some corrections

[mpi-energy2.git] / Heter_paper.tex

diff --git a/Heter_paper.tex b/Heter_paper.tex
index d31891279930b9743c2e58c7b367fefb4c3a4c37..390b151e88eb39b8baefde8fe99d66d26083ea73 100644 (file)
--- a/Heter_paper.tex
+++ b/Heter_paper.tex
@@ -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 
 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 comparison’s results show that our algorithm gives better
+energy-time trade-off.}
+

 \end{abstract}
 
 \section{Introduction}
 \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 
 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}
 Finally, in Section~\ref{sec.concl} the paper is ended with a summary and some future works.
 
 \section{Related works}


@@ -1036,7 +1040,7 @@ 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 
 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), $EDP=Enegry * Delay$. 
+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 
 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 


@@ -1077,7 +1081,7 @@ that gave some enhancements to the energy and performance tradeoff.
 
 \begin{figure}[t]
   \centering
 
 \begin{figure}[t]
   \centering

-   \includegraphics[scale=0.6]{fig/compare_EDP.pdf}
+   \includegraphics[scale=0.5]{fig/compare_EDP.pdf}

   \caption{Tradeoff comparison for NAS benchmarks class C}
   \label{fig:compare_EDP}
 \end{figure}
   \caption{Tradeoff comparison for NAS benchmarks class C}
   \label{fig:compare_EDP}
 \end{figure}


@@ -1088,7 +1092,8 @@ that gave some enhancements to the energy and performance tradeoff.
 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 
 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 
 
 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