Merge branch 'master' of ssh://info.iut-bm.univ-fcomte.fr/mpi-energy2

[mpi-energy2.git] / Heter_paper.tex

diff --git a/Heter_paper.tex b/Heter_paper.tex
index e411a70b4b967c03e205f72db9b846a078965e4a..a88d174d6305b84e2fe52db18396ad4d28197e6d 100644 (file)
--- a/Heter_paper.tex
+++ b/Heter_paper.tex
@@ -76,34 +76,49 @@
 \maketitle
 
 \begin{abstract}
 \maketitle
 
 \begin{abstract}

-Computing platforms are consuming more and more energy due to the increase of the number of nodes composing them. To minimize the operating costs of these platforms many techniques have been used. Dynamic voltage and frequency scaling (DVFS) is one of them, it reduces the frequency of a CPU to lower its energy consumption. However, lowering the frequency of a CPU might increase the execution time of an application running on that processor. Therefore, the frequency that gives the best  tradeoff between the energy consumption and the performance of an application must be selected. 
-
-In this paper, a new online frequencies selecting algorithm for heterogeneous platforms is presented. It selects the frequency that gives  the best tradeoff between energy saving and
-performance degradation, for each node computing the message passing iterative application. The algorithm has a small overhead and works without training or profiling.
-It uses a new energy model for message passing iterative applications running on a heterogeneous platform. 
-The proposed algorithm was 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.
+Computing platforms are consuming more and more energy due to the increase of the number of nodes composing them. 
+To minimize the operating costs of these platforms many techniques have been used. Dynamic voltage and frequency 
+scaling (DVFS) is one of them, it reduces the frequency of a CPU to lower its energy consumption. However, 
+lowering the frequency of a CPU might increase the execution time of an application running on that processor. 
+Therefore, the frequency that gives the best  tradeoff between the energy consumption and the performance of an 
+application must be selected. 
+
+In this paper, a new online frequencies selecting algorithm for heterogeneous platforms is presented. 
+It selects the frequency that gives  the best tradeoff between energy saving and performance degradation, 
+for each node computing the message passing iterative application. The algorithm has a small overhead and 
+works without training or profiling. It uses a new energy model for message passing iterative applications 
+running on a heterogeneous platform. The proposed algorithm was 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.

 \end{abstract}
 
 \section{Introduction}
 \label{sec.intro}
 \end{abstract}
 
 \section{Introduction}
 \label{sec.intro}

-The need for more computing power is continually increasing. To partially satisfy this need, most supercomputers constructors just put more computing nodes in their platform. The resulting platform might achieve higher floating point operations per second (FLOPS), but the energy consumption and the heat dissipation are also increased. As an example, the chinese supercomputer Tianhe-2 had the highest FLOPS in November 2014 according to the Top500 list \cite{TOP500_Supercomputers_Sites}.  However, it was also the  most power hungry platform with its over 3 millions cores consuming around 17.8 megawatts.
-Moreover, according to the U.S. annual energy outlook 2014 
+The need for more computing power is continually increasing. To partially satisfy this need, most supercomputers 
+constructors just put more computing nodes in their platform. The resulting platform might achieve higher floating 
+point operations per second (FLOPS), but the energy consumption and the heat dissipation are also increased. 
+As an example, the chinese supercomputer Tianhe-2 had the highest FLOPS in November 2014 according to the Top500 
+list \cite{TOP500_Supercomputers_Sites}.  However, it was also the  most power hungry platform with its over 3 millions 
+cores consuming around 17.8 megawatts. Moreover, according to the U.S. annual energy outlook 2014 

 \cite{U.S_Annual.Energy.Outlook.2014}, the price of energy for 1 megawatt-hour 
 was approximately equal to \$70. 
 Therefore, the price of the energy consumed by the 
 Tianhe-2 platform is approximately more than \$10 millions each year. 
 \cite{U.S_Annual.Energy.Outlook.2014}, the price of energy for 1 megawatt-hour 
 was approximately equal to \$70. 
 Therefore, the price of the energy consumed by the 
 Tianhe-2 platform is approximately more than \$10 millions each year. 

-The computing platforms must be more energy efficient and offer the highest number of FLOPS per watt possible, such as the TSUBAME-KFC at the GSIC center of Tokyo which  
+The computing platforms must be more energy efficient and offer the highest number of FLOPS per watt possible, 
+such as the TSUBAME-KFC at the GSIC center of Tokyo which  

 became the top of the Green500 list in June 2014 \cite{Green500_List}. 
 This heterogeneous platform executes more than four  GFLOPS per watt.
 
 became the top of the Green500 list in June 2014 \cite{Green500_List}. 
 This heterogeneous platform executes more than four  GFLOPS per watt.
 

- Besides hardware improvements, there are many software techniques to lower the energy consumption of these platforms, such as scheduling, DVFS, ... DVFS is a widely  used process to reduce the energy 
-consumption of a processor by lowering its frequency. \textbf{put a reference to DVFS} However, it also the reduces the number of FLOPS executed by the processor which might increase the execution time of the application running over that processor.
-Therefore,  researchers used different optimization strategies to select the frequency that gives the best tradeoff   between the energy reduction and 
-performance degradation ratio.
-\textbf{you should talk about the first paper here and say that the algorithm was applied to a homogeneous platform then define what is a heterogeneous platform, you can take it from the firdt paragraph in section 3 }
+Besides hardware improvements, there are many software techniques to lower the energy consumption of these platforms, 
+such as scheduling, DVFS, ... DVFS is a widely  used process to reduce the energy consumption of a processor by lowering 
+its frequency \cite{Rizvandi_Some.Observations.on.Optimal.Frequency}. However, it also the reduces the number of FLOPS 
+executed by the processor which might increase  the execution time of the application running over that processor.
+Therefore, researchers used different optimization strategies to select the frequency that gives the best tradeoff  
+between the energy reduction and 
+performance degradation ratio. \textbf{In our previous paper \cite{Our_first_paper},  a frequency selecting algorithm 
+was proposed for distributed iterative application running over homogeneous platform. While in this paper the algorithm is  significantly adapted to run over a heterogeneous platform. This platform is a collection of heterogeneous computing nodes interconnected via a high speed homogeneous network.}

 
 

-In this paper, a frequency selecting algorithm is proposed. It  selects the vector of frequencies for a heterogeneous platform that runs a message passing iterative application,  that gives the maximum energy reduction and minimum 
+The proposed frequency selecting algorithm selects the vector of frequencies for a heterogeneous platform that runs a message passing iterative application,  that gives the maximum energy reduction and minimum 

 performance degradation ratio simultaneously. The algorithm has a very small 
 overhead, works online and does not need any training or profiling.  
 
 performance degradation ratio simultaneously. The algorithm has a very small 
 overhead, works online and does not need any training or profiling.  
 


@@ -113,13 +128,12 @@ execution time of message passing programs can be predicted.  It also presents a
 model that predicts the energy consumption of an application running over a heterogeneous platform. Section~\ref{sec.compet} presents
 the energy-performance objective function that maximizes the reduction of energy
 consumption while minimizing the degradation of the program's performance.
 model that predicts the energy consumption of an application running over a heterogeneous platform. Section~\ref{sec.compet} presents
 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.\textbf{the verification should be put here}  
+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. 
 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. 

-Finally, we conclude in Section~\ref{sec.concl} with a summary and some future works.
+Finally, in Section~\ref{sec.concl} the paper is ended with a summary and some future works.

 
 

-\textbf{never use we in an article and the algorithm is not heterogeneous! you cannot use scaling factors before defining what they are.}

 \section{Related works}
 \label{sec.relwork}
 DVFS is a technique enabled 
 \section{Related works}
 \label{sec.relwork}
 DVFS is a technique enabled 


@@ -168,22 +182,21 @@ In contrast to the above described papers, this paper presents the following con
        
 \item a new online frequency selecting algorithm for heterogeneous platforms. The algorithm has a very small 
       overhead and does not need for any training or profiling. It uses a new optimization function which simultaneously maximizes the performance and minimizes the energy consumption of a message passing iterative synchronous application .
        
 \item a new online frequency selecting algorithm for heterogeneous platforms. The algorithm has a very small 
       overhead and does not need for any training or profiling. It uses a new optimization function which simultaneously maximizes the performance and minimizes the energy consumption of a message passing iterative synchronous application .

+

       
 \end{enumerate}
 
 \section{The performance and energy consumption measurements on heterogeneous architecture}
 \label{sec.exe}
 
       
 \end{enumerate}
 
 \section{The performance and energy consumption measurements on heterogeneous architecture}
 \label{sec.exe}
 

-% \JC{The whole subsection ``Parallel Tasks Execution on Homogeneous Platform'',
-%   can be deleted if we need space, we can just say we are interested in this
-%   paper in homogeneous clusters}
+

 
 \subsection{The execution time of message passing distributed 
                 iterative applications on a heterogeneous platform}
 
 In this paper, we are interested in reducing the energy consumption of message
 passing distributed iterative synchronous applications running over
 
 \subsection{The execution time of message passing distributed 
                 iterative applications on a heterogeneous platform}
 
 In this paper, we are interested in reducing the energy consumption of message
 passing distributed iterative synchronous applications running over

-heterogeneous platforms. We define a heterogeneous platform as a collection of
+heterogeneous platforms. A heterogeneous platform is defined as a collection of

 heterogeneous computing nodes interconnected via a high speed homogeneous
 network. Therefore, each node has different characteristics such as computing
 power (FLOPS), energy consumption, CPU's frequency range, \dots{} but they all
 heterogeneous computing nodes interconnected via a high speed homogeneous
 network. Therefore, each node has different characteristics such as computing
 power (FLOPS), energy consumption, CPU's frequency range, \dots{} but they all


@@ -246,15 +259,36 @@ vector of scaling factors can be predicted using EQ (\ref{eq:perf}).
 where $TcpOld_i$ is the computation time  of processor $i$ during the first 
 iteration and $MinTcm$ is the communication time of the slowest processor from 
 the first iteration.  The model computes the maximum computation time 
 where $TcpOld_i$ is the computation time  of processor $i$ during the first 
 iteration and $MinTcm$ is the communication time of the slowest processor from 
 the first iteration.  The model computes the maximum computation time 

-with scaling factor from each node  added to the communication time of the 
+with scaling factor from each node  added to the communication time of the \subsection{The verifications of the proposed method}
+\label{sec.verif.method}
+The precision of the proposed algorithm mainly depends on the execution time prediction model defined in 
+EQ(\ref{eq:perf}) and the energy model computed by EQ(\ref{eq:energy}). 
+The energy model is also significantly dependent  on the execution time model because the static energy is 
+linearly related the execution time and the dynamic energy is related to the computation time. So, all of 
+the work presented in this paper is based on the execution time model. To verify this model, the predicted 
+execution time was compared to  the real execution time over Simgrid for all  the NAS parallel benchmarks 
+running class B on 8 or 9 nodes. The comparison showed that the proposed execution time model is very precise, 
+the maximum normalized difference between the predicted execution time  and the real execution time is equal 
+to 0.03 for all the NAS benchmarks.
+
+Since  the proposed algorithm is not an exact method and do not test all the possible solutions (vectors of scaling factors) 
+in the search space and to prove its efficiency, it was compared on small instances to a brute force search algorithm 
+that tests all the possible solutions. The brute force algorithm was applied to different NAS benchmarks classes with 
+different number of nodes. The solutions returned by the brute force algorithm and the proposed algorithm were identical 
+and the proposed algorithm was on average 10 times faster than the brute force algorithm. It has a small execution time: 
+for a heterogeneous cluster composed of four different types of nodes having the characteristics presented in 
+table~(\ref{table:platform}), it takes on average \np[ms]{0.04}  for 4 nodes and \np[ms]{0.15} on average for 144 nodes 
+to compute the best scaling factors vector.  The algorithm complexity is $O(F\cdot (N \cdot4) )$, where $F$ is the number 
+of iterations and $N$ is the number of computing nodes. The algorithm needs  from 12 to 20 iterations to select the best 
+vector of frequency scaling factors that gives the results of the sections (\ref{sec.res}) and (\ref{sec.compare}).

 slowest node, it means  only the  communication time without any slack time. 
 slowest node, it means  only the  communication time without any slack time. 

-Therefore, we can consider the execution time of the iterative application is 
+Therefore, the execution time of the iterative application is 

 equal to the execution time of one iteration as in EQ(\ref{eq:perf}) multiplied 
 by the number of iterations of that application.
 
 equal to the execution time of one iteration as in EQ(\ref{eq:perf}) multiplied 
 by the number of iterations of that application.
 

-This prediction model is developed from our model for predicting the execution time of 
+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}. 
 message passing distributed applications for homogeneous architectures~\cite{Our_first_paper}. 

-The execution time prediction model is used in our method for optimizing both 
+The execution time prediction model is used in the method for optimizing both 

 energy consumption and performance of iterative methods, which is presented in the 
 following sections.
 
 energy consumption and performance of iterative methods, which is presented in the 
 following sections.
 


@@ -296,8 +330,8 @@ voltage with respect to various frequency values in~\cite{Rauber_Analytical.Mode
 process of the frequency can be expressed by the scaling factor $S$ which is the
 ratio between the maximum and the new frequency as in EQ(\ref{eq:s}).
 The CPU governors are power schemes supplied by the operating
 process of the frequency can be expressed by the scaling factor $S$ which is the
 ratio between the maximum and the new frequency as in EQ(\ref{eq:s}).
 The CPU governors are power schemes supplied by the operating

-system's kernel to lower a core's frequency. we can calculate the new frequency 
-$F_{new}$ from EQ(\ref{eq:s}) as follow:
+system's kernel to lower a core's frequency. The new frequency 
+$F_{new}$ from EQ(\ref{eq:s}) can be calculated as follows:

 \begin{equation}
   \label{eq:fnew}
    F_\textit{new} = S^{-1} \cdot F_\textit{max}
 \begin{equation}
   \label{eq:fnew}
    F_\textit{new} = S^{-1} \cdot F_\textit{max}


@@ -323,7 +357,7 @@ and is given by the following equation:
 \end{equation}
 The static power is related to the power leakage of the CPU and is consumed during computation 
 and even when idle. As in~\cite{Rauber_Analytical.Modeling.for.Energy,Zhuo_Energy.efficient.Dynamic.Task.Scheduling}, 
 \end{equation}
 The static power is related to the power leakage of the CPU and is consumed during computation 
 and even when idle. As in~\cite{Rauber_Analytical.Modeling.for.Energy,Zhuo_Energy.efficient.Dynamic.Task.Scheduling}, 

-we assume that the static power of a processor is constant 
+ the static power of a processor is considered as constant 

 during idle and computation periods, and for all its available frequencies. 
 The static energy is the static power multiplied by the execution time of the program. 
 According to the execution time model in EQ(\ref{eq:perf}), the execution time of the program 
 during idle and computation periods, and for all its available frequencies. 
 The static energy is the static power multiplied by the execution time of the program. 
 According to the execution time model in EQ(\ref{eq:perf}), the execution time of the program 


@@ -358,8 +392,8 @@ for each  processor.  It is computed as follows:
 Reducing the frequencies of the processors according to the vector of
 scaling factors $(S_1, S_2,\dots, S_N)$ may degrade the performance of the
 application and thus, increase the static energy because the execution time is
 Reducing the frequencies of the processors according to the vector of
 scaling factors $(S_1, S_2,\dots, S_N)$ may degrade the performance of the
 application and thus, increase the static energy because the execution time is

-increased~\cite{Kim_Leakage.Current.Moore.Law}. We can measure the overall energy consumption for the iterative 
-application by measuring  the energy consumption for one iteration as in EQ(\ref{eq:energy}) 
+increased~\cite{Kim_Leakage.Current.Moore.Law}. The overall energy consumption for the iterative 
+application can be measured by measuring  the energy consumption for one iteration as in EQ(\ref{eq:energy}) 

 multiplied by the number of iterations of that application.
 
 
 multiplied by the number of iterations of that application.
 
 


@@ -388,8 +422,8 @@ The relation between the energy consumption and the execution time for an applic
 complex and nonlinear, Thus, unlike the relation between the execution time 
 and the scaling factor, the relation of the energy with the frequency scaling
 factors is nonlinear, for more details refer to~\cite{Freeh_Exploring.the.Energy.Time.Tradeoff}.  
 complex and nonlinear, Thus, unlike the relation between the execution time 
 and the scaling factor, the relation of the energy with the frequency scaling
 factors is nonlinear, for more details refer to~\cite{Freeh_Exploring.the.Energy.Time.Tradeoff}.  

-Moreover, they are not measured using the same metric.  To solve this problem, we normalize the
-execution time by computing the ratio between the new execution time (after 
+Moreover, they are not measured using the same metric.  To solve this problem,  the
+execution time is normalized by computing the ratio between the new execution time (after 

 scaling down the frequencies of some processors) and the initial one (with maximum 
 frequency for all nodes,) as follows:
 \begin{multline}
 scaling down the frequencies of some processors) and the initial one (with maximum 
 frequency for all nodes,) as follows:
 \begin{multline}


@@ -400,7 +434,7 @@ frequency for all nodes,) as follows:
 \end{multline}
 
 
 \end{multline}
 
 

-In the same way, we normalize the energy by computing the ratio between the consumed energy 
+In the same way, the energy is normalized by computing the ratio between the consumed energy 

 while scaling down the frequency and the consumed energy with maximum frequency for all nodes:
 \begin{multline}
   \label{eq:enorm}
 while scaling down the frequency and the consumed energy with maximum frequency for all nodes:
 \begin{multline}
   \label{eq:enorm}


@@ -421,9 +455,9 @@ reduction with minimum execution time reduction.
 
  
   
 
  
   

-Our solution for this problem is to make the optimization process for energy and 
-execution time follow the same direction.  Therefore, we inverse the equation of the 
-normalized execution time which gives the normalized performance equation, as follows:
+This problem can be solved by making the optimization process for energy and 
+execution time follow the same direction.  Therefore, the equation of the 
+normalized execution time is inverted which gives the normalized performance equation, as follows:

 \begin{multline}
   \label{eq:pnorm_inv}
   P_\textit{Norm} = \frac{T_\textit{Old}}{T_\textit{New}}\\
 \begin{multline}
   \label{eq:pnorm_inv}
   P_\textit{Norm} = \frac{T_\textit{Old}}{T_\textit{New}}\\


@@ -443,11 +477,11 @@ normalized execution time which gives the normalized performance equation, as fo
   \caption{The energy and performance relation}
 \end{figure}
 
   \caption{The energy and performance relation}
 \end{figure}
 

-Then, we can model our objective function as finding the maximum distance
+Then, the objective function can be modeled   as finding the maximum distance

 between the energy curve EQ~(\ref{eq:enorm}) and the  performance
 curve EQ~(\ref{eq:pnorm_inv}) over all available sets of scaling factors.  This
 represents the minimum energy consumption with minimum execution time (maximum 
 between the energy curve EQ~(\ref{eq:enorm}) and the  performance
 curve EQ~(\ref{eq:pnorm_inv}) over all available sets of scaling factors.  This
 represents the minimum energy consumption with minimum execution time (maximum 

-performance) at the same time, see figure~(\ref{fig:r1}) or figure~(\ref{fig:r2}). Then our objective
+performance) at the same time, see figure~(\ref{fig:r1}) or figure~(\ref{fig:r2}). Then the objective

 function has the following form:
 \begin{equation}
   \label{eq:max}
 function has the following form:
 \begin{equation}
   \label{eq:max}


@@ -457,15 +491,16 @@ function has the following form:
        \overbrace{E_\textit{Norm}(S_{ij})}^{\text{Minimize}} )
 \end{equation}
 where $N$ is the number of nodes and $F$ is the  number of available frequencies for each nodes. 
        \overbrace{E_\textit{Norm}(S_{ij})}^{\text{Minimize}} )
 \end{equation}
 where $N$ is the number of nodes and $F$ is the  number of available frequencies for each nodes. 

-Then we can select the optimal set of scaling factors that satisfies EQ~(\ref{eq:max}).  
-Our objective function can work with any energy model or any power values for each node 
+Then, the optimal set of scaling factors that satisfies EQ~(\ref{eq:max}) can be selected.  
+The objective function can work with any energy model or any power values for each node 

 (static and dynamic powers). However, the most energy reduction gain can be achieved when 
 the energy curve has a convex form as shown in~\cite{Zhuo_Energy.efficient.Dynamic.Task.Scheduling,Rauber_Analytical.Modeling.for.Energy,Hao_Learning.based.DVFS}.
 
 \section{The scaling factors selection algorithm for heterogeneous platforms }
 \label{sec.optim}
 
 (static and dynamic powers). However, the most energy reduction gain can be achieved when 
 the energy curve has a convex form as shown in~\cite{Zhuo_Energy.efficient.Dynamic.Task.Scheduling,Rauber_Analytical.Modeling.for.Energy,Hao_Learning.based.DVFS}.
 
 \section{The scaling factors selection algorithm for heterogeneous platforms }
 \label{sec.optim}
 

-In this section we  propose algorithm~(\ref{HSA}) which selects the frequency scaling factors 
+\subsection{The algorithm details}
+In this section algorithm~(\ref{HSA}) is presented. It selects the frequency scaling factors 

 vector that gives the best trade-off between minimizing the energy consumption  and maximizing 
 the performance of a message passing synchronous iterative application executed on a heterogeneous 
 platform. It works online during the execution time of the iterative message passing program.  
 vector that gives the best trade-off between minimizing the energy consumption  and maximizing 
 the performance of a message passing synchronous iterative application executed on a heterogeneous 
 platform. It works online during the execution time of the iterative message passing program.  


@@ -481,7 +516,7 @@ The nodes in a heterogeneous platform have different computing powers, thus whil
 passing iterative synchronous applications, fast nodes have to wait for the slower ones to finish their 
 computations before being able to synchronously communicate with them as in figure (\ref{fig:heter}). 
 These periods are called idle or slack times. 
 passing iterative synchronous applications, fast nodes have to wait for the slower ones to finish their 
 computations before being able to synchronously communicate with them as in figure (\ref{fig:heter}). 
 These periods are called idle or slack times. 

-Our algorithm takes into account this problem and tries to reduce these slack times when selecting the 
+The algorithm takes into account this problem and tries to reduce these slack times when selecting the 

 frequency scaling factors vector. At first, it selects initial frequency scaling factors that increase 
 the execution times of fast nodes and  minimize the  differences between  the  computation times of 
 fast and slow nodes. The value of the initial frequency scaling factor  for each node is inversely 
 frequency scaling factors vector. At first, it selects initial frequency scaling factors that increase 
 the execution times of fast nodes and  minimize the  differences between  the  computation times of 
 fast and slow nodes. The value of the initial frequency scaling factor  for each node is inversely 


@@ -578,7 +613,7 @@ which results in bigger energy savings.
     \EndWhile
     \State  Return $Sopt_1,Sopt_2,\dots,Sopt_N$
   \end{algorithmic}
     \EndWhile
     \State  Return $Sopt_1,Sopt_2,\dots,Sopt_N$
   \end{algorithmic}

-  \caption{Heterogeneous scaling algorithm}
+  \caption{frequency scaling factors selection algorithm}

   \label{HSA}
 \end{algorithm}
 
   \label{HSA}
 \end{algorithm}
 


@@ -602,15 +637,39 @@ which results in bigger energy savings.
   \label{dvfs}
 \end{algorithm}
 
   \label{dvfs}
 \end{algorithm}
 

+\subsection{The verifications of the proposed algorithm}
+\label{sec.verif.algo}
+The precision of the proposed algorithm mainly depends on the execution time prediction model defined in 
+EQ(\ref{eq:perf}) and the energy model computed by EQ(\ref{eq:energy}). 
+The energy model is also significantly dependent  on the execution time model because the static energy is 
+linearly related the execution time and the dynamic energy is related to the computation time. So, all of 
+the work presented in this paper is based on the execution time model. To verify this model, the predicted 
+execution time was compared to  the real execution time over SimGrid/SMPI simulator, v3.10~\cite{casanova+giersch+legrand+al.2014.versatile}, 
+for all  the NAS parallel benchmarks NPB v3.3 
+\cite{NAS.Parallel.Benchmarks}, running class B on 8 or 9 nodes. The comparison showed that the proposed execution time model is very precise, 
+the maximum normalized difference between the predicted execution time  and the real execution time is equal 
+to 0.03 for all the NAS benchmarks.
+
+Since  the proposed algorithm is not an exact method and do not test all the possible solutions (vectors of scaling factors) 
+in the search space and to prove its efficiency, it was compared on small instances to a brute force search algorithm 
+that tests all the possible solutions. The brute force algorithm was applied to different NAS benchmarks classes with 
+different number of nodes. The solutions returned by the brute force algorithm and the proposed algorithm were identical 
+and the proposed algorithm was on average 10 times faster than the brute force algorithm. It has a small execution time: 
+for a heterogeneous cluster composed of four different types of nodes having the characteristics presented in 
+table~(\ref{table:platform}), it takes on average \np[ms]{0.04}  for 4 nodes and \np[ms]{0.15} on average for 144 nodes 
+to compute the best scaling factors vector.  The algorithm complexity is $O(F\cdot (N \cdot4) )$, where $F$ is the number 
+of iterations and $N$ is the number of computing nodes. The algorithm needs  from 12 to 20 iterations to select the best 
+vector of frequency scaling factors that gives the results of the next sections.
+

 \section{Experimental results}
 \label{sec.expe}
 To evaluate the efficiency and the overall energy consumption reduction of algorithm~(\ref{HSA}), 
 \section{Experimental results}
 \label{sec.expe}
 To evaluate the efficiency and the overall energy consumption reduction of algorithm~(\ref{HSA}), 

-it was applied to the NAS parallel benchmarks NPB v3.3  \cite{NAS.Parallel.Benchmarks}. The experiments were executed 
-on the simulator SimGrid/SMPI v3.10~\cite{casanova+giersch+legrand+al.2014.versatile} which offers 
-easy tools to create a heterogeneous platform and run message passing applications over it. The 
-heterogeneous platform that was used in the experiments, had one core per node because just one 
-process was executed per node. The heterogeneous platform  was composed of four types of nodes. 
-Each type of nodes had different characteristics such as the maximum CPU frequency, the number of
+it was applied to the NAS parallel benchmarks NPB v3.3. The experiments were executed 
+on the simulator SimGrid/SMPI which offers easy tools to create a heterogeneous platform and run 
+message passing applications over it. The  heterogeneous platform that was used in the experiments, 
+had one core per node because just one  process was executed per node. 
+The heterogeneous platform  was composed of four types of nodes. Each type of nodes had different 
+characteristics such as the maximum CPU frequency, the number of

 available frequencies and the computational power, see table (\ref{table:platform}). The characteristics 
 of these different types of  nodes are inspired   from the specifications of real Intel processors. 
 The heterogeneous platform had up to 144 nodes and had nodes from the four types in equal proportions, 
 available frequencies and the computational power, see table (\ref{table:platform}). The characteristics 
 of these different types of  nodes are inspired   from the specifications of real Intel processors. 
 The heterogeneous platform had up to 144 nodes and had nodes from the four types in equal proportions, 


@@ -992,44 +1051,18 @@ results in less energy saving but less performance degradation.
 
 
 
 
 
 

-\subsection{The verifications of the proposed method}
-\label{sec.verif}
-The precision of the proposed algorithm mainly depends on the execution time prediction model defined in 
-EQ(\ref{eq:perf}) and the energy model computed by EQ(\ref{eq:energy}). 
-The energy model is also significantly dependent  on the execution time model because the static energy is 
-linearly related the execution time and the dynamic energy is related to the computation time. So, all of 
-the work presented in this paper is based on the execution time model. To verify this model, the predicted 
-execution time was compared to  the real execution time over Simgrid for all  the NAS parallel benchmarks 
-running class B on 8 or 9 nodes. The comparison showed that the proposed execution time model is very precise, 
-the maximum normalized difference between  the predicted execution time  and the real execution time is equal 
-to 0.03 for all the NAS benchmarks.

 
 

-Since  the proposed algorithm is not an exact method and do not test all the possible solutions (vectors of scaling factors) 
-in the search space and to prove its efficiency, it was compared on small instances to a brute force search algorithm 
-that tests all the possible solutions. The brute force algorithm was applied to different NAS benchmarks classes with 
-different number of nodes. The solutions returned by the brute force algorithm and the proposed algorithm were identical 
-and the proposed algorithm was on average 10 times faster than the brute force algorithm. It has a small execution time: 
-for a heterogeneous cluster composed of four different types of nodes having the characteristics presented in 
-table~(\ref{table:platform}), it takes on average \np[ms]{0.04}  for 4 nodes and \np[ms]{0.15} on average for 144 nodes 
-to compute the best scaling factors vector.  The algorithm complexity is $O(F\cdot (N \cdot4) )$, where $F$ is the number 
-of iterations and $N$ is the number of computing nodes. The algorithm needs  from 12 to 20 iterations to select the best 
-vector of frequency scaling factors that gives the results of the sections (\ref{sec.res}) and (\ref{sec.compare}) .

 
 \section{Conclusion}
 
 \section{Conclusion}

-\label{sec.concl}
-In this paper, we have presented a new online heterogeneous scaling algorithm
-that selects the best possible vector of frequency scaling factors. This vector 
-gives the maximum distance (optimal tradeoff) between the normalized energy and 
-the performance curves. In addition, we developed a new energy model for measuring  
+\label{sec.concl} 
+In this paper, a new online frequency selecting algorithm have 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 
 and predicting the energy of distributed iterative applications running over heterogeneous 

-cluster. The proposed method evaluated on Simgrid/SMPI  simulator to built a heterogeneous 
-platform to executes NAS parallel benchmarks. The results of the experiments showed the ability of
-the proposed algorithm to changes its behaviour to selects different scaling factors  when 
-the number of computing nodes and both of the static and the dynamic powers are changed. 
-
-In the future, we plan to improve this method to apply on asynchronous  iterative applications 
-where each task does not wait the others tasks to finish there works. This leads us to develop a new 
-energy model to an asynchronous iterative applications, where the number of iterations is not 
+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. 
+
+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 
+energy model because the number of iterations is not 

 known in advance and depends on the global convergence of the iterative system.
 
 \section*{Acknowledgment}
 known in advance and depends on the global convergence of the iterative system.
 
 \section*{Acknowledgment}