adding the abstract

[mpi-energy2.git] / Heter_paper.tex

diff --git a/Heter_paper.tex b/Heter_paper.tex
index 3e88c0d9eb0c6829f4c3808f6c9a25eb9d9bf1b3..65232f04f870ef03b4b48f7e9cf6cc99481360b1 100644 (file)
--- a/Heter_paper.tex
+++ b/Heter_paper.tex
@@ -61,7 +61,6 @@
     Raphaël Couturier,
     Ahmed Fanfakh and
     Arnaud Giersch
     Raphaël Couturier,
     Ahmed Fanfakh and
     Arnaud Giersch

-the normalized performance equation, as follows:

   } 
   \IEEEauthorblockA{%
     FEMTO-ST Institute\\
   } 
   \IEEEauthorblockA{%
     FEMTO-ST Institute\\


@@ -77,34 +76,48 @@ the normalized performance equation, as follows:
 \maketitle
 
 \begin{abstract}
 \maketitle
 
 \begin{abstract}

-  
+Green computing emphasizes the importance of energy conservation, minimizing the negative impact 
+on the environment while achieving high performance and minimizing operating costs. So, energy reduction 
+process in a high performance clusters it can be archived using dynamic voltage and frequency 
+scaling (DVFS) technique, through reducing the frequency of a CPU. Using DVFS to lower levels 
+result in a high increase in performance degradation ratio. Therefore selecting the best frequencies 
+must give the best possible tradeoff between the energy and the performance of parallel program.
+
+In this paper we present a new online heterogeneous scaling algorithm that selects the best vector 
+of frequency scaling factors. These factors give the best tradeoff between the energy saving and the
+performance degradation. The algorithm has small overhead and works without training and profiling.
+We developed a new energy model for distributed iterative application running on heterogeneous cluster. 
+The proposed algorithm experimented  on Simgrid simulator that applying the NAS parallel benchmarks.
+It reduces the energy consumption up to 35\% while limits the performance degradation as much as possible.

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

-Modern processors continue to increased in a performance. 
-The CPUs constructors are competing to achieve maximum number 
+Modern processors continue increasing in performance, 
+the CPUs constructors are competing to achieve maximum number 

 of floating point operations per second (FLOPS). 
 Thus, the energy consumption and the heat dissipation are increased 
 drastically according to this increase. Because the number of FLOPS 
 of floating point operations per second (FLOPS). 
 Thus, the energy consumption and the heat dissipation are increased 
 drastically according to this increase. Because the number of FLOPS 

-is linearly related to the power consumption of a CPU~\cite{51}.  
-As an example of the more power hungry cluster, Tianhe-2 became in 
-the top of the Top500 list in June 2014 \cite{43}. It has more than 
-3 millions of cores and consumed more than 17.8 megawatts. 
-Moreover, according to the U.S. annual energy outlook 2014 \cite{60}, 
-the price of energy for 1 megawatt-hour was approximately equal to \$70. 
+is more related to the power consumption of a CPU
+~\cite{Luley_Energy.efficiency.evaluation.and.benchmarking}.  
+As an example of the most power hungry cluster, Tianhe-2 became in 
+the top of the Top500 list in June 2014 \cite{TOP500_Supercomputers_Sites}. 
+It has more than 3 millions of cores and consumed more than 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, we can consider the price of the energy consumption for the 
 Tianhe-2 platform is approximately more than \$10 millions  for 
 one year. For this reason, the heterogeneous clusters must be offer more 
 energy efficiency due to the increase in the energy cost and the environment 
 influences. Therefore, a green computing clusters with maximum number of 
 FLOPS per watt are required nowadays. For example, the GSIC center of Tokyo, 
 Therefore, we can consider the price of the energy consumption for the 
 Tianhe-2 platform is approximately more than \$10 millions  for 
 one year. For this reason, the heterogeneous clusters must be offer more 
 energy efficiency due to the increase in the energy cost and the environment 
 influences. Therefore, a green computing clusters with maximum number of 
 FLOPS per watt are required nowadays. For example, the GSIC center of Tokyo, 

-became the top of the Green500 list in June 2014 \cite{59}. This platform  
-has more than four thousand of  MFLOPS per watt. Dynamic voltage and frequency 
-scaling (DVFS) is a process used widely to reduce the energy consumption of 
-the processor. In a heterogeneous clusters enabled DVFS, many researchers 
+became the top of the Green500 list in June 2014 \cite{Green500_List}. 
+This heterogeneous platform has more than four thousand of  MFLOPS per watt. Dynamic 
+voltage and frequency scaling (DVFS) is a process used widely to reduce the energy 
+consumption of the processor. In heterogeneous clusters enabled DVFS, many researchers 

 used DVFS  in a different ways. DVFS can be minimized the energy consumption 
 used DVFS  in a different ways. DVFS can be minimized the energy consumption 

-but it leads to a disadvantage due to increase in performance degradation. 
+but it leads to a disadvantage due to the increase in performance degradation. 

 Therefore,  researchers used different optimization strategies to overcame 
 this problem. The best tradeoff relation between the energy reduction and 
 performance degradation ratio is became a key challenges in a heterogeneous 
 Therefore,  researchers used different optimization strategies to overcame 
 this problem. The best tradeoff relation between the energy reduction and 
 performance degradation ratio is became a key challenges in a heterogeneous 


@@ -128,55 +141,74 @@ Finally, we conclude in Section~\ref{sec.concl} with a summary and some future w
 
 \section{Related works}
 \label{sec.relwork}
 
 \section{Related works}
 \label{sec.relwork}

-Energy reduction process for a high performance clusters recently performed using 
+Energy reduction process for high performance clusters recently performed using 

 dynamic voltage and frequency scaling (DVFS) technique. DVFS is a technique enabled 
 dynamic voltage and frequency scaling (DVFS) technique. DVFS is a technique enabled 

-in a modern processors to scaled down both of the  voltage and the frequency of 
+in modern processors to scaled down both of the voltage and the frequency of 

 the CPU while it is in the computing mode to reduce the energy consumption. DVFS is 
 also  allowed in the graphical processors GPUs, to achieved the same goal. Applying 
 DVFS has a dramatical side effect if it is applied to minimum levels to gain more 
 the CPU while it is in the computing mode to reduce the energy consumption. DVFS is 
 also  allowed in the graphical processors GPUs, to achieved the same goal. Applying 
 DVFS has a dramatical side effect if it is applied to minimum levels to gain more 

-energy reduction, producing a high percentage of performance degradations for the 
+energy reduction, producing  a high percentage of performance degradations for the 

 parallel applications.  Many researchers used different strategies to solve this 
 parallel applications.  Many researchers used different strategies to solve this 

-nonlinear problem for example in~\cite{19,42}, their methods add big overheads to 
-the algorithm to select the suitable frequency.  In this paper we  present a method 
-to find the optimal set of frequency scaling factors for a heterogeneous cluster to 
-simultaneously optimize both the energy and the execution time  without adding a big 
-overhead. This work is developed from our previous work of a homogeneous cluster~\cite{45}. 
+nonlinear problem for example in
+~\cite{Hao_Learning.based.DVFS,Dhiman_Online.Learning.Power.Management}, their methods 
+add big overheads to the algorithm to select the suitable frequency.  
+In this paper we  present a method 
+to find the optimal set of frequency scaling factors for heterogeneous cluster to 
+simultaneously optimize both the energy and the execution time  without adding big 
+overhead. This work is developed from our previous work of homogeneous cluster~\cite{Our_first_paper}. 

 Therefore we are interested to present some works that concerned the heterogeneous clusters 
 enabled DVFS. In general, the heterogeneous cluster works fall into two categorizes: 
 GPUs-CPUs heterogeneous clusters and CPUs-CPUs heterogeneous clusters. In GPUs-CPUs 
 Therefore we are interested to present some works that concerned the heterogeneous clusters 
 enabled DVFS. In general, the heterogeneous cluster works fall into two categorizes: 
 GPUs-CPUs heterogeneous clusters and CPUs-CPUs heterogeneous clusters. In GPUs-CPUs 

-heterogeneous clusters some parallel tasks executed on a GPUs and the others executed 
-on a CPUs. As an example of this works, Luley et al.~\cite{51}, proposed  a heterogeneous 
+heterogeneous clusters some parallel tasks executed on  GPUs and the others executed 
+on  CPUs. As an example of this works, Luley et al.
+~\cite{Luley_Energy.efficiency.evaluation.and.benchmarking}, proposed  a heterogeneous 

 cluster composed of Intel Xeon CPUs and NVIDIA GPUs. Their main goal is to determined the 
 energy efficiency as a function of performance per watt, the best tradeoff is done when the 
 cluster composed of Intel Xeon CPUs and NVIDIA GPUs. Their main goal is to determined the 
 energy efficiency as a function of performance per watt, the best tradeoff is done when the 

-performance per watt function is maximized. In the work of Kia Ma et al.~\cite{49}, 
-They developed a scheduling algorithm to distributed different workloads proportional 
-to the computing power of the node to be executed on a CPU or a GPU, emphasize all tasks 
-must be finished in the same time. 
-Recently, Rong et al.~\cite{50}, Their study explain that a heterogeneous clusters enabled 
-DVFS using GPUs and CPUs gave better energy and performance efficiency than other clusters 
-composed of only CPUs. The CPUs-CPUs heterogeneous clusters consist of number of computing 
-nodes  all of the type CPU. Our work in this paper can be classified to this type of the 
-clusters. As an example of this works see  Naveen et al.~\cite{52} work, They developed a 
-policy to dynamically assigned the frequency to a heterogeneous cluster. The goal is to 
-minimizing a fixed metric of $energy*delay^2$. Where our proposed method is automatically 
+performance per watt function is maximized. In the work of Kia Ma et al.
+~\cite{KaiMa_Holistic.Approach.to.Energy.Efficiency.in.GPU-CPU}, they developed a scheduling 
+algorithm to distributed different workloads proportional to the computing power of the node 
+to be executed on CPU or GPU, emphasize all tasks must be finished in the same time. 
+Recently, Rong et al.~\cite{Rong_Effects.of.DVFS.on.K20.GPU}, Their study explain that 
+a heterogeneous clusters enabled DVFS using GPUs and CPUs gave better energy and performance 
+efficiency than other clusters composed of only CPUs. 
+The CPUs-CPUs heterogeneous clusters consist of number of computing nodes  all of the type CPU. 
+Our work in this paper can be classified to this type of the clusters. 
+As an example of these works see  Naveen et al.~\cite{Naveen_Power.Efficient.Resource.Scaling} work, 
+They developed a policy to dynamically assigned the frequency to a heterogeneous cluster. 
+The goal is to minimizing a fixed metric of $energy*delay^2$. Where our proposed method is automatically 

 optimized  the relation between the energy and the delay of the iterative applications. 
 optimized  the relation between the energy and the delay of the iterative applications. 

-Other works such as Lizhe et al.~\cite{53}, their algorithm divided the executed tasks into 
-two types: the critical and non critical tasks. The algorithm scaled down the frequency of 
-the non critical tasks as function to the  amount of the slack and communication times that 
-have with maximum of performance degradation percentage of 10\%. In our method there is no 
+Other works such as Lizhe et al.~\cite{Lizhe_Energy.aware.parallel.task.scheduling}, 
+their algorithm divided the executed tasks into two types: the critical and 
+non critical tasks. The algorithm scaled down the frequency of the non critical tasks 
+as function to the  amount of the slack and communication times that 
+have with maximum of performance degradation percentage less than 10\%. In our method there is no 

 fixed bounds for performance degradation percentage and the bound is dynamically computed 
 according to the energy and the performance tradeoff relation of the executed application. 
 There are some approaches used a heterogeneous cluster composed from two different types 
 fixed bounds for performance degradation percentage and the bound is dynamically computed 
 according to the energy and the performance tradeoff relation of the executed application. 
 There are some approaches used a heterogeneous cluster composed from two different types 

-of Intel and AMD processors such as~\cite{54} and \cite{55}, they predicated  both the energy 
+of Intel and AMD processors such as~\cite{Joshi_Blackbox.prediction.of.impact.of.DVFS} 
+and \cite{Spiliopoulos_Green.governors.Adaptive.DVFS}, they predicated  both the energy 

 and the performance for each frequency gear, then the algorithm selected the best gear that gave 
 the best tradeoff. In contrast our algorithm works over a heterogeneous  platform composed of 
 and the performance for each frequency gear, then the algorithm selected the best gear that gave 
 the best tradeoff. In contrast our algorithm works over a heterogeneous  platform composed of 

-four different types of processors. Others approaches such as \cite{56} and \cite{57}, they 
-are selected the best frequencies for a specified heterogeneous clusters offline using some 
+four different types of processors. Others approaches such as 
+\cite{Shelepov_Scheduling.on.Heterogeneous.Multicore} and \cite{Li_Minimizing.Energy.Consumption.for.Frame.Based.Tasks}, 
+they are selected the best frequencies for a specified heterogeneous clusters offline using some 

 heuristic methods. While our proposed algorithm works online during the execution time of 
 heuristic methods. While our proposed algorithm works online during the execution time of 

-iterative application. Greedy dynamic approach used by Chen et al.~\cite{58},  minimized 
-the power consumption of a heterogeneous severs  with time/space complexity, this approach 
+iterative application. Greedy dynamic approach used by Chen et al.~\cite{Chen_DVFS.under.quality.of.service.requirements},  
+minimized the power consumption of a heterogeneous severs  with time/space complexity, this approach 

 had considerable overhead. In our proposed scaling algorithm has very small overhead and 
 had considerable overhead. In our proposed scaling algorithm has very small overhead and 

-it is works without any previous analysis for the application time complexity. 
+it is works without any previous analysis for the application time complexity. The primary 
+contributions of our paper are :
+\begin{enumerate}
+\item It is presents  a new online heterogeneous scaling algorithm which has very small 
+      overhead and not need for any training and profiling.
+\item It is develops a new energy model for iterative distributed applications running over 
+       a heterogeneous clusters, taking into account the communication and slack times.
+\item The proposed scaling algorithm predicts both the energy and the execution time 
+      of the iterative application.
+\item It demonstrates a new optimization function which maximize the performance and 
+      minimize the energy consumption simultaneously.
+      
+\end{enumerate}

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


@@ -196,12 +228,12 @@ network. Therefore, each node has different characteristics such as computing
 power (FLOPS), energy consumption, CPU's frequency range, \dots{} but they all
 have the same network bandwidth and latency.
 
 power (FLOPS), energy consumption, CPU's frequency range, \dots{} but they all
 have the same network bandwidth and latency.
 

-The  overall execution time  of a distributed iterative synchronous application 
+The overall execution time  of a distributed iterative synchronous application 

 over a heterogeneous platform  consists of the sum of the computation time and 
 the communication time for every iteration on a node. However, due to the 
 heterogeneous computation power of the computing nodes, slack times might occur 
 when fast nodes have to  wait, during synchronous communications, for  the slower 
 over a heterogeneous platform  consists of the sum of the computation time and 
 the communication time for every iteration on a node. However, due to the 
 heterogeneous computation power of the computing nodes, slack times might occur 
 when fast nodes have to  wait, during synchronous communications, for  the slower 

-nodes to finish  their computations (see Figure~(\ref{fig:heter}). 
+nodes to finish  their computations (see Figure~(\ref{fig:heter})). 

 Therefore,  the overall execution time  of the program is the execution time of the slowest
 task which have the highest computation time and no slack time.
   
 Therefore,  the overall execution time  of the program is the execution time of the slowest
 task which have the highest computation time and no slack time.
   


@@ -230,8 +262,9 @@ as in EQ (\ref{eq:s}).
  The execution time of the computation part is linearly proportional to the 
  frequency scaling factor $S$ but  the communication time is not affected by the 
  scaling factor because  the processors involved remain idle during the  
  The execution time of the computation part is linearly proportional to the 
  frequency scaling factor $S$ but  the communication time is not affected by the 
  scaling factor because  the processors involved remain idle during the  

- communications~\cite{17}. The communication time for a task is the summation of 
- periods of time that begin with an MPI call for sending or receiving   a message 
+ communications~\cite{Freeh_Exploring.the.Energy.Time.Tradeoff}. 
+ The communication time for a task is the summation of  periods of 
+ time that begin with an MPI call for sending or receiving   a message 

  till the message is synchronously sent or received.
 
 Since in a heterogeneous platform, each node has different characteristics,
  till the message is synchronously sent or received.
 
 Since in a heterogeneous platform, each node has different characteristics,


@@ -258,28 +291,30 @@ Therefore, we can consider 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 based on our model for predicting the execution time of 
-message passing distributed applications for homogeneous architectures~\cite{45}. 
+This prediction model is developed from our 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 our method for optimizing both 
 energy consumption and performance of iterative methods, which is presented in the 
 following sections.
 
 
 \subsection{Energy model for heterogeneous platform}
 The execution time prediction model is used in our method for optimizing both 
 energy consumption and performance of iterative methods, which is presented in the 
 following sections.
 
 
 \subsection{Energy model for heterogeneous platform}

-Many researchers~\cite{9,3,15,26} divide the power consumed by a processor into
+Many researchers~\cite{Malkowski_energy.efficient.high.performance.computing,
+Rauber_Analytical.Modeling.for.Energy,Zhuo_Energy.efficient.Dynamic.Task.Scheduling,
+Rizvandi_Some.Observations.on.Optimal.Frequency} divide the power consumed by a processor into

 two power metrics: the static and the dynamic power.  While the first one is
 consumed as long as the computing unit is turned on, the latter is only consumed during
 two power metrics: the static and the dynamic power.  While the first one is
 consumed as long as the computing unit is turned on, the latter is only consumed during

-computation times.  The dynamic power $P_{d}$ is related to the switching
+computation times.  The dynamic power $Pd$ is related to the switching

 activity $\alpha$, load capacitance $C_L$, the supply voltage $V$ and
 operational frequency $F$, as shown in EQ(\ref{eq:pd}).
 \begin{equation}
   \label{eq:pd}
 activity $\alpha$, load capacitance $C_L$, the supply voltage $V$ and
 operational frequency $F$, as shown in EQ(\ref{eq:pd}).
 \begin{equation}
   \label{eq:pd}

-  P_\textit{d} = \alpha \cdot C_L \cdot V^2 \cdot F
+  Pd = \alpha \cdot C_L \cdot V^2 \cdot F

 \end{equation}
 \end{equation}

-The static power $P_{s}$ captures the leakage power as follows:
+The static power $Ps$ captures the leakage power as follows:

 \begin{equation}
   \label{eq:ps}
 \begin{equation}
   \label{eq:ps}

-   P_\textit{s}  = V \cdot N_{trans} \cdot K_{design} \cdot I_{leak}
+   Ps  = V \cdot N_{trans} \cdot K_{design} \cdot I_{leak}

 \end{equation}
 where V is the supply voltage, $N_{trans}$ is the number of transistors,
 $K_{design}$ is a design dependent parameter and $I_{leak}$ is a
 \end{equation}
 where V is the supply voltage, $N_{trans}$ is the number of transistors,
 $K_{design}$ is a design dependent parameter and $I_{leak}$ is a


@@ -287,19 +322,18 @@ technology-dependent parameter.  The energy consumed by an individual processor
 to execute a given program can be computed as:
 \begin{equation}
   \label{eq:eind}
 to execute a given program can be computed as:
 \begin{equation}
   \label{eq:eind}

-   E_\textit{ind} =  P_\textit{d} \cdot Tcp + P_\textit{s} \cdot T
+   E_\textit{ind} =  Pd \cdot Tcp + Ps \cdot T

 \end{equation}
 \end{equation}

-where $T$ is the execution time of the program, $T_{cp}$ is the computation
-time and $T_{cp} \leq T$.  $T_{cp}$ may be equal to $T$ if there is no
+where $T$ is the execution time of the program, $Tcp$ is the computation
+time and $Tcp \leq T$.  $Tcp$ may be equal to $T$ if there is no

 communication and no slack time.
 
 communication and no slack time.
 

-The main objective of DVFS operation is to
-reduce the overall energy consumption~\cite{37}.  The operational frequency $F$
-depends linearly on the supply voltage $V$, i.e., $V = \beta \cdot F$ with some
+The main objective of DVFS operation is to reduce the overall energy consumption~\cite{Le_DVFS.Laws.of.Diminishing.Returns}.  
+The operational frequency $F$ depends linearly on the supply voltage $V$, i.e., $V = \beta \cdot F$ with some

 constant $\beta$.  This equation is used to study the change of the dynamic
 constant $\beta$.  This equation is used to study the change of the dynamic

-voltage with respect to various frequency values in~\cite{3}.  The reduction
+voltage with respect to various frequency values in~\cite{Rauber_Analytical.Modeling.for.Energy}.  The reduction

 process of the frequency can be expressed by the scaling factor $S$ which is the
 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}).
+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:
 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:


@@ -318,7 +352,7 @@ where $ {P}_\textit{dNew}$  and $P_{dOld}$ are the  dynamic power consumed with
 new frequency and the maximum frequency respectively.
 
 According to EQ(\ref{eq:pdnew}) the dynamic power is reduced by a factor of $S^{-3}$ when 
 new frequency and the maximum frequency respectively.
 
 According to EQ(\ref{eq:pdnew}) the dynamic power is reduced by a factor of $S^{-3}$ when 

-reducing the frequency by a factor of $S$~\cite{3}. Since the FLOPS of a CPU is proportional 
+reducing the frequency by a factor of $S$~\cite{Rauber_Analytical.Modeling.for.Energy}. Since the FLOPS of a CPU is proportional 

 to the frequency of a CPU, the computation time is increased proportionally to $S$.  
 The new dynamic energy is the  dynamic power multiplied by the new time of computation 
 and is given by the following equation:
 to the frequency of a CPU, the computation time is increased proportionally to $S$.  
 The new dynamic energy is the  dynamic power multiplied by the new time of computation 
 and is given by the following equation:


@@ -327,7 +361,8 @@ and is given by the following equation:
    E_\textit{dNew} = P_{dOld} \cdot S^{-3} \cdot (Tcp \cdot S)= S^{-2}\cdot P_{dOld} \cdot  Tcp 
 \end{equation}
 The static power is related to the power leakage of the CPU and is consumed during computation 
    E_\textit{dNew} = P_{dOld} \cdot S^{-3} \cdot (Tcp \cdot S)= S^{-2}\cdot P_{dOld} \cdot  Tcp 
 \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{3,46}, we assume that the static power of a processor is constant 
+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 

 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 


@@ -336,7 +371,7 @@ to the frequency scaling factor, while this scaling factor does not affect the c
 The static energy of a processor after scaling its frequency is computed as follows: 
 \begin{equation}
   \label{eq:Estatic}
 The static energy of a processor after scaling its frequency is computed as follows: 
 \begin{equation}
   \label{eq:Estatic}

- E_\textit{s} = P_\textit{s} \cdot (Tcp \cdot S  + Tcm)
+ E_\textit{s} = Ps \cdot (Tcp \cdot S  + Tcm)

 \end{equation}
 
 In the considered heterogeneous platform, each processor $i$ might have different dynamic and 
 \end{equation}
 
 In the considered heterogeneous platform, each processor $i$ might have different dynamic and 


@@ -362,7 +397,7 @@ 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{36}. We can measure the overall energy consumption for the iterative 
+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}) 
 multiplied by the number of iterations of that application.
 
 application by measuring  the energy consumption for one iteration as in EQ(\ref{eq:energy}) 
 multiplied by the number of iterations of that application.
 


@@ -380,7 +415,7 @@ of the application might not be the optimal one. It is not trivial to select the
 frequency scaling factor for each processor while considering the characteristics of each processor 
 (computation power, range of frequencies, dynamic and static powers) and the task executed 
 (computation/communication ratio) in order to reduce the overall energy consumption and not 
 frequency scaling factor for each processor while considering the characteristics of each processor 
 (computation power, range of frequencies, dynamic and static powers) and the task executed 
 (computation/communication ratio) in order to reduce the overall energy consumption and not 

-significantly increase the execution time. In our previous work~\cite{45}, we  proposed a method 
+significantly increase the execution time. In our previous work~\cite{Our_first_paper}, we  proposed a method 

 that selects the optimal frequency scaling factor for a homogeneous cluster executing a message 
 passing iterative synchronous application while giving the best trade-off  between the energy 
 consumption and the performance for such applications. In this work we are interested in 
 that selects the optimal frequency scaling factor for a homogeneous cluster executing a message 
 passing iterative synchronous application while giving the best trade-off  between the energy 
 consumption and the performance for such applications. In this work we are interested in 


@@ -391,8 +426,8 @@ between energy consumption and performance.
 The relation between the energy consumption and the execution time for an application is 
 complex and nonlinear, Thus, unlike the relation between the execution time 
 and the scaling factor, the relation of the energy with the frequency scaling
 The relation between the energy consumption and the execution time for an application is 
 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{17}.  Moreover, they are
-not measured using the same metric.  To solve this problem, we normalize the
+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 
 scaling down the frequencies of some processors) and the initial one (with maximum 
 frequency for all nodes,) as follows:
 execution time 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:


@@ -464,7 +499,7 @@ where $N$ is the number of nodes and $F$ is the  number of available frequencies
 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 
 (static and dynamic powers). However, the most energy reduction gain can be achieved when 
 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 
 (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{15,3,19}.
+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}
 
 \section{The scaling factors selection algorithm for heterogeneous platforms }
 \label{sec.optim}


@@ -609,7 +644,7 @@ which results in bigger energy savings.
 \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{44}. The experiments were executed 
+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 
 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 


@@ -620,9 +655,9 @@ of these different types of  nodes are inspired   from the specifications of rea
 The heterogeneous platform had up to 144 nodes and had nodes from the four types in equal proportions, 
 for example if  a benchmark was executed on 8 nodes, 2 nodes from each type were used. Since the constructors 
 of CPUs do not specify the dynamic and the static power of their CPUs, for each type of node they were 
 The heterogeneous platform had up to 144 nodes and had nodes from the four types in equal proportions, 
 for example if  a benchmark was executed on 8 nodes, 2 nodes from each type were used. Since the constructors 
 of CPUs do not specify the dynamic and the static power of their CPUs, for each type of node they were 

-chosen proportionally to  its computing power (FLOPS).  In the initial heterogeneous platform,  while computing 
+chosen proportionally to its computing power (FLOPS).  In the initial heterogeneous platform,  while computing 

 with highest frequency, each node  consumed power proportional to its computing power which 80\% of it was 
 with highest frequency, each node  consumed power proportional to its computing power which 80\% of it was 

-dynamic power and the rest was 20\% for the static power, the same assumption  was made in \cite{45,3}. 
+dynamic power and the rest was 20\% for the static power, the same assumption  was made in \cite{Our_first_paper,Rauber_Analytical.Modeling.for.Energy}. 

 Finally, These nodes were connected via an ethernet network with 1 Gbit/s bandwidth.
 
 
 Finally, These nodes were connected via an ethernet network with 1 Gbit/s bandwidth.
 
 


@@ -876,8 +911,8 @@ Plots (\ref{fig:energy} and \ref{fig:per_deg}) present the energy saving and per
 respectively for all the benchmarks according to the number of used nodes. As shown in the first plot, 
 the energy saving percentages of the benchmarks MG, LU, BT and FT are decreased linearly  when the the 
 number of nodes is increased. While for the  EP and SP benchmarks, the energy saving percentage is not 
 respectively for all the benchmarks according to the number of used nodes. As shown in the first plot, 
 the energy saving percentages of the benchmarks MG, LU, BT and FT are decreased linearly  when the the 
 number of nodes is increased. While for the  EP and SP benchmarks, the energy saving percentage is not 

-affected by the increase of the number of computing nodes, because in these benchmarks there are no 
-communications. Finally, the energy saving of the GC benchmark  is significantly decreased when the number 
+affected by the increase of the number of computing nodes, because in these benchmarks there are little or 
+no communications. Finally, the energy saving of the GC benchmark  is significantly decreased when the number 

 of nodes is increased because  this benchmark has more communications than the others. The second plot 
 shows that the performance degradation percentages of most of the benchmarks are decreased when they 
 run on a big number of nodes because they spend more time communicating than computing, thus, scaling 
 of nodes is increased because  this benchmark has more communications than the others. The second plot 
 shows that the performance degradation percentages of most of the benchmarks are decreased when they 
 run on a big number of nodes because they spend more time communicating than computing, thus, scaling