ b/mpi-energy2-extension/Heter_paper.tex
@@ -733,7 +733,7 @@ frequency scaling factors are computed as a ratio between the computation time
 of the slowest node and the computation time of the node $i$ as follows:
 \begin{equation}
   \label{eq:Scp}
-  \Scp[ij] =  \frac{ \mathop{\max_{i=1,\dots N}}_{j=1,\dots,M}(\Tcp[ij])} {\Tcp[ij]}
+  \Scp[ij] =  \frac{ \mathop{\max\limits_{i=1,\dots N}}\limits_{j=1,\dots,M}(\Tcp[ij])} {\Tcp[ij]}
 \end{equation}
 Using the initial frequency scaling factors computed in (\ref{eq:Scp}), the
 algorithm computes the initial frequencies for all nodes as a ratio between the
@@ -855,25 +855,25 @@ The benchmarks have seven different classes, S, W, A, B, C, D and E, that repres
   \centering
   \begin{tabular}{|*{7}{c|}}
     \hline
-    Cluster     & CPU         & Max   & Min   & Diff. & no. of cores    & dynamic power   \\
-    Name        & model       & Freq. & Freq. & Freq. & per CPU         & of one core     \\
-                &             & GHz   & GHz   & GHz   &                 &           \\
+                &             & Max   & Min   & Diff. &                 &               \\
+    Cluster     & CPU         & Freq. & Freq. & Freq. & No. of cores    & Dynamic power \\
+    Name        & model       & GHz   & GHz   & GHz   & per CPU         & of one core   \\
     \hline
-                & Intel       & 2.3  & 1.2  & 0.1     & 6               & \np[W]{35} \\
-    Taurus      & Xeon        &       &       &       &                 &            \\
-                & E5-2630     &       &       &       &                 &            \\         
+                & Intel       &       &       &         &           &              \\
+    Taurus      & Xeon        & 2.3   & 1.2   & 0.1     & 6         & \np[W]{35}    \\
+                & E5-2630     &       &       &         &           &            \\         
     \hline
-                & Intel       & 2.53  & 1.2   & 0.133 & 4               & \np[W]{23} \\
-    Graphene    & Xeon        &       &       &       &                 &            \\
-                & X3440       &       &       &       &                 &            \\    
+                & Intel       &       &       &         &           &             \\
+    Graphene    & Xeon        & 2.53  & 1.2   & 0.133   & 4         & \np[W]{23}  \\
+                & X3440       &       &       &         &           &             \\    
     \hline
-                & Intel       & 2.5   & 2     & 0.5   & 4               & \np[W]{46} \\
-    Griffon     & Xeon        &       &       &       &                 &            \\
-                & L5420       &       &       &       &                 &            \\  
+                & Intel       &       &       &         &           &            \\
+    Griffon     & Xeon        & 2.5   & 2     & 0.5     & 4         & \np[W]{46}  \\
+                & L5420       &       &       &         &           &            \\  
     \hline
-                & Intel       & 2     & 1.2   & 0.1   & 8               & \np[W]{35} \\
-     Graphite   & Xeon        &       &       &       &                 &            \\
-                & E5-2650     &       &       &       &                 &            \\  
+                & Intel       &       &       &         &           &            \\
+     Graphite   & Xeon        & 2     & 1.2   & 0.1     & 8         & \np[W]{35} \\
+                & E5-2650     &       &       &         &           &            \\  
     \hline
   \end{tabular}
   \label{table:grid5000}
@@ -942,9 +942,7 @@ 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 power showed in Table \ref{table:grid5000}
-
-and the static 
+(\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.  
 
@@ -1097,7 +1095,18 @@ the one site one core scenario  when compared to the ratio of the multi-cores sc
 More energy reduction can be gained when this ratio is big because it pushes the proposed scaling algorithm to select smaller frequencies that decrease the dynamic power consumption. These experiments also showed that the energy 
 consumption and the execution times of the EP and MG benchmarks do not change significantly over these two
 scenarios  because there are no or small communications. Contrary to EP and MG, the  energy consumptions and the execution times of the rest of the  benchmarks  vary according to the  communication times that are different from one scenario to the other.
-  
+\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}%
+    \subfloat[The performance degradation of running NAS benchmarks over one core and multicores scenarios
+      ]{%
+    \includegraphics[width=.48\textwidth]{fig/per_d_mc.eps}\label{fig:per-d-mc}}\hspace{0.4cm}%
+    \subfloat[The tradeoff distance of running NAS benchmarks over one core and multicores scenarios]{%
+    \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*}  
   
 The energy saving percentages of all NAS benchmarks running over these two scenarios are presented in figure \ref{fig:eng-s-mc}. 
 The figure shows that  the energy saving percentages in the one 
@@ -1114,18 +1123,7 @@ in figure \ref{fig:dist-mc}. These  tradeoff distance between energy consumption
 
 
 
-\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}%
-    \subfloat[The performance degradation of running NAS benchmarks over one core and multicores scenarios
-      ]{%
-    \includegraphics[width=.48\textwidth]{fig/per_d_mc.eps}\label{fig:per-d-mc}}\hspace{0.4cm}%
-    \subfloat[The tradeoff distance of running NAS benchmarks over one core and multicores scenarios]{%
-    \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*}
+
 
 
 
@@ -1140,7 +1138,7 @@ The experiments have been executed with these two new static power scenarios  ov
 In these experiments, class D of the NAS parallel benchmarks are executed over the Nancy site. 16 computing nodes from the three clusters, Graphite, Graphene and Griffon, where used in this experiment. 
 
 
-\begin{figure}
+\begin{figure*}[t]
   \centering
   \subfloat[The energy saving percentages for the nodes executing the NAS benchmarks over the three power scenarios]{%
     \includegraphics[width=.48\textwidth]{fig/eng_pow.eps}\label{fig:eng-pow}} \hspace{0.4cm}%
@@ -1151,7 +1149,7 @@ In these experiments, class D of the NAS parallel benchmarks are executed over t
     \includegraphics[width=.48\textwidth]{fig/dist_pow.eps}\label{fig:dist-pow}}
   \label{fig:exp-pow}
   \caption{The experimental results of different static power scenarios}
-\end{figure}
+\end{figure*}
 
 
 
@@ -1250,8 +1248,7 @@ that the proposed algorithm outperforms the latter by selecting a vector of freq
 
 In the near future, we would like to develop a similar method that is adapted to
 asynchronous iterative applications where iterations are not synchronized and communications are overlapped with computations. 
- The development of
-such a method might require a new energy model because the
+The development of such a method might require a new energy model because the
 number of iterations is not known in advance and depends on
 the global convergence of the iterative system.
 
