correcting power mesurement fig

[mpi-energy2.git] / Heter_paper.tex

diff --git a/Heter_paper.tex b/Heter_paper.tex
index a697d6e88da68d2e009844b1fdf7fe790bb6e307..2f78db5cca9768e9b5adce2a1079de9039e3f041 100644 (file)
--- a/Heter_paper.tex
+++ b/Heter_paper.tex
@@ -53,23 +53,27 @@
 \newcommand{\Sopt}[1][]{\Xsub{S}{opt}_{#1}}
 \newcommand{\Tcm}[1][]{\Xsub{T}{cm}_{\fxheight{#1}}}
 \newcommand{\Tcp}[1][]{\Xsub{T}{cp}_{#1}}
 \newcommand{\Sopt}[1][]{\Xsub{S}{opt}_{#1}}
 \newcommand{\Tcm}[1][]{\Xsub{T}{cm}_{\fxheight{#1}}}
 \newcommand{\Tcp}[1][]{\Xsub{T}{cp}_{#1}}

+\newcommand{\Ppeak}[1][]{\Xsub{P}{peak}_{#1}}
+\newcommand{\Pidle}[1][]{\Xsub{P}{idle}_{\fxheight{#1}}}

 \newcommand{\TcpOld}[1][]{\Xsub{T}{cpOld}_{#1}}
 \newcommand{\Tnew}{\Xsub{T}{New}}
 \newcommand{\TcpOld}[1][]{\Xsub{T}{cpOld}_{#1}}
 \newcommand{\Tnew}{\Xsub{T}{New}}

-\newcommand{\Told}{\Xsub{T}{Old}} 
+\newcommand{\Told}{\Xsub{T}{Old}}

 
 

-\begin{document} 
+\begin{document}

 
 

-\title{Energy Consumption Reduction for Message Passing Iterative  Applications in Heterogeneous Architecture Using DVFS}
- 
-\author{% 
+\title{Energy Consumption Reduction with DVFS for \\
+  Message Passing Iterative Applications on \\
+  Heterogeneous Architectures}
+
+\author{%

   \IEEEauthorblockN{%
     Jean-Claude Charr,
     Raphaël Couturier,
     Ahmed Fanfakh and
     Arnaud Giersch
   \IEEEauthorblockN{%
     Jean-Claude Charr,
     Raphaël Couturier,
     Ahmed Fanfakh and
     Arnaud Giersch

-  } 
+  }

   \IEEEauthorblockA{%
   \IEEEauthorblockA{%

-    FEMTO-ST Institute, University of Franche-Comte\\
+    FEMTO-ST Institute, University of Franche-Comté\\

     IUT de Belfort-Montbéliard,
     19 avenue du Maréchal Juin, BP 527, 90016 Belfort cedex, France\\
     % Telephone: \mbox{+33 3 84 58 77 86}, % Raphaël
     IUT de Belfort-Montbéliard,
     19 avenue du Maréchal Juin, BP 527, 90016 Belfort cedex, France\\
     % Telephone: \mbox{+33 3 84 58 77 86}, % Raphaël


@@ -81,263 +85,113 @@
 \maketitle
 
 \begin{abstract}
 \maketitle
 
 \begin{abstract}

-Computing platforms  are consuming  more and more  energy due to  the increasing
-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 trade-off  between the energy  consumption and the
-performance of an application must be selected.
-
-In this paper, a new online frequency selecting algorithm for heterogeneous
-platforms is presented.  It selects the frequencies and tries to give the best
-trade-off 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 is evaluated on the SimGrid simulator while running the NAS
-parallel benchmarks.  The experiments show that it reduces the energy
-consumption by up to \np[\%]{35} while limiting the performance degradation as
-much as possible.  Finally, the algorithm is compared to an existing method, the
-comparison results showing that it outperforms the latter.
+  

 
 \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 platforms 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  million 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 million  each year.  The
-computing platforms must  be more energy efficient and  offer the highest number
-of FLOPS  per watt  possible, such as  the L-CSC  from the GSI  Helmholtz Center
-which became the top of the Green500 list in November 2014 \cite{Green500_List}.
-This heterogeneous platform executes more than 5 GFLOPS per watt while consuming
-57.15 kilowatts.
-
-Besides platform  improvements, there are many software  and hardware techniques
-to lower  the energy consumption of  these platforms, such  as scheduling, DVFS,
-\dots{}  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 reduces
-the number of FLOPS executed by the processor which might increase the execution
-time of the application running over that processor.  Therefore, researchers use
-different optimization  strategies to select  the frequency that gives  the best
-trade-off  between the  energy reduction  and performance  degradation  ratio. In
-\cite{Our_first_paper}, a  frequency selecting algorithm was  proposed to reduce
-the energy  consumption of message  passing iterative applications  running over
-homogeneous platforms.  The results of  the experiments show  significant energy
-consumption  reductions. In  this  paper, a  new  frequency selecting  algorithm
-adapted  for heterogeneous  platform  is  presented. It  selects  the vector  of
-frequencies, for  a heterogeneous platform  running a message  passing iterative
-application, that simultaneously tries to offer the maximum energy reduction and
-minimum performance degradation ratio. The  algorithm has a very small overhead,
-works online and does not need any training or profiling.
-
-This paper is organized as follows: Section~\ref{sec.relwork} presents some
-related works from other authors.  Section~\ref{sec.exe} describes how the
-execution time of message passing programs can be predicted.  It also presents an energy
-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. 
-Section~\ref{sec.expe} presents the results of applying the algorithm on  the NAS parallel benchmarks and executing them 
-on a heterogeneous platform. It shows the results of running three 
-different power scenarios and comparing them. Moreover, it also shows the comparison results
-between the proposed method and an existing method.
-Finally, in Section~\ref{sec.concl} the paper ends with a summary and some future works.
+
+

 
 \section{Related works}
 \label{sec.relwork}
 
 \section{Related works}
 \label{sec.relwork}

-DVFS is a technique used in modern processors to scale down both the voltage and
-the frequency of the CPU while computing, in order to reduce the energy
-consumption of the processor. DVFS is also allowed in GPUs to achieve the same
-goal. Reducing the frequency of a processor lowers its number of FLOPS and might
-degrade the performance of the application running on that processor, especially
-if it is compute bound. Therefore selecting the appropriate frequency for a
-processor to satisfy some objectives while taking into account all the
-constraints, is not a trivial operation.  Many researchers used different
-strategies to tackle this problem. Some of them developed online methods that
-compute the new frequency while executing the application, such
-as~\cite{Hao_Learning.based.DVFS,Spiliopoulos_Green.governors.Adaptive.DVFS}.
-Others used offline methods that might need to run the application and profile
-it before selecting the new frequency, such
-as~\cite{Rountree_Bounding.energy.consumption.in.MPI,Cochran_Pack_and_Cap_Adaptive_DVFS}.
-The methods could be heuristics, exact or brute force methods that satisfy
-varied objectives such as energy reduction or performance. They also could be
-adapted to the execution's environment and the type of the application such as
-sequential, parallel or distributed architecture, homogeneous or heterogeneous
-platform, synchronous or asynchronous application, \dots{}
-
-In this paper, we are interested in reducing energy for message passing iterative synchronous applications running over heterogeneous platforms.
-Some works have already been done for such platforms and they can be classified into two types of heterogeneous platforms: 
-\begin{itemize}
-
-\item the platform is composed of homogeneous GPUs and homogeneous CPUs.
-\item the platform is only composed of heterogeneous CPUs.
-
-\end{itemize}
-
-For the first type of platform, the computing intensive parallel tasks are
-executed on the GPUs and the rest are executed on the CPUs.  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 was to maximize the energy efficiency of the platform during computation by
-maximizing the number of FLOPS per watt generated.
-In~\cite{KaiMa_Holistic.Approach.to.Energy.Efficiency.in.GPU-CPU}, Kai Ma et
-al. developed a scheduling algorithm that distributes workloads proportional to
-the computing power of the nodes which could be a GPU or a CPU. All the tasks
-must be completed at the same time.  In~\cite{Rong_Effects.of.DVFS.on.K20.GPU},
-Rong et al. showed that a heterogeneous (GPUs and CPUs) cluster that enables
-DVFS gave better energy and performance efficiency than other clusters only
-composed of CPUs.
- 
-The work presented in this paper concerns the second type of platform, with
-heterogeneous CPUs.  Many methods were conceived to reduce the energy
-consumption of this type of platform.  Naveen et
-al.~\cite{Naveen_Power.Efficient.Resource.Scaling} developed a method that
-minimizes the value of $\mathit{energy}\times \mathit{delay}^2$ (the delay is
-the sum of slack times that happen during synchronous communications) by
-dynamically assigning new frequencies to the CPUs of the heterogeneous cluster.
-Lizhe et al.~\cite{Lizhe_Energy.aware.parallel.task.scheduling} proposed an
-algorithm that divides the executed tasks into two types: the critical and non
-critical tasks. The algorithm scales down the frequency of non critical tasks
-proportionally to their slack and communication times while limiting the
-performance degradation percentage to less than \np[\%]{10}.
-In~\cite{Joshi_Blackbox.prediction.of.impact.of.DVFS}, they developed a
-heterogeneous cluster composed of two types of Intel and AMD processors. They
-use a gradient method to predict the impact of DVFS operations on performance.
-In~\cite{Shelepov_Scheduling.on.Heterogeneous.Multicore} and
-\cite{Li_Minimizing.Energy.Consumption.for.Frame.Based.Tasks}, the best
-frequencies for a specified heterogeneous cluster are selected offline using
-some heuristic.  Chen et
-al.~\cite{Chen_DVFS.under.quality.of.service.requirements} used a greedy dynamic
-programming approach to minimize the power consumption of heterogeneous servers
-while respecting given time constraints.  This approach had considerable
-overhead.  In contrast to the above described papers, this paper presents the
-following contributions :
-\begin{enumerate}
-\item two new energy and performance models for message passing iterative
-  synchronous applications running over a heterogeneous platform. Both models
-  take into account communication and slack times. The models can predict the
-  required energy and the execution time of the application.
-       
-\item a new online frequency selecting algorithm for heterogeneous
-  platforms. The algorithm has a very small overhead and does not need 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}
 
 
 \section{The performance and energy consumption measurements on heterogeneous architecture}
 \label{sec.exe}
 

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

-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
-have the same network bandwidth and latency.
-
-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
-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 has the highest computation time and no slack time.
-  
- \begin{figure}[!t]
+heterogeneous grid platforms. A heterogeneous grid platform could be defined as a collection of
+heterogeneous computing clusters interconnected via a long distance network which has lower bandwidth 
+and higher latency than the local networks of the clusters. Each computing cluster in the grid is composed of homogeneous nodes that are connected together via high speed network. Therefore, each cluster has different characteristics such as computing power (FLOPS), energy consumption, CPU's frequency range, network bandwidth and latency.
+
+\begin{figure}[!t]

   \centering
   \centering

-   \includegraphics[scale=0.6]{fig/commtasks}
+  \includegraphics[scale=0.6]{fig/commtasks}

   \caption{Parallel tasks on a heterogeneous platform}
   \label{fig:heter}
 \end{figure}
 
   \caption{Parallel tasks on a heterogeneous platform}
   \label{fig:heter}
 \end{figure}
 

-Dynamic Voltage and Frequency Scaling (DVFS) is a process, implemented in 
-modern processors, that reduces the energy consumption of a CPU by scaling 
-down its voltage and frequency.  Since DVFS lowers the frequency of a CPU 
-and consequently its computing power, the execution time of a program running 
-over that scaled down processor might increase, especially if the program is 
-compute bound.  The frequency reduction process can be  expressed by the scaling 
-factor S which is the ratio between  the maximum and the new frequency of a CPU 
+The overall execution time of a distributed iterative synchronous application 
+over a heterogeneous grid 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 clusters, slack times may occur
+when fast nodes have to wait, during synchronous communications, for the slower
+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 has the highest computation time and no slack time.
+
+Dynamic Voltage and Frequency Scaling (DVFS) is a process, implemented in
+modern processors, that reduces the energy consumption of a CPU by scaling
+down its voltage and frequency.  Since DVFS lowers the frequency of a CPU
+and consequently its computing power, the execution time of a program running
+over that scaled down processor may increase, especially if the program is
+compute bound.  The frequency reduction process can be  expressed by the scaling
+factor S which is the ratio between  the maximum and the new frequency of a CPU

 as in (\ref{eq:s}).
 \begin{equation}
   \label{eq:s}
 as in (\ref{eq:s}).
 \begin{equation}
   \label{eq:s}

- S = \frac{\Fmax}{\Fnew}
+  S = \frac{\Fmax}{\Fnew}

 \end{equation}
 \end{equation}

- The execution time of a compute bound sequential program is linearly proportional 
- to the frequency scaling factor $S$.  On the other hand,  message passing 
- distributed applications consist of two parts: computation and communication. 
- 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{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 
- until the message is synchronously sent or received.
-
-Since in a heterogeneous platform each node has different characteristics,
-especially different frequency gears, when applying DVFS operations on these
-nodes, they may get different scaling factors represented by a scaling vector:
-$(S_1, S_2,\dots, S_N)$ where $S_i$ is the scaling factor of processor $i$. To
+The execution time of a compute bound sequential program is linearly
+proportional to the frequency scaling factor $S$.  On the other hand, message
+passing distributed applications consist of two parts: computation and
+communication.  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{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 until the message is
+synchronously sent or received.
+
+Since in a heterogeneous grid each cluster has different characteristics,
+especially different frequency gears, when applying DVFS operations on the nodes 
+of these clusters, they may get different scaling factors represented by a scaling vector:
+$(S_{11}, S_{12},\dots, S_{NM})$ where $S_{ij}$ is the scaling factor of processor $j$ in cluster $i$ . To

 be able to predict the execution time of message passing synchronous iterative
 be able to predict the execution time of message passing synchronous iterative

-applications running over a heterogeneous platform, for different vectors of
+applications running over a heterogeneous grid, for different vectors of

 scaling factors, the communication time and the computation time for all the
 tasks must be measured during the first iteration before applying any DVFS
 operation. Then the execution time for one iteration of the application with any
 vector of scaling factors can be predicted using (\ref{eq:perf}).
 \begin{equation}
   \label{eq:perf}
 scaling factors, the communication time and the computation time for all the
 tasks must be measured during the first iteration before applying any DVFS
 operation. Then the execution time for one iteration of the application with any
 vector of scaling factors can be predicted using (\ref{eq:perf}).
 \begin{equation}
   \label{eq:perf}

-  \Tnew = \max_{i=1,2,\dots,N} ({\TcpOld[i]} \cdot S_{i}) +  \MinTcm
-\end{equation}
-Where:
-\begin{equation}
-\label{eq:perf2}
- \MinTcm = \min_{i=1,2,\dots,N} (\Tcm[i])
+  \Tnew = \mathop{\max_{i=1,\dots N}}_{j=1,\dots,M}({\TcpOld[ij]} \cdot S_{ij}) 
+  +\mathop{\min_{j=1,\dots,M}}  (\Tcm[hj])

 \end{equation}
 \end{equation}

-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 slowest
-node. It means only the communication  time without any slack time is taken into
-account.  Therefore, the execution time of the iterative application is equal to
-the  execution time of  one iteration  as in  (\ref{eq:perf}) multiplied  by the
+
+where $N$ is the number of  clusters in the grid, $M$ is the number of  nodes in
+each cluster, $\TcpOld[ij]$ is the computation time of processor $j$ in the cluster $i$ 
+and $\Tcm[hj]$ is the communication time of processor $j$ in the cluster $h$ during the 
+first  iteration. The model computes the maximum computation time with scaling factor 
+from each node added to the communication time of the slowest node in the slowest cluster $h$.
+It means only the communication time without any slack time is taken into account.  
+Therefore, the execution time of the iterative application is equal to
+the execution time of one iteration as in (\ref{eq:perf}) multiplied by the

 number of iterations of that application.
 
 number of iterations of that application.
 

-This prediction model is developed from  the model to predict 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  to optimize both the energy consumption and the performance of
-iterative methods, which is presented in the following sections.
+This prediction model is developed from the model to predict the execution time
+of message passing distributed applications for homogeneous and heterogeneous clusters
+~\cite{Our_first_paper,pdsec2015}.  The execution time prediction model is
+used in the method to optimize both the energy consumption and the performance
+of iterative methods, which is presented in the following sections.

 
 
 \subsection{Energy model for heterogeneous platform}
 
 
 \subsection{Energy model for heterogeneous platform}

+

 Many researchers~\cite{Malkowski_energy.efficient.high.performance.computing,
 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
-computation times.  The dynamic power $\Pd$ is related to the switching
-activity $\alpha$, load capacitance $\CL$, the supply voltage $V$ and
-operational frequency $F$, as shown in (\ref{eq:pd}).
+  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 computation times.  The dynamic power $\Pd$ is related to
+the switching activity $\alpha$, load capacitance $\CL$, the supply voltage $V$
+and operational frequency $F$, as shown in (\ref{eq:pd}).

 \begin{equation}
   \label{eq:pd}
   \Pd = \alpha \cdot \CL \cdot V^2 \cdot F
 \begin{equation}
   \label{eq:pd}
   \Pd = \alpha \cdot \CL \cdot V^2 \cdot F


@@ -359,163 +213,174 @@ where $T$ is the execution time of the program, $\Tcp$ is the computation
 time and $\Tcp \le T$.  $\Tcp$ may be equal to $T$ if there is no
 communication and no slack time.
 
 time and $\Tcp \le T$.  $\Tcp$ may be equal to $T$ if there is no
 communication and no slack time.
 

-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
-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
-ratio between the maximum and the new frequency as in (\ref{eq:s}).
-The CPU governors are power schemes supplied by the operating
-system's kernel to lower a core's frequency. The new frequency 
-$\Fnew$ from (\ref{eq:s}) can be calculated as follows:
+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 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 ratio between
+the maximum and the new frequency as in (\ref{eq:s}).  The CPU governors are
+power schemes supplied by the operating system's kernel to lower a core's
+frequency. The new frequency $\Fnew$ from (\ref{eq:s}) can be calculated as
+follows:

 \begin{equation}
   \label{eq:fnew}
    \Fnew = S^{-1} \cdot \Fmax
 \end{equation}
 \begin{equation}
   \label{eq:fnew}
    \Fnew = S^{-1} \cdot \Fmax
 \end{equation}

-Replacing $\Fnew$ in (\ref{eq:pd}) as in (\ref{eq:fnew}) gives the following 
+Replacing $\Fnew$ in (\ref{eq:pd}) as in (\ref{eq:fnew}) gives the following

 equation for dynamic power consumption:
 \begin{multline}
   \label{eq:pdnew}
    \PdNew = \alpha \cdot \CL \cdot V^2 \cdot \Fnew = \alpha \cdot \CL \cdot \beta^2 \cdot \Fnew^3 \\
    {} = \alpha \cdot \CL \cdot V^2 \cdot \Fmax \cdot S^{-3} = \PdOld \cdot S^{-3}
 \end{multline}
 equation for dynamic power consumption:
 \begin{multline}
   \label{eq:pdnew}
    \PdNew = \alpha \cdot \CL \cdot V^2 \cdot \Fnew = \alpha \cdot \CL \cdot \beta^2 \cdot \Fnew^3 \\
    {} = \alpha \cdot \CL \cdot V^2 \cdot \Fmax \cdot S^{-3} = \PdOld \cdot S^{-3}
 \end{multline}

-where $\PdNew$  and $\PdOld$ are the  dynamic power consumed with the 
+where $\PdNew$  and $\PdOld$ are the  dynamic power consumed with the

 new frequency and the maximum frequency respectively.
 
 new frequency and the maximum frequency respectively.
 

-According to (\ref{eq:pdnew}) the dynamic power is reduced by a factor of $S^{-3}$ when 
-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:
+According to (\ref{eq:pdnew}) the dynamic power is reduced by a factor of
+$S^{-3}$ when 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:

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

-   \EdNew = \PdOld \cdot S^{-3} \cdot (\Tcp \cdot S)= S^{-2}\cdot \PdOld \cdot  \Tcp 
+   \EdNew = \PdOld \cdot S^{-3} \cdot (\Tcp \cdot S)= S^{-2}\cdot \PdOld \cdot  \Tcp

 \end{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}, 
- 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 (\ref{eq:perf}), the execution time of the program 
-is the sum of the computation and the communication times. The computation time is linearly related  
-to the frequency scaling factor, while this scaling factor does not affect the communication time. 
-The static energy of a processor after scaling its frequency is computed as follows: 
+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},
+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 (\ref{eq:perf}), the execution time of the
+program is the sum of the computation and the communication times. The
+computation time is linearly related to the frequency scaling factor, while this
+scaling factor does not affect the communication time.  The static energy of a
+processor after scaling its frequency is computed as follows:

 \begin{equation}
   \label{eq:Estatic}
   \Es = \Ps \cdot (\Tcp \cdot S  + \Tcm)
 \end{equation}
 
 \begin{equation}
   \label{eq:Estatic}
   \Es = \Ps \cdot (\Tcp \cdot S  + \Tcm)
 \end{equation}
 

-In  the  considered  heterogeneous  platform,  each  processor  $i$  might  have
-different   dynamic  and  static   powers,  noted   as  $\Pd[i]$   and  $\Ps[i]$
-respectively.  Therefore,  even if  the  distributed  message passing  iterative
-application  is  load balanced,  the  computation time  of  each  CPU $i$  noted
-$\Tcp[i]$ might  be different and  different frequency scaling factors  might be
-computed in order to decrease  the overall energy consumption of the application
-and reduce slack  times.  The communication time of a processor  $i$ is noted as
-$\Tcm[i]$  and could  contain slack  times when  communicating  with slower
-nodes,  see Figure~\ref{fig:heter}.  Therefore,  all nodes  do  not have  equal
-communication  times. While  the dynamic  energy  is computed  according to  the
-frequency  scaling   factor  and   the  dynamic  power   of  each  node   as  in
-(\ref{eq:Edyn}), the static energy is computed  as the sum of the execution time
-of  one iteration multiplied  by the static  power of  each processor.   The overall
-energy consumption of a message  passing distributed application executed over a
-heterogeneous platform during one iteration  is the summation of all dynamic and
-static energies for each processor.  It is computed as follows:
+In the considered heterogeneous grid platform, each node $j$ in cluster $i$ may have
+different dynamic and static powers from the nodes of the other clusters, 
+noted as $\Pd[ij]$ and $\Ps[ij]$ respectively.  Therefore, even if the distributed 
+message passing iterative application is load balanced, the computation time of each CPU $j$ 
+in cluster $i$ noted $\Tcp[ij]$ may be different and different frequency scaling factors may be
+computed in order to decrease the overall energy consumption of the application
+and reduce slack times.  The communication time of a processor $j$ in cluster $i$ is noted as
+$\Tcm[ij]$ and could contain slack times when communicating with slower nodes,
+see Figure~\ref{fig:heter}.  Therefore, all nodes do not have equal
+communication times. While the dynamic energy is computed according to the
+frequency scaling factor and the dynamic power of each node as in
+(\ref{eq:Edyn}), the static energy is computed as the sum of the execution time
+of one iteration multiplied by the static power of each processor.  The overall
+energy consumption of a message passing distributed application executed over a
+heterogeneous grid platform during one iteration is the summation of all dynamic and
+static energies for $M$ processors in $N$ clusters.  It is computed as follows:

 \begin{multline}
   \label{eq:energy}
 \begin{multline}
   \label{eq:energy}

- E = \sum_{i=1}^{N} {(S_i^{-2} \cdot \Pd[i] \cdot  \Tcp[i])} + {} \\
- \sum_{i=1}^{N} (\Ps[i] \cdot (\max_{i=1,2,\dots,N} (\Tcp[i] \cdot S_{i}) +
-  {\MinTcm))}
- \end{multline}
-
-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}. The overall energy consumption for the iterative 
-application can be measured by measuring  the energy consumption for one iteration as in (\ref{eq:energy}) 
-multiplied by the number of iterations of that application.
+ E = \sum_{i=1}^{N} \sum_{i=1}^{M} {(S_{ij}^{-2} \cdot \Pd[ij] \cdot  \Tcp[ij])} +  
+ \sum_{i=1}^{N} \sum_{j=1}^{M} (\Ps[ij] \cdot {} \\
+  (\mathop{\max_{i=1,\dots N}}_{j=1,\dots,M}({\Tcp[ij]} \cdot S_{ij}) 
+  +\mathop{\min_{j=1,\dots M}} (\Tcm[hj]) ))
+\end{multline}

 
 

+Reducing the frequencies of the processors according to the vector of scaling
+factors $(S_{11}, S_{12},\dots, S_{NM})$ 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}. The overall energy consumption
+for the iterative application can be measured by measuring the energy
+consumption for one iteration as in (\ref{eq:energy}) multiplied by the number
+of iterations of that application.

 
 \section{Optimization of both energy consumption and performance}
 \label{sec.compet}
 
 Using the lowest frequency for each processor does not necessarily give the most
 
 \section{Optimization of both energy consumption and performance}
 \label{sec.compet}
 
 Using the lowest frequency for each processor does not necessarily give the most

-energy efficient  execution of an  application. Indeed, even though  the dynamic
-power  is  reduced  while  scaling  down  the  frequency  of  a  processor,  its
-computation power  is proportionally decreased. Hence, the  execution time might
-be drastically  increased and  during that time,  dynamic and static  powers are
-being consumed.  Therefore,  it might cancel any gains  achieved by scaling down
-the frequency of all nodes to  the minimum and the overall energy consumption of
-the application might not  be the optimal one.  It is not  trivial to select the
-appropriate 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). The  aim being  to reduce  the overall energy  consumption and  to avoid
-increasing    significantly    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 heterogeneous  clusters  as  described  above.  Due  to  the
-heterogeneity of the processors, a vector of scaling factors should
-be selected and  it must give the best trade-off  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 between  the energy and the
-frequency   scaling    factors   is   nonlinear,   for    more   details   refer
-to~\cite{Freeh_Exploring.the.Energy.Time.Tradeoff}.   Moreover,  these relations
-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
+energy efficient execution of an application. Indeed, even though the dynamic
+power is reduced while scaling down the frequency of a processor, its
+computation power is proportionally decreased. Hence, the execution time might
+be drastically increased and during that time, dynamic and static powers are
+being consumed.  Therefore, it might cancel any gains achieved by scaling down
+the frequency of all nodes to the minimum and the overall energy consumption of
+the application might not be the optimal one.  It is not trivial to select the
+appropriate 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). The aim being to reduce the overall energy consumption and to avoid
+increasing significantly the execution time.  In our previous
+work~\cite{Our_first_paper,pdsec2015}, we proposed a method that selects the optimal
+frequency scaling factor for a homogeneous and heterogeneous clusters 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 heterogeneous grid as described above.  Due to the
+heterogeneity of the processors, a vector of scaling factors should be selected
+and it must give the best trade-off 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 between the energy and the
+frequency scaling factors is nonlinear, for more details refer
+to~\cite{Freeh_Exploring.the.Energy.Time.Tradeoff}.  Moreover, these relations
+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:
 maximum frequency for all nodes) as follows:

-\begin{multline}
+\begin{equation}

   \label{eq:pnorm}
   \label{eq:pnorm}

-  \Pnorm = \frac{\Tnew}{\Told}\\
-       {} = \frac{ \max_{i=1,2,\dots,N} (\Tcp[i] \cdot S_{i}) +\MinTcm}
-           {\max_{i=1,2,\dots,N}{(\Tcp[i]+\Tcm[i])}}
-\end{multline}
+  \Pnorm = \frac{\Tnew}{\Told}                 
+\end{equation}

 
 
 
 

-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}
+Where $Tnew$ is computed as in (\ref{eq:perf}) and $Told$ is computed as in (\ref{eq:told})
+\begin{equation}
+  \label{eq:told}
+   \Told = \mathop{\max_{i=1,2,\dots,N}}_{j=1,2,\dots,M} (\Tcp[ij]+\Tcm[ij])             
+\end{equation}
+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{equation}

   \label{eq:enorm}
   \label{eq:enorm}

-  \Enorm = \frac{\Ereduced}{\Eoriginal} \\
-  {} = \frac{ \sum_{i=1}^{N}{(S_i^{-2} \cdot \Pd[i] \cdot  \Tcp[i])} +
- \sum_{i=1}^{N} {(\Ps[i] \cdot \Tnew)}}{\sum_{i=1}^{N}{( \Pd[i] \cdot  \Tcp[i])} +
- \sum_{i=1}^{N} {(\Ps[i] \cdot \Told)}}
-\end{multline} 
-Where $\Ereduced$ and $\Eoriginal$ are computed using (\ref{eq:energy}) and
-  $\Tnew$ and $\Told$ are computed as in (\ref{eq:pnorm}).
-
-While the main 
-goal is to optimize the energy and execution time at the same time, the normalized 
-energy and execution time curves are not in the same direction. According 
-to the equations~(\ref{eq:pnorm}) and (\ref{eq:enorm}), the vector  of frequency
-scaling factors $S_1,S_2,\dots,S_N$ reduce both the energy and the execution
-time simultaneously.  But the main objective is to produce maximum energy
-reduction with minimum execution time reduction.  
-  
-This problem can be solved by making the optimization process for energy and 
-execution time following 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}
-  \Pnorm = \frac{\Told}{\Tnew}\\
-          = \frac{\max_{i=1,2,\dots,N}{(\Tcp[i]+\Tcm[i])}}
-            { \max_{i=1,2,\dots,N} (\Tcp[i] \cdot S_{i}) + \MinTcm} 
-\end{multline}
+  \Enorm = \frac{\Ereduced}{\Eoriginal} 
+\end{equation}

 
 

+Where $\Ereduced$  is computed using (\ref{eq:energy}) and $\Eoriginal$ is 
+computed as in ().
+
+\textcolor{red}{A reference is missing}
+\begin{equation}
+  \label{eq:eorginal}
+    \Eoriginal = \sum_{i=1}^{N} \sum_{j=1}^{M} ( \Pd[ij] \cdot  \Tcp[ij])  + 
+     \mathop{\sum_{i=1}^{N}} \sum_{j=1}^{M} (\Ps[ij] \cdot \Told)       
+\end{equation}
+
+While the main goal is to optimize the energy and execution time at the same
+time, the normalized energy and execution time curves do not evolve (increase/decrease) in the same way. 
+According to the equations~(\ref{eq:pnorm}) and (\ref{eq:enorm}), the
+vector of frequency scaling factors $S_1,S_2,\dots,S_N$ reduce both the energy
+and the execution time simultaneously.  But the main objective is to produce
+maximum energy reduction with minimum execution time reduction.
+
+This problem can be solved by making the optimization process for energy and
+execution time follow the same evolution according to the vector of scaling factors
+$(S_{11}, S_{12},\dots, S_{NM})$. Therefore, the equation of the
+normalized execution time is inverted which gives the normalized performance
+equation, as follows:
+\begin{equation}
+  \label{eq:pnorm_inv}
+  \Pnorm = \frac{\Told}{\Tnew}          
+\end{equation}

 
 \begin{figure}[!t]
   \centering
 
 \begin{figure}[!t]
   \centering

-  \subfloat[Homogeneous platform]{%
+  \subfloat[Homogeneous cluster]{%

     \includegraphics[width=.33\textwidth]{fig/homo}\label{fig:r1}}%
     \includegraphics[width=.33\textwidth]{fig/homo}\label{fig:r1}}%

-  
-  
-  \subfloat[Heterogeneous platform]{%
+
+  \subfloat[Heterogeneous grid]{%

     \includegraphics[width=.33\textwidth]{fig/heter}\label{fig:r2}}
   \label{fig:rel}
   \caption{The energy and performance relation}
     \includegraphics[width=.33\textwidth]{fig/heter}\label{fig:r2}}
   \label{fig:rel}
   \caption{The energy and performance relation}


@@ -529,25 +394,96 @@ 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}
 Figure~\ref{fig:r2}. Then the objective function has the following form:
 \begin{equation}
   \label{eq:max}

-  \MaxDist = 
-  \mathop{\max_{i=1,\dots F}}_{j=1,\dots,N}
-      (\overbrace{\Pnorm(S_{ij})}^{\text{Maximize}} -
-       \overbrace{\Enorm(S_{ij})}^{\text{Minimize}} )
+  \MaxDist =
+\mathop{  \mathop{\max_{i=1,\dots N}}_{j=1,\dots,M}}_{k=1,\dots,F}
+      (\overbrace{\Pnorm(S_{ijk})}^{\text{Maximize}} -
+       \overbrace{\Enorm(S_{ijk})}^{\text{Minimize}} )

 \end{equation}
 \end{equation}

-where $N$ is the number of nodes and $F$ is the  number of available frequencies for each node. 
-Then, the optimal set of scaling factors that satisfies (\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 important 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 }
+where $N$ is the number of clusters, $M$ is the number of nodes in each cluster and
+$F$ is the number of available frequencies for each node.  Then, the optimal set 
+of scaling factors that satisfies (\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 important 
+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  grids }

 \label{sec.optim}
 
 \label{sec.optim}
 

+\begin{algorithm}
+  \begin{algorithmic}[1]
+    % \footnotesize
+    \Require ~
+    \begin{description}
+    \item [{$N$}] number of clusters in the grid.
+    \item [{$M$}] number of nodes in each cluster.
+    \item[{$\Tcp[ij]$}] array of all computation times for all nodes during one iteration and with the highest frequency.
+    \item[{$\Tcm[ij]$}] array of all communication times for all nodes during one iteration and with the highest frequency.
+    \item[{$\Fmax[ij]$}] array of the maximum frequencies for all nodes.
+    \item[{$\Pd[ij]$}] array of the dynamic powers for all nodes.
+    \item[{$\Ps[ij]$}] array of the static powers for all nodes.
+    \item[{$\Fdiff[ij]$}] array of the differences between two successive frequencies for all nodes.
+    \end{description}
+    \Ensure $\Sopt[11],\Sopt[12] \dots, \Sopt[NM_i]$,  a vector of scaling factors that gives the optimal tradeoff between energy consumption and execution time
+
+    \State $\Scp[ij] \gets \frac{\max_{i=1,2,\dots,N}(\max_{j=1,2,\dots,M_i}(\Tcp[ij]))}{\Tcp[ij]} $
+    \State $F_{ij} \gets  \frac{\Fmax[ij]}{\Scp[i]},~{i=1,2,\cdots,N},~{j=1,2,\dots,M_i}.$
+    \State Round the computed initial frequencies $F_i$ to the closest  available frequency for each node.
+    \If{(not the first frequency)}
+          \State $F_{ij} \gets F_{ij}+\Fdiff[ij],~i=1,\dots,N,~{j=1,\dots,M_i}.$
+    \EndIf
+    \State $\Told \gets $ computed as in equations (\ref{eq:told}).
+    \State $\Eoriginal \gets $ computed as in equations (\ref{eq:eorginal}) .
+    \State $\Sopt[ij] \gets 1,~i=1,\dots,N,~{j=1,\dots,M_i}. $
+    \State $\Dist \gets 0 $
+    \While {(all nodes have not reached their  minimum   \newline\hspace*{2.5em} frequency \textbf{or}  $\Pnorm - \Enorm < 0 $)}
+        \If{(not the last freq. \textbf{and} not the slowest node)}
+        \State $F_{ij} \gets F_{ij} - \Fdiff[ij],~{i=1,\dots,N},~{j=1,\dots,M_i}$.
+        \State $S_{ij} \gets \frac{\Fmax[ij]}{F_{ij}},~{i=1,\dots,N},~{j=1,\dots,M_i}.$
+        \EndIf
+       \State $\Tnew \gets $ computed as  in equations (\ref{eq:perf}). 
+       \State $\Ereduced \gets $ computed as  in equations (\ref{eq:energy}). 
+       \State $\Pnorm \gets \frac{\Told}{\Tnew}$
+       \State $\Enorm\gets \frac{\Ereduced}{\Eoriginal}$
+      \If{$(\Pnorm - \Enorm > \Dist)$}
+        \State $\Sopt[ij] \gets S_{ij},~i=1,\dots,N,~j=1,\dots,M_i. $
+        \State $\Dist \gets \Pnorm - \Enorm$
+      \EndIf
+    \EndWhile
+    \State  Return $\Sopt[11],\Sopt[12],\dots,\Sopt[NM_i]$
+  \end{algorithmic}
+  \caption{Scaling factors selection algorithm}
+  \label{HSA}
+\end{algorithm}
+
+\begin{algorithm}
+  \begin{algorithmic}[1]
+    % \footnotesize
+    \For {$k=1$ to \textit{some iterations}}
+      \State Computations section.
+      \State Communications section.
+      \If {$(k=1)$}
+        \State Gather all times of computation and\newline\hspace*{3em}%
+               communication from each node.
+        \State Call Algorithm \ref{HSA}.
+        \State Compute the new frequencies from the\newline\hspace*{3em}%
+               returned optimal scaling factors.
+        \State Set the new frequencies to nodes.
+      \EndIf
+    \EndFor
+  \end{algorithmic}
+  \caption{DVFS algorithm}
+  \label{dvfs}
+\end{algorithm}
+

 \subsection{The algorithm details}
 \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
+
+\textcolor{red}{Delete the subsection if there's only one.}
+
+In this section, the scaling factors selection algorithm for  grids, algorithm~\ref{HSA}, is presented. It selects the vector of the frequency
+scaling factors  that gives the best trade-off between minimizing the

 energy consumption and maximizing the performance of a message passing
 energy consumption and maximizing the performance of a message passing

-synchronous iterative application executed on a heterogeneous platform. It works
+synchronous iterative application executed on a  grid. It works

 online during the execution time of the iterative message passing program.  It
 uses information gathered during the first iteration such as the computation
 time and the communication time in one iteration for each node. The algorithm is
 online during the execution time of the iterative message passing program.  It
 uses information gathered during the first iteration such as the computation
 time and the communication time in one iteration for each node. The algorithm is


@@ -558,13 +494,20 @@ to the computed scaling factors.  This algorithm is called just once during the
 execution of the program. Algorithm~\ref{dvfs} shows where and when the proposed
 scaling algorithm is called in the iterative MPI program.
 
 execution of the program. Algorithm~\ref{dvfs} shows where and when the proposed
 scaling algorithm is called in the iterative MPI program.
 

-The nodes in a heterogeneous platform have different computing powers, thus
+\begin{figure}[!t]
+  \centering
+  \includegraphics[scale=0.45]{fig/init_freq}
+  \caption{Selecting the initial frequencies}
+  \label{fig:st_freq}
+\end{figure}
+
+Nodes from distinct clusters in a grid have different computing powers, thus

 while executing message 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.  The algorithm takes into account this
 while executing message 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.  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
+problem and tries to reduce these slack times when selecting the vector of the frequency
+scaling factors. 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 proportional to its
 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 proportional to its


@@ -573,682 +516,192 @@ frequency scaling factors are computed as a ratio between the computation time
 of the slowest node and the computation time of the node $i$ as follows:
 \begin{equation}
   \label{eq:Scp}
 of the slowest node and the computation time of the node $i$ as follows:
 \begin{equation}
   \label{eq:Scp}

- \Scp[i] = \frac{\max_{i=1,2,\dots,N}(\Tcp[i])}{\Tcp[i]}
+  \Scp[ij] =  \frac{ \mathop{\max_{i=1,\dots N}}_{j=1,\dots,M}(\Tcp[ij])} {\Tcp[ij]}

 \end{equation}
 \end{equation}

-Using the initial  frequency scaling factors computed in (\ref{eq:Scp}), the algorithm computes 
-the initial frequencies for all nodes as a ratio between the maximum frequency of node $i$  
-and the computation scaling factor $\Scp[i]$ as follows:
+Using the initial frequency scaling factors computed in (\ref{eq:Scp}), the
+algorithm computes the initial frequencies for all nodes as a ratio between the
+maximum frequency of node $i$ and the computation scaling factor $\Scp[i]$ as
+follows:

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

- F_{i} = \frac{\Fmax[i]}{\Scp[i]},~{i=1,2,\dots,N}
+  F_{ij} = \frac{\Fmax[ij]}{\Scp[ij]},~{i=1,2,\dots,N},~{j=1,\dots,M}

 \end{equation}
 If the computed initial frequency for a node is not available in the gears of
 that node, it is replaced by the nearest available frequency.  In
 \end{equation}
 If the computed initial frequency for a node is not available in the gears of
 that node, it is replaced by the nearest available frequency.  In

-Figure~\ref{fig:st_freq}, the nodes are sorted by their computing power in
+Figure~\ref{fig:st_freq}, the nodes are sorted by their computing powers in

 ascending order and the frequencies of the faster nodes are scaled down
 according to the computed initial frequency scaling factors.  The resulting new
 frequencies are highlighted in Figure~\ref{fig:st_freq}.  This set of
 frequencies can be considered as a higher bound for the search space of the
 ascending order and the frequencies of the faster nodes are scaled down
 according to the computed initial frequency scaling factors.  The resulting new
 frequencies are highlighted in Figure~\ref{fig:st_freq}.  This set of
 frequencies can be considered as a higher bound for the search space of the

-optimal vector of frequencies because selecting frequency scaling factors higher
+optimal vector of frequencies because selecting higher frequencies

 than the higher bound will not improve the performance of the application and it
 will increase its overall energy consumption.  Therefore the algorithm that
 selects the frequency scaling factors starts the search method from these
 initial frequencies and takes a downward search direction toward lower
 than the higher bound will not improve the performance of the application and it
 will increase its overall energy consumption.  Therefore the algorithm that
 selects the frequency scaling factors starts the search method from these
 initial frequencies and takes a downward search direction toward lower

-frequencies. The algorithm iterates on all left frequencies, from the higher
-bound until all nodes reach their minimum frequencies, to compute their overall
-energy consumption and performance, and select the optimal frequency scaling
-factors vector. At each iteration the algorithm determines the slowest node
+frequencies until reaching the nodes' minimum frequencies or lower bounds. A node's frequency is considered its lower bound if the computed distance between the energy and performance at this frequency is less than zero.
+A negative distance means that the performance degradation ratio is higher than the energy saving ratio.
+In this situation, the algorithm must stop the downward search because it has reached the lower bound and it is useless to test the lower frequencies. Indeed, they will all give worse distances. 
+
+Therefore, the algorithm iterates on all remaining frequencies, from the higher
+bound until all nodes reach their minimum frequencies or their lower bounds, to compute the overall
+energy consumption and performance and selects the optimal vector of the frequency scaling
+factors. At each iteration the algorithm determines the slowest node

 according to the equation (\ref{eq:perf}) and keeps its frequency unchanged,
 while it lowers the frequency of all other nodes by one gear.  The new overall
 energy consumption and execution time are computed according to the new scaling
 factors.  The optimal set of frequency scaling factors is the set that gives the
 highest distance according to the objective function (\ref{eq:max}).
 
 according to the equation (\ref{eq:perf}) and keeps its frequency unchanged,
 while it lowers the frequency of all other nodes by one gear.  The new overall
 energy consumption and execution time are computed according to the new scaling
 factors.  The optimal set of frequency scaling factors is the set that gives the
 highest distance according to the objective function (\ref{eq:max}).
 

-Figures~\ref{fig:r1} and \ref{fig:r2}  illustrate the normalized performance and
-consumed  energy for  an application  running on  a homogeneous  platform  and a
-heterogeneous platform respectively while increasing the scaling factors. It can
-be noticed  that in a  homogeneous platform the  search for the  optimal scaling
-factor should start  from the maximum frequency because  the performance and the
-consumed energy decrease from the beginning of the plot. On the other hand,
-in the heterogeneous platform the  performance is maintained at the beginning of
-the plot  even if the  frequencies of the  faster nodes decrease  until the
-computing power of scaled down  nodes are lower than the slowest  node. In other
-words, until they reach the higher bound. It can also be noticed that the higher
-the difference between the faster nodes  and the slower nodes is, the bigger the
-maximum distance  between the  energy curve and  the performance curve  is while
- the scaling factors are varying which results in bigger energy savings.
-\begin{figure}[!t]
-  \centering
-    \includegraphics[scale=0.5]{fig/start_freq}
-  \caption{Selecting the initial frequencies}
-  \label{fig:st_freq}
-\end{figure}
+Figures~\ref{fig:r1} and \ref{fig:r2} illustrate the normalized performance and
+consumed energy for an application running on a homogeneous cluster and a
+ grid platform respectively while increasing the scaling factors. It can
+be noticed that in a homogeneous cluster the search for the optimal scaling
+factor should start from the maximum frequency because the performance and the
+consumed energy decrease from the beginning of the plot. On the other hand, in
+the  grid platform the performance is maintained at the beginning of the
+plot even if the frequencies of the faster nodes decrease until the computing
+power of scaled down nodes are lower than the slowest node. In other words,
+until they reach the higher bound. It can also be noticed that the higher the
+difference between the faster nodes and the slower nodes is, the bigger the
+maximum distance between the energy curve and the performance curve is, which results in bigger energy savings. 

 
 
 
 

+\section{Experimental results}
+\label{sec.expe}
+While in~\cite{mpi-energy2} the energy  model and the scaling factors selection algorithm were applied to a heterogeneous cluster and  evaluated over the SimGrid simulator~\cite{SimGrid.org}, 
+in this paper real experiments were conducted over the grid'5000 platform. 
+
+\subsection{Grid'5000 architature and power consumption}
+\label{sec.grid5000}
+Grid'5000~\cite{grid5000} is a large-scale testbed that consists of ten sites distributed over all metropolitan France and Luxembourg. All the sites are connected together via        a special long distance network called RENATER,
+which is the French National Telecommunication Network for Technology.
+Each site of the grid is composed of few heterogeneous 
+computing clusters and each cluster contains many homogeneous nodes. In total,
+ grid'5000 has about  one thousand heterogeneous nodes and eight thousand cores.  In each site,
+the clusters and their nodes are connected via  high speed local area networks. 
+Two types of local networks are used, Ethernet or Infiniband networks which have  different characteristics in terms of bandwidth and latency.  
+
+Since grid'5000 is dedicated for testing, contrary to production grids it allows a user to deploy its own customized operating system on all the booked nodes. The user could have root rights and thus apply DVFS operations while executing a distributed application. Moreover, the grid'5000 testbed provides at some sites a power measurement tool to capture 
+the power consumption  for each node in those sites. The measured power is the overall consumed power by by all the components of a node at a given instant, such as CPU, hard drive, main-board, memory, ...  For more details refer to
+\cite{Energy_measurement}. To just measure the CPU power of one core in a node $j$, 
+ firstly,  the power consumed by the node while being idle at instant $y$, noted as $\Pidle[jy]$, was measured. Then, the power was measured while running a single thread benchmark with no communication (no idle time) over the same node with its CPU scaled to the maximum available frequency. The latter power measured at time $x$ with maximum frequency for one core of node $j$ is noted $P\max[jx]$. The difference between the two measured power consumption represents the 
+dynamic power consumption of that core with the maximum frequency, see  figure(\ref{fig:power_cons}). 
+
+\textcolor{red}{why maximum and minimum, change peak in the equation and the figure}
+
+The dynamic power $\Pd[j]$ is computed as in equation (\ref{eq:pdyn})
+\begin{equation}
+  \label{eq:pdyn}
+    \Pd[j] = \max_{x=\beta_1,\dots \beta_2} (P\max[jx])  -  \min_{y=\Theta_1,\dots \Theta_2} (\Pidle[jy])
+\end{equation}

 
 

+where $\Pd[j]$ is the dynamic power consumption for one core of node $j$, 
+$\lbrace \beta_1,\beta_2 \rbrace$ is the time interval for the measured peak power values, 
+$\lbrace\Theta_1,\Theta_2\rbrace$ is the time interval for the measured  idle power values.
+Therefore, the dynamic power of one core is computed as the difference between the maximum 
+measured value in peak powers vector and the minimum measured value in the idle powers vector.

 
 

-\begin{algorithm}
-  \begin{algorithmic}[1]
-    % \footnotesize
-    \Require ~
-    \begin{description}
-    \item[{$\Tcp[i]$}] array of all computation times for all nodes during one iteration and with highest frequency.
-    \item[{$\Tcm[i]$}] array of all communication times for all nodes during one iteration and with highest frequency.
-    \item[{$\Fmax[i]$}] array of the maximum frequencies for all nodes.
-    \item[{$\Pd[i]$}] array of the dynamic powers for all nodes.
-    \item[{$\Ps[i]$}] array of the static powers for all nodes.
-    \item[{$\Fdiff[i]$}] array of the difference between two successive frequencies for all nodes.
-    \end{description}
-    \Ensure $\Sopt[1],\Sopt[2] \dots, \Sopt[N]$ is a vector of optimal scaling factors
+On the other hand, the static power consumption by one core is a part of the measured idle power consumption of the node. Since in grid'5000 there is no way to measure precisely the consumed static power and in~\cite{Our_first_paper,pdsec2015,Rauber_Analytical.Modeling.for.Energy} it was assumed that  the static power  represents a ratio of the dynamic power, the value of the static power is assumed as  np[\%]{20} of dynamic power consumption of the core.

 
 

-    \State $\Scp[i] \gets \frac{\max_{i=1,2,\dots,N}(\Tcp[i])}{\Tcp[i]} $
-    \State $F_{i} \gets  \frac{\Fmax[i]}{\Scp[i]},~{i=1,2,\cdots,N}$
-    \State Round the computed initial frequencies $F_i$ to the closest one available in each node.
-    \If{(not the first frequency)}
-          \State $F_i \gets F_i+\Fdiff[i],~i=1,\dots,N.$
-    \EndIf 
-    \State $\Told \gets \max_{i=1,\dots,N} (\Tcp[i]+\Tcm[i])$
-    % \State $\Eoriginal \gets \sum_{i=1}^{N}{( \Pd[i] \cdot  \Tcp[i])} +\sum_{i=1}^{N} {(\Ps[i] \cdot \Told)}$
-    \State $\Eoriginal \gets \sum_{i=1}^{N}{( \Pd[i] \cdot  \Tcp[i] + \Ps[i] \cdot \Told)}$
-    \State $\Sopt[i] \gets 1,~i=1,\dots,N. $
-    \State $\Dist \gets 0 $
-    \While {(all nodes not reach their  minimum  frequency)}
-        \If{(not the last freq. \textbf{and} not the slowest node)}
-        \State $F_i \gets F_i - \Fdiff[i],~i=1,\dots,N.$
-        \State $S_i \gets \frac{\Fmax[i]}{F_i},~i=1,\dots,N.$
-        \EndIf
-       \State $\Tnew \gets \max_{i=1,\dots,N} (\Tcp[i] \cdot S_{i}) + \MinTcm $
-%       \State $\Ereduced \gets \sum_{i=1}^{N}{(S_i^{-2} \cdot \Pd[i] \cdot  \Tcp[i])} + \sum_{i=1}^{N} {(\Ps[i] \cdot \rlap{\Tnew)}} $
-       \State $\Ereduced \gets \sum_{i=1}^{N}{(S_i^{-2} \cdot \Pd[i] \cdot  \Tcp[i] + \Ps[i] \cdot \rlap{\Tnew)}} $
-       \State $\Pnorm \gets \frac{\Told}{\Tnew}$
-       \State $\Enorm\gets \frac{\Ereduced}{\Eoriginal}$
-      \If{$(\Pnorm - \Enorm > \Dist)$}
-        \State $\Sopt[i] \gets S_{i},~i=1,\dots,N. $
-        \State $\Dist \gets \Pnorm - \Enorm$
-      \EndIf
-    \EndWhile
-    \State  Return $\Sopt[1],\Sopt[2],\dots,\Sopt[N]$
-  \end{algorithmic}
-  \caption{frequency scaling factors selection algorithm}
-  \label{HSA}
-\end{algorithm}
+In the experiments presented in the following sections, two sites of grid'5000 were used, Lyon and Nancy sites. These two sites have in total seven different clusters as in figure (\ref{fig:grid5000}).

 
 

-\begin{algorithm}
-  \begin{algorithmic}[1]
-    % \footnotesize
-    \For {$k=1$ to \textit{some iterations}}
-      \State Computations section.
-      \State Communications section.
-      \If {$(k=1)$}
-        \State Gather all times of computation and\newline\hspace*{3em}%
-               communication from each node.
-        \State Call Algorithm \ref{HSA}.
-        \State Compute the new frequencies from the\newline\hspace*{3em}%
-               returned optimal scaling factors.
-        \State Set the new frequencies to nodes.
-      \EndIf
-    \EndFor
-  \end{algorithmic}
-  \caption{DVFS algorithm}
-  \label{dvfs}
-\end{algorithm}
+Four clusters from the two sites were selected in the experiments: one cluster from 
+Lyon's site, Taurus cluster, and three clusters from Nancy's site, Graphene, 
+Griffon and Graphite. Each one of these clusters has homogeneous nodes inside, while nodes from different clusters are heterogeneous in many aspects such as: computing power, power consumption, available 
+frequency ranges and local network features: the bandwidth and the latency.  Table \ref{table:grid5000} shows 
+the details characteristics of these four clusters. Moreover, the dynamic powers were computed  using the equation (\ref{eq:pdyn}) for all the nodes in the 
+selected clusters and are presented in table  \ref{table:grid5000}.

 
 

-\subsection{The evaluation of the proposed algorithm}
-\label{sec.verif.algo}
-The precision  of the  proposed algorithm mainly  depends on the  execution time
-prediction model  defined in  (\ref{eq:perf}) and the  energy model  computed by
-(\ref{eq:energy}).   The energy  model is  also significantly  dependent  on the
-execution  time model  because  the static  energy  is linearly  related to  the
-execution time  and the dynamic energy  is related to the  computation time. So,
-all the works presented  in this paper are 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 it does not test all the
-possible solutions (vectors of scaling factors) in the search space. 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 $O(F\cdot N)$, 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}, 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, 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 with
-highest frequency, each node consumed an amount of power proportional to its
-computing power (which corresponds to \np[\%]{80} of its dynamic power and the
-remaining \np[\%]{20} to 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.

 
 
 
 

-\begin{table}[!t]
-  \caption{Heterogeneous nodes characteristics}
-  % title of Table
+\begin{figure}[!t]

   \centering
   \centering

-  \begin{tabular}{|*{7}{r|}}
-    \hline
-    Node          &Simulated  & Max      & Min          & Diff.          & Dynamic      & Static \\
-    type          &GFLOPS     & Freq.    & Freq.        & Freq.          & power        & power \\
-                  &           & GHz      & GHz          &GHz             &              &       \\
-    \hline
-    1             &40         & 2.50     & 1.20         & 0.100          & \np[W]{20}   &\np[W]{4} \\
-         
-    \hline
-    2             &50         & 2.66     & 1.60         & 0.133          & \np[W]{25}   &\np[W]{5} \\
-                  
-    \hline
-    3             &60         & 2.90     & 1.20         & 0.100          & \np[W]{30}   &\np[W]{6} \\
-                  
-    \hline
-    4             &70         & 3.40     & 1.60         & 0.133          & \np[W]{35}   &\np[W]{7} \\
-                  
-    \hline
-  \end{tabular}
-  \label{table:platform}
-\end{table}
+  \includegraphics[scale=1]{fig/grid5000}
+  \caption{The selected two sites of grid'5000}
+  \label{fig:grid5000}
+\end{figure}

 
 

- 
-%\subsection{Performance prediction verification}

 
 

+The energy model and the scaling factors selection algorithm were applied to the NAS parallel benchmarks v3.3 \cite{NAS.Parallel.Benchmarks} and evaluated over grid'5000.
+The benchmark suite contains seven applications: CG, MG, EP, LU, BT, SP and FT. These applications have different computations and communications ratios and strategies which make them good testbed applications to evaluate the proposed algorithm and energy model.
+The benchmarks have seven different classes, S, W, A, B, C, D and E, that represent the size of the problem that the method solves. In this work, the class D was used for all benchmarks in all the experiments presented in the next sections. 

 
 

-\subsection{The experimental results of the scaling algorithm}
-\label{sec.res}

 
 
 
 

-The proposed algorithm was applied to the seven parallel NAS benchmarks (EP, CG,
-MG, FT, BT, LU and SP) and  the benchmarks were executed with the three classes:
-A, B and C. However, due to the lack of space in this paper, only the results of
-the  biggest class,  C, are  presented while  being run  on different  number of
-nodes,  ranging from 4  to 128  or 144  nodes depending  on the  benchmark being
-executed. Indeed, the benchmarks CG, MG, LU, EP and FT had to be executed on $1,
-2, 4, 8, 16, 32, 64, 128$ nodes.   The other benchmarks such as BT and SP had to
-be executed on $1, 4, 9, 16, 36, 64, 144$ nodes.

 
 

- 
- 
-\begin{table}[!t]
-  \caption{Running NAS benchmarks on 4 nodes }
-  % title of Table
+\begin{figure}[!t]

   \centering
   \centering

-  \begin{tabular}{|*{7}{r|}}
-    \hline
-    \hspace{-2.2084pt}%
-    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       \\
-    \hline 
-    MG         & 18.89         & 1074.87           &35.37            &4.34                   &31.03       \\
-   \hline
-    EP         &79.73         &5521.04         &26.83            &3.04               &23.79      \\
-   \hline
-    LU         &308.65        &21126.00           &34.00             &6.16                   &27.84      \\
-    \hline
-    BT         &360.12         &21505.55          &35.36         &8.49               &26.87     \\
-   \hline
-    SP         &234.24        &13572.16           &35.22         &5.70               &29.52    \\
-   \hline
-    FT         &81.58          &4151.48        &35.58         &0.99                  &34.59    \\
-\hline 
-  \end{tabular}
-  \label{table:res_4n}
-% \end{table}
+  \includegraphics[scale=0.6]{fig/power_consumption.pdf}
+  \caption{The power consumption by one core from Taurus cluster}
+  \label{fig:power_cons}
+\end{figure}

 
 

-\medskip
-% \begin{table}[!t]
-  \caption{Running NAS benchmarks on 8 and 9 nodes }
-  % title of Table
-  \centering
-  \begin{tabular}{|*{7}{r|}}
-    \hline
-     \hspace{-2.2084pt}%
-    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     \\
-    \hline 
-    MG         &8.99          &953.39          &33.78        &6.41                    &27.37     \\
-   \hline
-    EP         &40.39         &5652.81         &27.04        &0.49                    &26.55     \\
-   \hline
-    LU         &218.79            &36149.77        &28.23        &0.01                    &28.22      \\
-    \hline
-    BT         &166.89                &23207.42            &32.32            &7.89                    &24.43      \\
-   \hline
-    SP         &104.73        &18414.62            &24.73            &2.78                    &21.95      \\
-   \hline
-    FT         &51.10         &4913.26         &31.02        &2.54                    &28.48      \\
-\hline 
-  \end{tabular}
-  \label{table:res_8n}
-% \end{table}

 
 

-\medskip
-% \begin{table}[!t]
-  \caption{Running NAS benchmarks on 16 nodes }
-  % title of Table
-  \centering
-  \begin{tabular}{|*{7}{r|}}
-    \hline
-    \hspace{-2.2084pt}%
-    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          \\
-    \hline 
-    MG         &5.71          &1076.19         &32.49        &6.05                    &26.44         \\
-   \hline
-    EP         &20.11         &5638.49         &26.85        &0.56                    &26.29         \\
-   \hline
-    LU         &144.13        &42529.06            &28.80            &6.56                    &22.24         \\
-    \hline
-    BT         &97.29         &22813.86            &34.95        &5.80                &29.15         \\
-   \hline
-    SP         &66.49         &20821.67            &22.49            &3.82                    &18.67         \\
-   \hline
-    FT            &37.01          &5505.60             &31.59        &6.48                    &25.11         \\
-\hline 
-  \end{tabular}
-  \label{table:res_16n}
-% \end{table}

 
 

-\medskip
-% \begin{table}[!t]
-  \caption{Running NAS benchmarks on 32 and 36 nodes }
+  
+\begin{table}[!t]
+  \caption{CPUs characteristics of the selected clusters}

   % title of Table
   \centering
   % title of Table
   \centering

-  \begin{tabular}{|*{7}{r|}}
+  \begin{tabular}{|*{7}{c|}}

     \hline
     \hline

-    \hspace{-2.2084pt}%
-    Program    & Execution     & Energy         & Energy      & Performance        & Distance      \\
-    name       & time/s        & consumption/J  & saving\%    & degradation\%      &               \\
+    Cluster     & CPU         & Max   & Min   & Diff. & no. of cores    & dynamic power   \\
+    Name        & model       & Freq. & Freq. & Freq. & per CPU         & of one core     \\
+                &             & GHz   & GHz   & GHz   &                 &           \\

     \hline
     \hline

-    CG         &32.35         &6704.21         &16.15        &5.30                    &10.85           \\
-    \hline 
-    MG         &4.30          &1355.58         &28.93        &8.85                    &20.08          \\
-   \hline
-    EP         &9.96           &5519.68                &26.98        &0.02                    &26.96          \\
-   \hline
-    LU         &99.93         &67463.43            &23.60            &2.45                    &21.15          \\
+    Taurus      & Intel       & 2.3  & 1.2  & 0.1     & 6               & \np[W]{35} \\
+                & Xeon        &       &       &       &                 &            \\
+                & E5-2630     &       &       &       &                 &            \\         

     \hline
     \hline

-    BT         &48.61         &23796.97            &34.62            &5.83                    &28.79          \\
-   \hline
-    SP         &46.01         &27007.43            &22.72            &3.45                    &19.27           \\
-   \hline
-    FT            &28.06          &7142.69             &23.09        &2.90                    &20.19           \\
-\hline 
-  \end{tabular}
-  \label{table:res_32n}
-% \end{table}
-
-\medskip
-% \begin{table}[!t]
-  \caption{Running NAS benchmarks on 64 nodes }
-  % title of Table
-  \centering
-  \begin{tabular}{|*{7}{r|}}
+    Graphene    & Intel       & 2.53  & 1.2   & 0.133 & 4               & \np[W]{23} \\
+                & Xeon        &       &       &       &                 &            \\
+                & X3440       &       &       &       &                 &            \\    

     \hline
     \hline

-    \hspace{-2.2084pt}%
-    Program    & Execution     & Energy         & Energy      & Performance        & Distance      \\
-    name       & time/s        & consumption/J  & saving\%    & degradation\%      &               \\
+    Griffon     & Intel       & 2.5   & 2     & 0.5   & 4               & \np[W]{46} \\
+                & Xeon        &       &       &       &                 &            \\
+                & L5420       &       &       &       &                 &            \\  

     \hline
     \hline

-    CG         &46.65         &17521.83            &8.13             &1.68                    &6.45           \\
-    \hline 
-    MG         &3.27          &1534.70         &29.27        &14.35               &14.92          \\
-   \hline
-    EP         &5.05           &5471.1084          &27.12            &3.11                &24.01         \\
-   \hline
-    LU         &73.92         &101339.16           &21.96            &3.67                    &18.29         \\
+    Graphite    & Intel       & 2     & 1.2   & 0.1   & 8               & \np[W]{35} \\
+                & Xeon        &       &       &       &                 &            \\
+                & E5-2650     &       &       &       &                 &            \\  

     \hline
     \hline

-    BT         &39.99         &27166.71            &32.02            &12.28               &19.74         \\
-   \hline
-    SP         &52.00         &49099.28            &24.84            &0.03                    &24.81         \\
-   \hline
-    FT         &25.97         &10416.82        &20.15        &4.87                    &15.28         \\
-\hline 

   \end{tabular}
   \end{tabular}

-  \label{table:res_64n}
-% \end{table}
+  \label{table:grid5000}
+\end{table} 

 
 

-\medskip
-% \begin{table}[!t]
-  \caption{Running NAS benchmarks on 128 and 144 nodes }
-  % title of Table
-  \centering
-  \begin{tabular}{|*{7}{r|}}
-    \hline
-    \hspace{-2.2084pt}%
-    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          \\
-    \hline 
-    MG         &3.55           &2843.33         &18.77       &10.38               &8.39          \\
-   \hline
-    EP         &2.67           &5669.66                &27.09        &0.03                    &27.06         \\
-   \hline
-    LU         &51.23         &144471.90       &16.67        &2.36                    &14.31         \\
-    \hline
-    BT         &37.96          &44243.82           &23.18            &1.28                    &21.90         \\
-   \hline
-    SP         &64.53         &115409.71           &26.72            &0.05                    &26.67         \\
-   \hline
-    FT         &25.51         &18808.72            &12.85            &2.84                    &10.01         \\
-\hline 
-  \end{tabular}
-  \label{table:res_128n}
-\end{table}
-The overall energy consumption was computed for each instance according to the
-energy consumption model (\ref{eq:energy}), with and without applying the
-algorithm. The execution time was also measured for all these experiments. Then,
-the energy saving and performance degradation percentages were computed for each
-instance.  The results are presented in Tables~\ref{table:res_4n},
-\ref{table:res_8n}, \ref{table:res_16n}, \ref{table:res_32n},
-\ref{table:res_64n} and \ref{table:res_128n}. All these results are the average
-values from many experiments for energy savings and performance degradation.
-The tables show the experimental results for running the NAS parallel benchmarks
-on different number of nodes.  The experiments show that the algorithm
-significantly reduces the energy consumption (up to \np[\%]{35}) and tries to
-limit the performance degradation.  They also show that the energy saving
-percentage decreases when the number of computing nodes increases.  This
-reduction is due to the increase of the communication times compared to the
-execution times when the benchmarks are run over a high number of nodes.
-Indeed, the benchmarks with the same class, C, are executed on different numbers
-of nodes, so the computation required for each iteration is divided by the
-number of computing nodes.  On the other hand, more communications are required
-when increasing the number of nodes so the static energy increases linearly
-according to the communication time and the dynamic power is less relevant in
-the overall energy consumption.  Therefore, reducing the frequency with
-Algorithm~\ref{HSA} is less effective in reducing the overall energy savings. It
-can also be noticed that for the benchmarks EP and SP that contain little or no
-communications, the energy savings are not significantly affected by the high
-number of nodes.  No experiments were conducted using bigger classes than D,
-because they require a lot of memory (more than 64GB) when being executed by the
-simulator on one machine.  The maximum distance between the normalized energy
-curve and the normalized performance for each instance is also shown in the
-result tables. It decrease in the same way as the energy saving percentage.  The
-tables also show that the performance degradation percentage is not
-significantly increased when the number of computing nodes is increased because
-the computation times are small when compared to the communication times.
-
-
- 
-\begin{figure}[!t]
-  \centering
-  \subfloat[Energy saving]{%
-    \includegraphics[width=.33\textwidth]{fig/energy}\label{fig:energy}}%
-  
-  \subfloat[Performance degradation ]{%
-    \includegraphics[width=.33\textwidth]{fig/per_deg}\label{fig:per_deg}}
-  \label{fig:avg}
-  \caption{The energy and performance for all NAS benchmarks running with a different number of nodes}
-\end{figure}

 
 

-Figures~\ref{fig:energy} and  \ref{fig:per_deg} present  the energy  saving and
-performance  degradation 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 decrease linearly when  the number of nodes
-increase. 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 little or  no communications. Finally, the energy saving of
-the  GC benchmark  significantly  decrease  when the  number  of nodes  increase
-because this benchmark has more  communications than the others. The second plot
-shows that  the performance  degradation percentages of  most of  the benchmarks
-decrease when  they run on a  big number of  nodes because they spend  more time
-communicating than computing,  thus, scaling down the frequencies  of some nodes
-has less effect on the performance.

 
 
 
 

+\subsection{The experimental results of the scaling algorithm}
+\label{sec.res}

 
 

+\subsection{The experimental results of multi-cores clusters}
+\label{sec.res}

 
 \subsection{The results for different power consumption scenarios}
 \label{sec.compare}
 
 \subsection{The results for different power consumption scenarios}
 \label{sec.compare}

-The results of the previous section were obtained while using processors that
-consume during computation an overall power which is \np[\%]{80} composed of
-dynamic power and of \np[\%]{20} of static power. In this section, these ratios
-are changed and two new power scenarios are considered in order to evaluate how
-the proposed algorithm adapts itself according to the static and dynamic power
-values.  The two new power scenarios are the following:
-
-\begin{itemize}
-\item \np[\%]{70} of dynamic power and \np[\%]{30} of static power
-\item \np[\%]{90} of dynamic power and \np[\%]{10} of static power
-\end{itemize}
-
-The NAS parallel benchmarks were executed again over processors that follow the
-new power scenarios.  The class C of each benchmark was run over 8 or 9 nodes
-and the results are presented in Tables~\ref{table:res_s1} and
-\ref{table:res_s2}. These tables show that the energy saving percentage of the
-\np[\%]{70}-\np[\%]{30} scenario is smaller for all benchmarks compared to the
-energy saving of the \np[\%]{90}-\np[\%]{10} scenario.  Indeed, in the latter
-more dynamic power is consumed when nodes are running on their maximum
-frequencies, thus, scaling down the frequency of the nodes results in higher
-energy savings than in the \np[\%]{70}-\np[\%]{30} scenario. On the other hand,
-the performance degradation percentage is smaller in the \np[\%]{70}-\np[\%]{30}
-scenario compared to the \np[\%]{90}-\np[\%]{10} scenario. This is due to the
-higher static power percentage in the first scenario which makes it more
-relevant in the overall consumed energy.  Indeed, the static energy is related
-to the execution time and if the performance is degraded the amount of consumed
-static energy directly increases.  Therefore, the proposed algorithm does not
-really significantly scale down much the frequencies of the nodes in order to
-limit the increase of the execution time and thus limiting the effect of the
-consumed static energy.
-
-Both new power scenarios are compared to the old one in
-Figure~\ref{fig:sen_comp}. It shows the average of the performance degradation,
-the energy saving and the distances for all NAS benchmarks of class C running on
-8 or 9 nodes.  The comparison shows that the energy saving ratio is proportional
-to the dynamic power ratio: it is increased when applying the
-\np[\%]{90}-\np[\%]{10} scenario because at maximum frequency the dynamic energy
-is the most relevant in the overall consumed energy and can be reduced by
-lowering the frequency of some processors. On the other hand, the energy saving
-decreases when the \np[\%]{70}-\np[\%]{30} scenario is used because the dynamic
-energy is less relevant in the overall consumed energy and lowering the
-frequency does not return big energy savings.  Moreover, the average of the
-performance degradation is decreased when using a higher ratio for static power
-(e.g.  \np[\%]{70}-\np[\%]{30} scenario and \np[\%]{80}-\np[\%]{20}
-scenario). Since the proposed algorithm optimizes the energy consumption when
-using a higher ratio for dynamic power the algorithm selects bigger frequency
-scaling factors that result in more energy saving but less performance, for
-example see Figure~\ref{fig:scales_comp}. The opposite happens when using a
-higher ratio for static power, the algorithm proportionally selects smaller
-scaling values which result in less energy saving but also less performance
-degradation.
-
-
- \begin{table}[!t]
-  \caption{The results of the \np[\%]{70}-\np[\%]{30} power scenario}
-  % title of Table
-  \centering
-  \begin{tabular}{|*{6}{r|}}
-    \hline
-    Program    & Energy          & Energy      & Performance        & Distance     \\
-    name       & consumption/J   & saving\%    & degradation\%      &              \\
-    \hline
-    CG         &4144.21          &22.42        &7.72                &14.70         \\
-    \hline 
-    MG         &1133.23          &24.50        &5.34                &19.16          \\
-   \hline
-    EP         &6170.30                &16.19         &0.02                &16.17          \\
-   \hline
-    LU         &39477.28        &20.43        &0.07                &20.36          \\
-    \hline
-    BT         &26169.55           &25.34             &6.62                &18.71          \\
-   \hline
-    SP         &19620.09           &19.32             &3.66                &15.66          \\
-   \hline
-    FT         &6094.07                &23.17         &0.36                &22.81          \\
-\hline 
-  \end{tabular}
-  \label{table:res_s1}
-\end{table}
-
-
-
-\begin{table}[!t]
-  \caption{The results of the \np[\%]{90}-\np[\%]{10} power scenario}
-  % title of Table
-  \centering
-  \begin{tabular}{|*{6}{r|}}
-    \hline
-    Program    & Energy          & Energy      & Performance        & Distance     \\
-    name       & consumption/J   & saving\%    & degradation\%      &              \\
-    \hline
-    CG         &2812.38                 &36.36        &6.80                &29.56         \\
-    \hline 
-    MG         &825.427                 &38.35        &6.41                &31.94         \\
-   \hline
-    EP         &5281.62                 &35.02        &2.68                &32.34         \\
-   \hline
-    LU         &31611.28            &39.15        &3.51                    &35.64        \\
-    \hline
-    BT         &21296.46            &36.70            &6.60                &30.10       \\
-   \hline
-    SP         &15183.42            &35.19            &11.76               &23.43        \\
-   \hline
-    FT         &3856.54                 &40.80        &5.67                &35.13        \\
-\hline 
-  \end{tabular}
-  \label{table:res_s2}
-\end{table}

 
 

-\begin{table}[!t]
- \caption{Comparing the proposed algorithm}
- \centering
-\begin{tabular}{|*{7}{r|}}
-\hline
-Program & \multicolumn{2}{c|}{Energy saving \%} & \multicolumn{2}{c|}{Perf.  degradation \%} & \multicolumn{2}{c|}{Distance} \\ \cline{2-7} 
-name    & EDP             & MaxDist          & EDP            & MaxDist           & EDP          & MaxDist        \\ \hline
-CG      & 27.58           & 31.25            & 5.82           & 7.12              & 21.76        & 24.13          \\ \hline
-MG      & 29.49           & 33.78            & 3.74           & 6.41              & 25.75        & 27.37          \\ \hline
-LU      & 19.55           & 28.33            & 0.0            & 0.01              & 19.55        & 28.22          \\ \hline
-EP      & 28.40           & 27.04            & 4.29           & 0.49              & 24.11        & 26.55          \\ \hline
-BT      & 27.68           & 32.32            & 6.45           & 7.87              & 21.23        & 24.43          \\ \hline
-SP      & 20.52           & 24.73            & 5.21           & 2.78              & 15.31         & 21.95         \\ \hline
-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
-  \subfloat[Comparison  between the results on 8 nodes]{%
-    \includegraphics[width=.33\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}}
-  \label{fig:comp}
-  \caption{The comparison of the three power scenarios}
-\end{figure}  
-
-\begin{figure}[!t]
-  \centering
-   \includegraphics[scale=0.5]{fig/compare_EDP.pdf}
-  \caption{Trade-off comparison for NAS benchmarks class C}
-  \label{fig:compare_EDP}
-\end{figure}

 
 
 \subsection{The comparison of the proposed scaling algorithm }
 \label{sec.compare_EDP}
 
 
 \subsection{The comparison of the proposed scaling algorithm }
 \label{sec.compare_EDP}

-In this section, the scaling factors selection algorithm, called MaxDist, is
-compared to Spiliopoulos et al. algorithm
-\cite{Spiliopoulos_Green.governors.Adaptive.DVFS}, called EDP.  They developed a
-green governor that regularly applies an online frequency selecting algorithm to
-reduce the energy consumed by a multicore architecture without degrading much
-its performance. The algorithm selects the frequencies that minimize the energy
-and delay products, $\mathit{EDP}=\mathit{energy}\times \mathit{delay}$ using
-the predicted overall energy consumption and execution time delay for each
-frequency.  To fairly compare both algorithms, the same energy and execution
-time models, equations (\ref{eq:energy}) and (\ref{eq:fnew}), were used for both
-algorithms to predict the energy consumption and the execution times. Also
-Spiliopoulos et al. algorithm was adapted to start the search from the initial
-frequencies computed using the equation (\ref{eq:Fint}). The resulting algorithm
-is an exhaustive search algorithm that minimizes the EDP and has the initial
-frequencies values as an upper bound.
-
-Both algorithms were applied to the parallel NAS benchmarks to compare their
-efficiency. Table~\ref{table:compare_EDP} presents the results of comparing the
-execution times and the energy consumption for both versions of the NAS
-benchmarks while running the class C of each benchmark over 8 or 9 heterogeneous
-nodes. The results show that our algorithm provides better energy savings than
-Spiliopoulos et al. algorithm, on average it results in \np[\%]{29.76} energy
-saving while their algorithm returns just \np[\%]{25.75}. The average of
-performance degradation percentage is approximately the same for both
-algorithms, about \np[\%]{4}.
-
-
-For all benchmarks,  our algorithm outperforms Spiliopoulos et  al. algorithm in
-terms of  energy and  performance trade-off, see  Figure~\ref{fig:compare_EDP},
-because it maximizes the distance  between the energy saving and the performance
-degradation values while giving the same weight for both metrics.
+

 
 
 \section{Conclusion}
 
 
 \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 trade-off) 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 platforms. 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 \np[\%]{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.  Finally, the algorithm was
-compared to Spiliopoulos et al.  algorithm and the results showed that it
-outperforms their algorithm in terms of energy-time trade-off.
-
-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 platforms 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  other tasks  to finish  their  works.  The
-development of such a method might require a new energy model because the number
-of iterations is  not known in advance and depends on  the global convergence of
-the iterative system.
+\label{sec.concl}

 
 

-\section*{Acknowledgment}

 
 

-This work has been partially supported by the Labex
-ACTION project (contract “ANR-11-LABX-01-01”). As a PhD student, 
-Mr. Ahmed Fanfakh, would like to thank the University of
-Babylon (Iraq) for supporting his work. 

 
 

+\section*{Acknowledgment}
+
+This work  has been  partially supported by  the Labex ACTION  project (contract
+``ANR-11-LABX-01-01'').  Computations  have been performed  on the supercomputer
+facilities  of the  Mésocentre de  calcul de  Franche-Comté. As  a  PhD student,
+Mr. Ahmed  Fanfakh, would  like to  thank the University  of Babylon  (Iraq) for
+supporting his work.

 
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