X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy2.git/blobdiff_plain/9605046971a2c8e2559533218ec4dfde7654c465..e11cd9c26d47da75e3ff6ac3e218f644ea6e5314:/Heter_paper.tex?ds=sidebyside

diff --git a/Heter_paper.tex b/Heter_paper.tex
index f34524c..390b151 100644
--- a/Heter_paper.tex
+++ b/Heter_paper.tex
@@ -8,7 +8,7 @@
 \usepackage{algorithm}
 \usepackage{subfig}
 \usepackage{amsmath}
-
+\usepackage{multirow}
 \usepackage{url}
 \DeclareUrlCommand\email{\urlstyle{same}}
 
@@ -89,7 +89,10 @@ for each node computing the message passing iterative application. The algorithm
 works without training or profiling. It uses a new energy model for message passing iterative applications 
 running on a heterogeneous platform. The proposed algorithm is evaluated  on the Simgrid simulator while 
 running the NAS parallel benchmarks. The experiments demonstrated that it reduces the energy consumption 
-up to 35\% while limiting the performance degradation as much as possible.
+up to 35\% while limiting the performance degradation as much as possible. \textcolor{red}{Furthermore, we compare the
+proposed algorithm with other method. The comparisonâs results show that our algorithm gives better
+energy-time trade-off.}
+
 \end{abstract}
 
 \section{Introduction}
@@ -127,8 +130,9 @@ the energy-performance objective function that maximizes the reduction of energy
 consumption while minimizing the degradation of the program's performance.
 Section~\ref{sec.optim} details the proposed frequency selecting algorithm then the precision of the proposed algorithm is verified. 
 Section~\ref{sec.expe} presents the results of applying the algorithm on  the NAS parallel benchmarks and executing them 
-on a heterogeneous platform. It also shows the results of running three 
-different power scenarios and comparing them. 
+on a heterogeneous platform. It shows the results of running three 
+different power scenarios and comparing them. \textcolor{red}{Moreover, it also shows the comparison results
+between our method and other method.}
 Finally, in Section~\ref{sec.concl} the paper is ended with a summary and some future works.
 
 \section{Related works}
@@ -207,7 +211,7 @@ task which have the highest computation time and no slack time.
   
  \begin{figure}[t]
   \centering
-   \includegraphics[scale=0.6]{fig/commtasks}
+   \includegraphics[scale=0.5]{fig/commtasks}
   \caption{Parallel tasks on a heterogeneous platform}
   \label{fig:heter}
 \end{figure}
@@ -266,7 +270,7 @@ by the number of iterations of that application.
 
 This prediction model is developed from the model for predicting the execution time of 
 message passing distributed applications for homogeneous architectures~\cite{Our_first_paper}. 
-The execution time prediction model is used in the method for optimizing both 
+The execution time prediction model is uSpiliopoulossed in the method for optimizing both 
 energy consumption and performance of iterative methods, which is presented in the 
 following sections.
 
@@ -670,16 +674,16 @@ Finally, These nodes were connected via an ethernet network with 1 Gbit/s bandwi
                   &           & GHz      & GHz          &GHz             &              &       \\
     \hline
     1             &40         & 2.5      & 1.2          & 0.1            & 20~w         &4~w    \\
-                  &           &          &              &                &              &  \\
+         
     \hline
     2             &50         & 2.66     & 1.6          & 0.133          & 25~w         &5~w    \\
-                  &           &          &              &                &              &  \\
+                  
     \hline
     3             &60         & 2.9      & 1.2          & 0.1            & 30~w         &6~w    \\
-                  &           &          &              &                &              &  \\
+                  
     \hline
     4             &70         & 3.4      & 1.6          & 0.133          & 35~w         &7~w    \\
-                  &           &          &              &                &              &  \\
+                  
     \hline
   \end{tabular}
   \label{table:platform}
@@ -708,7 +712,7 @@ The other benchmarks such as BT and SP should be executed on $1, 4, 9, 16, 36, 6
   \centering
   \begin{tabular}{|*{7}{l|}}
     \hline
-    Method     & Execution     & Energy         & Energy      & Performance        & Distance      \\
+    Program     & Execution     & Energy         & Energy      & Performance        & Distance      \\
     name       & time/s        & consumption/J  & saving\%    & degradation\%      &               \\
     \hline
     CG         &  64.64        & 3560.39        &34.16        &6.72               &27.44       \\
@@ -735,7 +739,7 @@ The other benchmarks such as BT and SP should be executed on $1, 4, 9, 16, 36, 6
   \centering
   \begin{tabular}{|*{7}{l|}}
     \hline
-    Method     & Execution     & Energy         & Energy      & Performance        & Distance      \\
+    Program    & Execution     & Energy         & Energy      & Performance        & Distance      \\
     name       & time/s        & consumption/J  & saving\%    & degradation\%      &               \\
     \hline
     CG         &36.11    	   &3263.49	        &31.25	      &7.12	               &24.13     \\
@@ -762,7 +766,7 @@ The other benchmarks such as BT and SP should be executed on $1, 4, 9, 16, 36, 6
   \centering
   \begin{tabular}{|*{7}{l|}}
     \hline
-    Method     & Execution     & Energy         & Energy      & Performance        & Distance      \\
+    Program     & Execution     & Energy         & Energy      & Performance        & Distance      \\
     name       & time/s        & consumption/J  & saving\%    & degradation\%      &               \\
     \hline
     CG         &31.74	       &4373.90	        &26.29	      &9.57	               &16.72          \\
@@ -789,7 +793,7 @@ The other benchmarks such as BT and SP should be executed on $1, 4, 9, 16, 36, 6
   \centering
   \begin{tabular}{|*{7}{l|}}
     \hline
-    Method     & Execution     & Energy         & Energy      & Performance        & Distance      \\
+    Program    & Execution     & Energy         & Energy      & Performance        & Distance      \\
     name       & time/s        & consumption/J  & saving\%    & degradation\%      &               \\
     \hline
     CG         &32.35	       &6704.21	        &16.15	      &5.30	               &10.85           \\
@@ -816,7 +820,7 @@ The other benchmarks such as BT and SP should be executed on $1, 4, 9, 16, 36, 6
   \centering
   \begin{tabular}{|*{7}{l|}}
     \hline
-    Method     & Execution     & Energy         & Energy      & Performance        & Distance      \\
+    Program    & Execution     & Energy         & Energy      & Performance        & Distance      \\
     name       & time/s        & consumption/J  & saving\%    & degradation\%      &               \\
     \hline
     CG         &46.65	       &17521.83	    &8.13	      &1.68	               &6.45           \\
@@ -844,7 +848,7 @@ The other benchmarks such as BT and SP should be executed on $1, 4, 9, 16, 36, 6
   \centering
   \begin{tabular}{|*{7}{l|}}
     \hline
-    Method     & Execution     & Energy         & Energy      & Performance        & Distance     \\
+    Program    & Execution     & Energy         & Energy      & Performance        & Distance     \\
     name       & time/s        & consumption/J  & saving\%    & degradation\%      &              \\
     \hline
     CG         &56.92	       &41163.36        &4.00	      &1.10	               &2.90          \\
@@ -964,7 +968,7 @@ results in less energy saving but less performance degradation.
   \centering
   \begin{tabular}{|*{6}{l|}}
     \hline
-    Method     & Energy          & Energy      & Performance        & Distance     \\
+    Program    & Energy          & Energy      & Performance        & Distance     \\
     name       & consumption/J   & saving\%    & degradation\%      &              \\
     \hline
     CG         &4144.21          &22.42        &7.72                &14.70         \\
@@ -993,7 +997,7 @@ results in less energy saving but less performance degradation.
   \centering
   \begin{tabular}{|*{6}{l|}}
     \hline
-    Method     & Energy          & Energy      & Performance        & Distance     \\
+    Program    & Energy          & Energy      & Performance        & Distance     \\
     name       & consumption/J   & saving\%    & degradation\%      &              \\
     \hline
     CG         &2812.38	         &36.36	       &6.80                &29.56         \\
@@ -1017,11 +1021,11 @@ results in less energy saving but less performance degradation.
 
 \begin{figure}
   \centering
-  \subfloat[Comparison the average of the results on 8 nodes]{%
-    \includegraphics[width=.33\textwidth]{fig/sen_comp}\label{fig:sen_comp}}%
+  \subfloat[Comparison  of the results on 8 nodes]{%
+    \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=.33\textwidth]{fig/three_scenarios}\label{fig:scales_comp}}
+    \includegraphics[width=.34\textwidth]{fig/three_scenarios}\label{fig:scales_comp}}
   \label{fig:comp}
   \caption{The comparison of the three power scenarios}
 \end{figure}  
@@ -1029,13 +1033,67 @@ results in less energy saving but less performance degradation.
 
 
 
+\subsection{The comparison of the proposed scaling algorithm }
+\label{sec.compare_EDP}
+
+In this section, we compare our scaling  factors selection algorithm
+with Spiliopoulos et al. algorithm \cite{Spiliopoulos_Green.governors.Adaptive.DVFS}. 
+They developed an online frequency selecting algorithm running over multicore architecture. 
+The algorithm predicted both the energy and performance during the runtime of the program, then 
+selecting the frequencies that minimized the energy and delay products (EDP), $EDP=Enegry*Delay$. 
+To be able to compare with this algorithm, we used our energy and execution time models in prediction process,
+equations (\ref{eq:energy}) and  (\ref{eq:fnew}). Also their algorithm is adapted to taking into account 
+the heterogeneous platform to starts selecting the 
+initial frequencies using the equation (\ref{eq:Fint}). The algorithm built to test all possible frequencies as 
+a brute-force search algorithm. 
+
+The comparison results of running NAS benchmarks class C on 8 or 9 nodes are 
+presented in table \ref{table:compare_EDP}. The results show that our algorithm has a biggest energy saving percentage, 
+on average it has 29.76\% and thier algorithm has 25.75\%,
+while the average of performance degradation percentage is approximately the same, the average for our algorithm is 
+equal to 3.89\% and for their algorithm is equal to 4.03\%. In general, our algorithm outperforms 
+Spiliopoulos et al. algorithm in term of energy and performance tradeoff see figure (\ref{fig:compare_EDP}). 
+This because our algorithm maximized the difference (the distance) between the energy saving and the performance degradation 
+comparing to their EDP optimization function. It is also keeps the frequency of the slowest node without change 
+that gave some enhancements to the energy and performance tradeoff.
+
+
+\begin{table}[h]
+ \caption{Comparing the proposed algorithm}
+ \centering
+\begin{tabular}{|l|l|l|l|l|l|l|l|}
+\hline
+\multicolumn{2}{|l|}{\multirow{2}{*}{\begin{tabular}[c]{@{}l@{}}Program \\ name\end{tabular}}} & \multicolumn{2}{l|}{Energy saving \%} & \multicolumn{2}{l|}{Perf.  degradation \%} & \multicolumn{2}{l|}{Distance} \\ \cline{3-8} 
+\multicolumn{2}{|l|}{}                                                                         & EDP             & MaxDist          & EDP            & MaxDist           & EDP          & MaxDist        \\ \hline
+\multicolumn{2}{|l|}{CG}                                                                       & 27.58           & 31.25            & 5.82           & 7.12              & 21.76        & 24.13          \\ \hline
+\multicolumn{2}{|l|}{MG}                                                                       & 29.49           & 33.78            & 3.74           & 6.41              & 25.75        & 27.37          \\ \hline
+\multicolumn{2}{|l|}{LU}                                                                       & 19.55           & 28.33            & 0.0            & 0.01              & 19.55        & 28.22          \\ \hline
+\multicolumn{2}{|l|}{EP}                                                                       & 28.40           & 27.04            & 4.29           & 0.49              & 24.11        & 26.55          \\ \hline
+\multicolumn{2}{|l|}{BT}                                                                       & 27.68           & 32.32            & 6.45           & 7.87              & 21.23        & 24.43          \\ \hline
+\multicolumn{2}{|l|}{SP}                                                                       & 20.52           & 24.73            & 5.21           & 2.78              & 15.31         & 21.95         \\ \hline
+\multicolumn{2}{|l|}{FT}                                                                       & 27.03           & 31.02            & 2.75           & 2.54              & 24.28        & 28.48           \\ \hline
+
+\end{tabular}
+\label{table:compare_EDP}
+\end{table}
+
+
+
+\begin{figure}[t]
+  \centering
+   \includegraphics[scale=0.5]{fig/compare_EDP.pdf}
+  \caption{Tradeoff comparison for NAS benchmarks class C}
+  \label{fig:compare_EDP}
+\end{figure}
+
 
 \section{Conclusion}
 \label{sec.concl} 
 In this paper, a new online frequency selecting algorithm has been presented. It selects the best possible vector of frequency scaling factors that gives the maximum distance (optimal tradeoff) between the predicted energy and 
 the predicted performance curves for a heterogeneous platform. This algorithm uses a new energy model for measuring  
 and predicting the energy of distributed iterative applications running over heterogeneous 
-platform. To evaluate the proposed method, it  was  applied on the NAS parallel benchmarks and executed over a heterogeneous platform simulated by  Simgrid. The results of the experiments showed that the algorithm reduces up to 35\% the energy consumption of a message passing iterative method while limiting the degradation of the performance. The algorithm also  selects different scaling factors   according to the percentage of the computing and communication times, and according to the values of  the static and  dynamic powers of the CPUs. 
+platform. To evaluate the proposed method, it  was  applied on the NAS parallel benchmarks and executed over a heterogeneous platform simulated by  Simgrid. The results of the experiments showed that the algorithm reduces up to 35\% the energy consumption of a message passing iterative method while limiting the degradation of the performance. The algorithm also  selects different scaling factors   according to the percentage of the computing and communication times, and according to the values of  the static and  dynamic powers of the CPUs. \textcolor{red}{ We compare our algorithm with Spiliopoulos et al. algorithm, the comparison results showed that our 
+algorithm outperforms their algorithm in term of energy-time tradeoff.}
 
 In the near future, this method will be applied to real heterogeneous platforms to evaluate its performance in a real study case. It would also be interesting to evaluate its scalability over large scale heterogeneous platform and measure the energy consumption reduction it can produce. Afterward, we would like  to develop a similar method that is adapted to asynchronous  iterative applications 
 where each task does not wait for others tasks to finish there works. The development of such method might require a new 
@@ -1044,6 +1102,10 @@ known in advance and depends on the global convergence of the iterative system.
 
 \section*{Acknowledgment}
 
+This work has been partially supported by the Labex
+ACTION project (contract âANR-11-LABX-01-01â). As a PhD student,
+Mr. Ahmed Fanfakh, would like to thank the University of
+Babylon (Iraq) for supporting his work.
 
 
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