X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy.git/blobdiff_plain/ce79ce10130fe42f774d3898afeb5699b82274b8..0f6674d9fe0512b7509ffc2e2a1e2d484c8354c6:/paper.tex?ds=inline

diff --git a/paper.tex b/paper.tex
index 657bea7..2838f2d 100644
--- a/paper.tex
+++ b/paper.tex
@@ -28,7 +28,7 @@
 \begin{document}
 
 \title{Dynamic Frequency Scaling for Energy Consumption
-  Reduction in Distributed MPI Programs}
+  Reduction in Synchronous Distributed Applications}
 
 \author{%
   \IEEEauthorblockN{%
@@ -53,12 +53,10 @@
 \begin{abstract}
   Dynamic Voltage Frequency Scaling (DVFS) can be applied to modern CPUs.  This
   technique is usually used to reduce the energy consumed by a CPU while
-  computing.  Indeed, power consumption by a processor at a given instant is
-  exponentially related to its frequency.  Thus, decreasing the frequency
+  computing.  Thus, decreasing the frequency
   reduces the power consumed by the CPU.  However, it can also significantly
   affect the performance of the executed program if it is compute bound and if a
-  low CPU frequency is selected.  The performance degradation ratio can even be
-  higher than the saved energy ratio.  Therefore, the chosen scaling factor must
+  low CPU frequency is selected.  Therefore, the chosen scaling factor must
   give the best possible trade-off between energy reduction and performance.
 
   In this paper we present an algorithm that predicts the energy consumed with
@@ -81,26 +79,26 @@ coming years.  To satisfy this demand, researchers and supercomputers
 constructors have been regularly increasing the number of computing cores and
 processors in supercomputers (for example in November 2013, according to the
 TOP500 list~\cite{43}, the Tianhe-2 was the fastest supercomputer.  It has more
-than 3 millions of cores and delivers more than \np[Tflop/s]{33} while consuming
+than 3 million of cores and delivers more than \np[Tflop/s]{33} while consuming
 \np[kW]{17808}).  This large increase in number of computing cores has led to
 large energy consumption by these architectures.  Moreover, the price of energy
 is expected to continue its ascent according to the demand.  For all these
-reasons energy reduction became an important topic in the high performance
-computing field.  To tackle this problem, many researchers used DVFS (Dynamic
+reasons energy reduction has become an important topic in the high performance
+computing field.  To tackle this problem, many researchers use DVFS (Dynamic
 Voltage Frequency Scaling) operations which reduce dynamically the frequency and
 voltage of cores and thus their energy consumption.  Indeed, modern CPUs offer a
 set of acceptable frequencies which are usually called gears, and the user or
 the operating system can modify the frequency of the processor according to its
 needs.  However, DVFS also degrades the performance of computation.  Therefore
-researchers try to reduce the frequency to minimum when processors are idle
+researchers try to reduce the frequency to the minimum when processors are idle
 (waiting for data from other processors or communicating with other processors).
-Moreover, depending on their objectives they use heuristics to find the best
+Moreover, depending on their objectives, they use heuristics to find the best
 scaling factor during the computation.  If they aim for performance they choose
 the best scaling factor that reduces the consumed energy while affecting as
 little as possible the performance.  On the other hand, if they aim for energy
 reduction, the chosen scaling factor must produce the most energy efficient
 execution without considering the degradation of the performance.  It is
-important to notice that lowering the frequency to minimum value does not always
+important to notice that lowering the frequency to the minimum value does not always
 give the most energy efficient execution due to energy leakage.  The best
 scaling factor might be chosen during execution (online) or during a
 pre-execution phase.  In this paper, we present an algorithm that selects a
@@ -108,21 +106,18 @@ frequency scaling factor that simultaneously takes into consideration the energy
 consumption by the CPU and the performance of the application.  The main
 objective of HPC systems is to execute as fast as possible the application.
 Therefore, our algorithm selects the scaling factor online with very small
-footprint.  The proposed algorithm takes into account the communication times of
-the MPI program to choose the scaling factor.  This algorithm has ability to
+overhead.  The proposed algorithm takes into account the communication times of
+the MPI program to choose the scaling factor.  This algorithm has the ability to
 predict both energy consumption and execution time over all available scaling
 factors.  The prediction achieved depends on some computing time information,
-gathered at the beginning of the runtime.  We apply this algorithm to seven MPI
-benchmarks.  These MPI programs are the NAS parallel benchmarks (NPB v3.3)
-developed by NASA~\cite{44}.  Our experiments are executed using the simulator
+gathered at the beginning of the runtime.  We apply this algorithm to the NAS parallel benchmarks (NPB v3.3)~\cite{44}.  Our experiments are executed using the simulator
 SimGrid/SMPI v3.10~\cite{Casanova:2008:SGF:1397760.1398183} over an homogeneous
 distributed memory architecture.  Furthermore, we compare the proposed algorithm
 with Rauber and RÃ¼nger methods~\cite{3}.  The comparison's results show that our
 algorithm gives better energy-time trade-off.
 
 This paper is organized as follows: Section~\ref{sec.relwork} presents some
-related works from other authors.  Section~\ref{sec.exe} explains the execution
-of parallel tasks and the sources of slack times.  It also presents an energy
+related works from other authors.  Section~\ref{sec.exe}  presents an energy
 model for homogeneous platforms.  Section~\ref{sec.mpip} describes how the
 performance of MPI programs can be predicted.  Section~\ref{sec.compet} presents
 the energy-performance objective function that maximizes the reduction of energy
@@ -144,121 +139,73 @@ classified into two categories: offline and online methods.
 
 The offline scaling factor selection methods are executed before the runtime of
 the program.  They return static scaling factor values to the processors
-participating in the execution of the parallel program.  On one hand, the
+participating in the execution of the parallel program.  On the one hand, the
 scaling factor values could be computed based on information retrieved by
 analyzing the code of the program and the computing system that will execute it.
 In~\cite{40}, Azevedo et al. detect during compilation the dependency points
 between tasks in a multi-task program.  This information is then used to lower
 the frequency of some processors in order to eliminate slack times.  A slack
-time is the period of time during which a processor that have already finished
-its computation, have to wait for a set of processors to finish their
+time is the period of time during which a processor that has already finished
+its computation, has to wait for a set of processors to finish their
 computations and send their results to the waiting processor in order to
 continue its task that is dependent on the results of computations being
 executed on other processors.  Freeh et al. showed in~\cite{17} that the
 communication times of MPI programs do not change when the frequency is scaled
 down.  On the other hand, some offline scaling factor selection methods use the
-information gathered from previous full or partial executions of the program.  A
-part or the whole program is usually executed over all the available frequency
-gears and the the execution time and the energy consumed with each frequency
-gear are measured.  Then an heuristic or an exact method uses the retrieved
+information gathered from previous full or partial executions of the program. The whole program or, a 
+part of it,  is usually executed over all the available frequency
+gears and the execution time and the energy consumed with each frequency
+gear are measured.  Then a heuristic or an exact method uses the retrieved
 information to compute the values of the scaling factor for the processors.
-In~\cite{29}, Xie et al. use an exact exponential breadth-first search algorithm
-to compute the scaling factor values that give the optimal energy reduction
-while respecting a deadline for a sequential program.  They also present a
-linear heuristic that approximates the optimal solution.  In~\cite{8} , Rountree
-et al. use a linear programming algorithm, while in~\cite{38,34}, Cochran et
-al. use multi logistic regression algorithm for the same goal.  The main
-drawback for these methods is that they all require executing a part or the
-whole program on all frequency gears for each new instance of the same program.
+In~\cite{8} , Rountree et al. use a linear programming algorithm, while in~\cite{34}, Cochran et
+al. use a multi-logistic regression algorithm for the same goal.  The main
+drawback of these methods is that they all require executing the
+whole program or, a part of it, on all frequency gears for each new instance of the same program.
 
 \subsection{Online scaling factor selection methods}
 
 The online scaling factor selection methods are executed during the runtime of
 the program.  They are usually integrated into iterative programs where the same
 block of instructions is executed many times.  During the first few iterations,
-many informations are measured such as the execution time, the energy consumed
+a lot of information is measured such as the execution time, the energy consumed
 using a multimeter, the slack times, \dots{} Then a method will exploit these
 measurements to compute the scaling factor values for each processor.  This
 operation, measurements and computing new scaling factor, can be repeated as
-much as needed if the iterations are not regular.  Kimura, Peraza, Yu-Liang et
-al.~\cite{11,2,31} used learning methods to select the appropriate scaling
+much as needed if the iterations are not regular. Peraza, Yu-Liang et
+al.~\cite{2,31} used varied heuristics to select the appropriate scaling
 factor values to eliminate the slack times during runtime.  However, as seen
-in~\cite{39,19}, machine learning methods can take a lot of time to converge
+in~\cite{19}, machine learning method takes a lot of time to converge
 when the number of available gears is big.  To reduce the impact of slack times,
 in~\cite{1}, Lim et al. developed an algorithm that detects the communication
 sections and changes the frequency during these sections only.  This approach
 might change the frequency of each processor many times per iteration if an
 iteration contains more than one communication section.  In~\cite{3}, Rauber and
-RÃ¼nger used an analytical model that can predict the consumed energy and the
-execution time for every frequency gear after measuring the consumed energy and
-the execution time with the highest frequency gear.  These predictions may be
-used to choose the optimal gear for each processor executing the parallel
-program to reduce energy consumption.  To maintain the performance of the
-parallel program , they set the processor with the biggest load to the highest
-gear and then compute the scaling factor values for the rest of the processors.
-Although this model was built for parallel architectures, it can be adapted to
-distributed architectures by taking into account the communications.  The
-primary contribution of our paper is presenting a new online scaling factor
-selection method which has the following characteristics:
-\begin{enumerate}
-\item It is based on Rauber and RÃ¼nger analytical model to predict the energy
-  consumption of the application with different frequency gears.
-\item It selects the frequency scaling factor for simultaneously optimizing
-  energy reduction and maintaining performance.
-\item It is well adapted to distributed architectures because it takes into
-  account the communication time.
-\item It is well adapted to distributed applications with imbalanced tasks.
-\item it has very small footprint when compared to other methods
+RÃ¼nger used an analytical model that can predict the consumed energy for every frequency gear after measuring the consumed energy. They
+maintain the performance as mush as possible by setting the highest frequency gear to the slowest task. 
+
+The primary contribution of 
+our paper is to present a new online scaling factor selection method which has the
+ following characteristics:\\
+1) It is based on Rauber and RÃ¼nger analytical model to predict the energy
+  consumption of the application with different frequency gears. 
+2) It selects the frequency scaling factor for simultaneously optimizing
+  energy reduction and maintaining performance. 
+3) It is well adapted to distributed architectures because it takes into
+  account the communication time. 
+4) It is well adapted to distributed applications with imbalanced tasks.
+5) It has a very small overhead when compared to other methods
   (e.g.,~\cite{19}) and does not require profiling or training as
-  in~\cite{38,34}.
-\end{enumerate}
+  in~\cite{34}.
 
 
-\section{Execution and energy of parallel tasks on homogeneous platform}
-\label{sec.exe}
-
 % \JC{The whole subsection ``Parallel Tasks Execution on Homogeneous Platform'',
 %   can be deleted if we need space, we can just say we are interested in this
 %   paper in homogeneous clusters}
 
-\subsection{Parallel tasks execution on homogeneous platform}
-
-A homogeneous cluster consists of identical nodes in terms of hardware and
-software.  Each node has its own memory and at least one processor which can be
-a multi-core.  The nodes are connected via a high bandwidth network.  Tasks
-executed on this model can be either synchronous or asynchronous.  In this paper
-we consider execution of the synchronous tasks on distributed homogeneous
-platform.  These tasks can exchange the data via synchronous message passing.
-\begin{figure*}[t]
-  \centering
-  \subfloat[Sync. imbalanced communications]{%
-    \includegraphics[scale=0.67]{fig/commtasks}\label{fig:h1}}
-  \subfloat[Sync. imbalanced computations]{%
-    \includegraphics[scale=0.67]{fig/compt}\label{fig:h2}}
-  \caption{Parallel tasks on homogeneous platform}
-  \label{fig:homo}
-\end{figure*}
-Therefore, the execution time of a task consists of the computation time and the
-communication time.  Moreover, the synchronous communications between tasks can
-lead to slack times while tasks wait at the synchronization barrier for other
-tasks to finish their tasks (see figure~(\ref{fig:h1})).  The imbalanced
-communications happen when nodes have to send/receive different amount of data
-or they communicate with different number of nodes.  Another source of slack
-times is the imbalanced computations.  This happens when processing different
-amounts of data on each processor (see figure~(\ref{fig:h2})).  In this case the
-fastest tasks have to wait at the synchronization barrier for the slowest ones
-to begin the next task.  In both cases the overall execution time of the program
-is the execution time of the slowest task as in EQ~(\ref{eq:T1}).
-\begin{equation}
-  \label{eq:T1}
-  \textit{Program Time} = \max_{i=1,2,\dots,N} T_i
-\end{equation}
-where $T_i$ is the execution time of task $i$ and all the tasks are executed
-concurrently on different processors.
-
-\subsection{Energy model for homogeneous platform}
 
-Many researchers~\cite{9,3,15,26} divide the power consumed by a processor to
+\section{Energy model for homogeneous platform}
+\label{sec.exe}
+Many researchers~\cite{9,3,15,26} 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 on, the latter is only consumed during
 computation times.  The dynamic power $P_{dyn}$ is related to the switching
@@ -283,7 +230,7 @@ to execute a given program can be computed as:
 \end{equation}
 where $T$ is the execution time of the program, $T_{Comp}$ is the computation
 time and $T_{Comp} \leq T$.  $T_{Comp}$ may be equal to $T$ if there is no
-communications, no slack times and no synchronizations.
+communication, no slack time and no synchronization.
 
 DVFS is a process that is allowed in modern processors to reduce the dynamic
 power by scaling down the voltage and frequency.  Its main objective is to
@@ -299,8 +246,7 @@ ratio between the maximum and the new frequency as in EQ~(\ref{eq:s}).
 \end{equation}
 The value of the scaling factor $S$ is greater than 1 when changing the
 frequency of the CPU to any new frequency value~(\emph{P-state}) in the
-governor.  The CPU governor is an interface driver supplied by the operating
-system's kernel to lower a core's frequency.  This factor reduces quadratically
+governor. This factor reduces quadratically
 the dynamic power which may cause degradation in performance and thus, the
 increase of the static energy because the execution time is increased~\cite{36}.
 If the tasks are sorted according to their execution times before scaling in a
@@ -315,7 +261,7 @@ function of the scaling factor $S$, as in EQ~(\ref{eq:energy}).
     P_\textit{static} \cdot T_1 \cdot S_1 \cdot N
  \hfill
 \end{equation}
-where $N$ is the number of parallel nodes, $T_i$ and $S_i$ for $i=1,\dots,N$ are
+where $N$ is the number of parallel nodes, $T_i$ for $i=1,\dots,N$ are
 the execution times and scaling factors of the sorted tasks.  Therefore, $T1$ is
 the time of the slowest task, and $S_1$ its scaling factor which should be the
 highest because they are proportional to the time values $T_i$.  The scaling
@@ -325,11 +271,9 @@ factors are computed as in EQ~(\ref{eq:si}).
   S_i = S \cdot \frac{T_1}{T_i}
       = \frac{F_\textit{max}}{F_\textit{new}} \cdot \frac{T_1}{T_i}
 \end{equation}
-In this paper we depend on Rauber and RÃ¼nger energy model EQ~(\ref{eq:energy})
-for two reasons: (1) this model is used for any number of concurrent tasks, and
-(2) we compare our algorithm with Rauber and RÃ¼nger scaling factor selection
-method which is based on EQ~(\ref{eq:energy}).  The optimal scaling factor is
-computed by minimizing the derivation for this equation which produces
+In this paper we use Rauber and RÃ¼nger's energy model, EQ~(\ref{eq:energy}), because it can be applied to homogeneous clusters if the communication time is taken in consideration. Moreover, we compare our algorithm with Rauber and RÃ¼nger's scaling factor selection
+method which uses the same energy model.  In their method, the optimal scaling factor is
+computed by minimizing the derivation of EQ~(\ref{eq:energy}) which produces
 EQ~(\ref{eq:sopt}).
 
 \begin{equation}
@@ -342,20 +286,20 @@ EQ~(\ref{eq:sopt}).
 \section{Performance evaluation of MPI programs}
 \label{sec.mpip}
 
-The performance (execution time) of parallel synchronous MPI applications depend
-on the time of the slowest task as in figure~(\ref{fig:homo}).  If there is no
+The performance (execution time) of parallel synchronous MPI applications depends
+on the time of the slowest task.  If there is no
 communication and the application is not data bounded, the execution time of a
 parallel program is linearly proportional to the operational frequency and any
 DVFS operation for energy reduction increases the execution time of the parallel
 program.  Therefore, the scaling factor $S$ is linearly proportional to the
-execution time.  However, in most of MPI applications the processes exchange
+execution time.  However, in most MPI applications the processes exchange
 data.  During these communications the processors involved remain idle until the
-communications are finished.  For that reason any change in the frequency has no
+communications are finished.  For that reason, any change in the frequency has no
 impact on the time of communication~\cite{17}.  The communication time for a
 task is the summation of periods of time that begin with an MPI call for sending
 or receiving a message till the message is synchronously sent or received.  To
 be able to predict the execution time of MPI program, the communication time and
-the computation time for the slower task must be measured before scaling.  These
+the computation time for the slowest task must be measured before scaling.  These
 times are used to predict the execution time for any MPI program as a function
 of the new scaling factor as in EQ~(\ref{eq:tnew}).
 \begin{equation}
@@ -365,16 +309,14 @@ of the new scaling factor as in EQ~(\ref{eq:tnew}).
 In this paper, this prediction method is used to select the best scaling factor
 for each processor as presented in the next section.
 
-\section{Performance to energy competition}
+\section{Performance and energy reduction trade-off}
 \label{sec.compet}
 
-This section demonstrates our approach for choosing the optimal scaling factor.
-This factor gives maximum energy reduction taking into account the execution
-times for both computation and communication.  The relation between the energy
-and the performance is nonlinear and complex, because the relation of the energy
-with scaling factor is nonlinear and with the performance it is linear
-see~\cite{17}.  Moreover, they are not measured using the same metric.  For
-solving this problem, we normalize the energy by calculating the ratio between
+This section presents our approach for choosing the optimal scaling factor.
+This factor gives maximum energy reduction while taking into account the execution
+times for both computation and communication.  The relation between the performance
+and the energy is nonlinear and complex. Thus, unlike the relation between the performance and  the scaling factor,  the relation of energy with the scaling factor is nonlinear, for more details refer to~\cite{17}.  Moreover, they are not measured using the same metric.  To
+solve this problem, we normalize the energy by calculating the ratio between
 the consumed energy with scaled frequency and the consumed energy without scaled
 frequency:
 \begin{multline}
@@ -386,7 +328,7 @@ frequency:
               P_\textit{dyn} \cdot \left(T_1+\sum_{i=2}^{N}\frac{T_i^3}{T_1^2}\right) +
               P_\textit{static} \cdot T_1 \cdot N }
 \end{multline}
-By the same way we can normalize the performance as follows:
+In the same way we can normalize the performance as follows:
 \begin{equation}
   \label{eq:pnorm}
   P_\textit{Norm} = \frac{T_\textit{New}}{T_\textit{Old}}
@@ -396,18 +338,17 @@ By the same way we can normalize the performance as follows:
 \end{equation}
 The second problem is that the optimization operation for both energy and
 performance is not in the same direction.  In other words, the normalized energy
-and the performance curves are not in the same direction see
-figure~(\ref{fig:r2}).  While the main goal is to optimize the energy and
+and the performance curves are not at the same direction see
+Figure~(\ref{fig:r2}).  While the main goal is to optimize the energy and
 performance in the same time.  According to the equations~(\ref{eq:enorm})
 and~(\ref{eq:pnorm}), the scaling factor $S$ reduce both the energy and the
 performance simultaneously.  But the main objective is to produce maximum energy
 reduction with minimum performance reduction.  Many researchers used different
 strategies to solve this nonlinear problem for example see~\cite{19,42}, their
-methods add big overhead to the algorithm for selecting the suitable frequency.
-In this paper we present a method to find the optimal scaling factor $S$ for
-optimizing both energy and performance simultaneously without adding big
-overheads.  Our solution for this problem is to make the optimization process
-have the same direction.  Therefore, we inverse the equation of normalize
+methods add big overheads to the algorithm to select the suitable frequency.
+In this paper we present a method to find the optimal scaling factor $S$ to optimize both energy and performance simultaneously without adding a big
+overhead.  Our solution for this problem is to make the optimization process
+for energy and performance follow the same direction.  Therefore, we inverse the equation of the normalized
 performance as follows:
 \begin{equation}
   \label{eq:pnorm_en}
@@ -416,21 +357,21 @@ performance as follows:
                  T_\textit{Max Comm Old}}{T_\textit{Max Comp Old} \cdot S +
                  T_\textit{Max Comm Old}}
 \end{equation}
-\begin{figure*}
+\begin{figure}
   \centering
   \subfloat[Converted relation.]{%
-    \includegraphics[width=.4\textwidth]{fig/file}\label{fig:r1}}%
-  \qquad%
+    \includegraphics[width=.24\textwidth]{fig/file}\label{fig:r1}}%
+%  \quad%
   \subfloat[Real relation.]{%
-    \includegraphics[width=.4\textwidth]{fig/file3}\label{fig:r2}}
+    \includegraphics[width=.24\textwidth]{fig/file3}\label{fig:r2}}
   \label{fig:rel}
   \caption{The energy and performance relation}
-\end{figure*}
-Then, we can modelize our objective function as finding the maximum distance
+\end{figure}
+Then, we can model our objective function as finding the maximum distance
 between the energy curve EQ~(\ref{eq:enorm}) and the inverse of performance
 curve EQ~(\ref{eq:pnorm_en}) over all available scaling factors.  This
 represents the minimum energy consumption with minimum execution time (better
-performance) at the same time, see figure~(\ref{fig:r1}).  Then our objective
+performance) at the same time, see Figure~(\ref{fig:r1}).  Then our objective
 function has the following form:
 \begin{equation}
   \label{eq:max}
@@ -480,35 +421,34 @@ objective function described above.
     \EndFor
     \State  Return $S_{opt}$
   \end{algorithmic}
-\end{algorithm}
+\end{algorithm}  
 
 The proposed algorithm works online during the execution time of the MPI
 program.  It selects the optimal scaling factor after gathering the computation
 and communication times from the program after one iteration.  Then the program
 changes the new frequencies of the CPUs according to the computed scaling
-factors.  This algorithm has a small execution time: for a homogeneous cluster
-composed of nodes having the characteristics presented in
-table~\ref{table:platform}, it takes \np[ms]{0.00152} on average for 4 nodes and
-\np[ms]{0.00665} on average for 32 nodes.  The algorithm complexity is $O(F\cdot
+factors. In our experiments over a  homogeneous cluster described in  section~\ref{sec.expe}, 
+this algorithm has a small execution time. It takes \np[$\mu$s]{1.52} on average for 4 nodes and
+\np[$\mu$s]{6.65} on average for 32 nodes.  The algorithm complexity is $O(F\cdot
 N)$, where $F$ is the number of available frequencies and $N$ is the number of
 computing nodes.  The algorithm is called just once during the execution of the
 program.  The DVFS algorithm~(\ref{dvfs}) shows where and when the algorithm is
 called in the MPI program.
-\begin{table}[htb]
-  \caption{Platform file parameters}
-  % title of Table
-  \centering
-  \begin{tabular}{|*{7}{l|}}
-    \hline
-    Max      & Min      & Backbone        & Backbone         & Link         & Link            & Sharing \\
-    Freq.    & Freq.    & Bandwidth       & Latency          & Bandwidth    & Latency         & Policy  \\
-    \hline
-    \np{2.5} & \np{800} & \np[GBps]{2.25} & \np[$\mu$s]{0.5} & \np[GBps]{1} & \np[$\mu$s]{50} & Full    \\
-    GHz      & MHz      &                 &                  &              &                 & Duplex  \\
-    \hline
-  \end{tabular}
-  \label{table:platform}
-\end{table}
+%\begin{table}[htb]
+%  \caption{Platform file parameters}
+%  % title of Table
+%  \centering
+%  \begin{tabular}{|*{7}{l|}}
+%    \hline
+%    Max      & Min      & Backbone        & Backbone         & Link         & Link            & Sharing \\
+%    Freq.    & Freq.    & Bandwidth       & Latency          & Bandwidth    & Latency         & Policy  \\
+%    \hline
+%    \np{2.5} & \np{800} & \np[GBps]{2.25} & \np[$\mu$s]{0.5} & \np[GBps]{1} & \np[$\mu$s]{50} & Full    \\
+%    GHz      & MHz      &                 &                  &              &                 & Duplex  \\
+%    \hline
+%  \end{tabular}
+%  \label{table:platform}
+%\end{table}
 
 \begin{algorithm}[tp]
   \caption{DVFS}
@@ -522,7 +462,7 @@ called in the MPI program.
                communication from each node.
         \State Call algorithm~\ref{EPSA} with these times.
         \State Compute the new frequency from the\newline\hspace*{3em}%
-               returned optimal scaling factor.
+              returned optimal scaling factor.
         \State Set the new frequency to the CPU.
       \EndIf
     \EndFor
@@ -539,55 +479,53 @@ frequency $F_i$ as follows:
 According to this equation all the nodes may have the same frequency value if
 they have balanced workloads, otherwise, they take different frequencies when
 having imbalanced workloads.  Thus, EQ~(\ref{eq:fi}) adapts the frequency of the
-CPU to the nodes' workloads to maintain performance.
+CPU to the nodes' workloads to maintain the performance of the program.
 
 \section{Experimental results}
 \label{sec.expe}
 Our experiments are executed on the simulator SimGrid/SMPI v3.10.  We configure
-the simulator to use a homogeneous cluster with one core per node.  The detailed
-characteristics of our platform file are shown in the
-table~(\ref{table:platform}).  Each node in the cluster has 18 frequency values
+the simulator to use a homogeneous cluster with one core per node.  
+%The detailed characteristics of our platform file are shown in Table~(\ref{table:platform}).  
+Each node in the cluster has 18 frequency values
 from \np[GHz]{2.5} to \np[MHz]{800} with \np[MHz]{100} difference between each
-two successive frequencies.  The simulated network link is \np[GB]{1} Ethernet
-(TCP/IP).  The backbone of the cluster simulates a high performance switch.
+two successive frequencies. The nodes are connected via an ethernet network with 1Gbit/s  bandwidth. 
 
 \subsection{Performance prediction verification}
 
 In this section we evaluate the precision of our performance prediction method
-based on EQ~(\ref{eq:tnew}) by applying it the NAS benchmarks.  The NAS programs
-are executed with the class B option for comparing the real execution time with
+based on EQ~(\ref{eq:tnew}) by applying it to the NAS benchmarks.  The NAS programs
+are executed with the class B option to compare the real execution time with
 the predicted execution time.  Each program runs offline with all available
 scaling factors on 8 or 9 nodes (depending on the benchmark) to produce real
 execution time values.  These scaling factors are computed by dividing the
 maximum frequency by the new one see EQ~(\ref{eq:s}).
-\begin{figure*}[t]
+\begin{figure}
   \centering
-  \includegraphics[width=.328\textwidth]{fig/cg_per}\hfill%
-  \includegraphics[width=.328\textwidth]{fig/mg_pre}\hfill%
+  \includegraphics[width=.24\textwidth]{fig/cg_per}\hfill%
+ % \includegraphics[width=.328\textwidth]{fig/mg_pre}\hfill%
  % \includegraphics[width=.4\textwidth]{fig/bt_pre}\qquad%
-  \includegraphics[width=.328\textwidth]{fig/lu_pre}\hfill%
-  \caption{Comparing predicted to real execution time}
+   \includegraphics[width=.24\textwidth]{fig/lu_pre}\hfill%
+  \caption{Comparing predicted to real execution times}
   \label{fig:pred}
-\end{figure*}
+\end{figure}
 %see Figure~\ref{fig:pred}
 In our cluster there are 18 available frequency states for each processor.  This
 leads to 18 run states for each program.  We use seven MPI programs of the NAS
 parallel benchmarks: CG, MG, EP, FT, BT, LU and SP.  Figure~(\ref{fig:pred})
-presents plots of the real execution times and the simulated ones.  The maximum
-normalized error between these two execution times varies between
-\np{0.0073}\AG[]{unit?} to \np{0.031} dependent on the executed benchmark.  The
-smallest prediction error was for CG and the worst one was for LU.
+presents plots of the real execution times and the simulated ones. The maximum
+normalized error between these two execution times varies between \np{0.0073} to
+\np{0.031} dependent on the executed benchmark.  The smallest prediction error
+was for CG and the worst one was for LU.
 
 \subsection{The experimental results for the scaling algorithm }
-
 The proposed algorithm was applied to seven MPI programs of the NAS benchmarks
 (EP, CG, MG, FT, BT, LU and SP) which were run with three classes (A, B and C).
 For each instance the benchmarks were executed on a number of processors
 proportional to the size of the class.  Each class represents the problem size
-ascending from the class A to C.  Additionally, depending on some speed up
+ascending from  class A to C.  Additionally, depending on some speed up
 points for each class we run the classes A, B and C on 4, 8 or 9 and 16 nodes
 respectively.  Depending on EQ~(\ref{eq:energy}), we measure the energy
-consumption for all the NAS MPI programs while assuming the power dynamic with
+consumption for all the NAS MPI programs while assuming that the dynamic power with
 the highest frequency is equal to \np[W]{20} and the power static is equal to
 \np[W]{4} for all experiments.  These power values were also used by Rauber and
 RÃ¼nger in~\cite{3}.  The results showed that the algorithm selected different
@@ -595,62 +533,36 @@ scaling factors for each program depending on the communication features of the
 program as in the plots~(\ref{fig:nas}).  These plots illustrate that there are
 different distances between the normalized energy and the normalized inverted
 performance curves, because there are different communication features for each
-benchmark.  When there are little or not communications, the inverted
+benchmark.  When there are little or no communications, the inverted
 performance curve is very close to the energy curve.  Then the distance between
 the two curves is very small.  This leads to small energy savings.  The opposite
 happens when there are a lot of communication, the distance between the two
 curves is big.  This leads to more energy savings (e.g. CG and FT), see
-table~(\ref{table:factors results}).  All discovered frequency scaling factors
+Table~(\ref{table:compareC}).  All discovered frequency scaling factors
 optimize both the energy and the performance simultaneously for all NAS
-benchmarks.  In table~(\ref{table:factors results}), we record all optimal
+benchmarks.  In Table~(\ref{table:compareC}), we record all optimal
 scaling factors results for each benchmark running class C.  These scaling
-factors give the maximum energy saving percent and the minimum performance
-degradation percent at the same time from all available scaling factors.
+factors give the maximum energy saving percentage and the minimum performance
+degradation percentage at the same time from all available scaling factors.
 \begin{figure*}[t]
   \centering
-  \includegraphics[width=.328\textwidth]{fig/ep}\hfill%
-  \includegraphics[width=.328\textwidth]{fig/cg}\hfill%
-  \includegraphics[width=.328\textwidth]{fig/sp}
-  \includegraphics[width=.328\textwidth]{fig/lu}\hfill%
-  \includegraphics[width=.328\textwidth]{fig/bt}\hfill%
-  \includegraphics[width=.328\textwidth]{fig/ft}
+  \includegraphics[width=.33\textwidth]{fig/ep}\hfill%
+  \includegraphics[width=.33\textwidth]{fig/cg}\hfill%
+ % \includegraphics[width=.328\textwidth]{fig/sp}
+ %  \includegraphics[width=.328\textwidth]{fig/lu}\hfill%
+  \includegraphics[width=.33\textwidth]{fig/bt}\hfill%
+ % \includegraphics[width=.328\textwidth]{fig/ft}
   \caption{Optimal scaling factors for the predicted energy and performance of NAS benchmarks}
   \label{fig:nas}
 \end{figure*}
-\begin{table}[htb]
-  \caption{The scaling factors results}
-  % title of Table
-  \centering
-  \begin{tabular}{|l|*{4}{r|}}
-    \hline
-    Program & Optimal        & Energy    & Performance    & Energy-Perf. \\
-    Name    & Scaling Factor & Saving \% & Degradation \% & Distance     \\
-    \hline
-    CG      & 1.56           & 39.23     & 14.88          & 24.35 \\
-    \hline
-    MG      & 1.47           & 34.97     & 21.70          & 13.27 \\
-    \hline
-    EP      & 1.04           & 22.14     & 20.73          &  1.41 \\
-    \hline
-    LU      & 1.38           & 35.83     & 22.49          & 13.34 \\
-    \hline
-    BT      & 1.31           & 29.60     & 21.28          &  8.32 \\
-    \hline
-    SP      & 1.38           & 33.48     & 21.36          & 12.12 \\
-    \hline
-    FT      & 1.47           & 34.72     & 19.00          & 15.72 \\
-    \hline
-  \end{tabular}
-  \label{table:factors results}
-  % is used to refer this table in the text
-\end{table}
-As shown in the table~(\ref{table:factors results}), when the optimal scaling
-factor has big value we can gain more energy savings for example as in CG and
-FT.  The opposite happens when the optimal scaling factor is small value as
-example BT and EP.  Our algorithm selects big scaling factor value when the
-communication and the other slacks times are big and smaller ones in opposite
-cases.  In EP there are no communications inside the iterations.  This make our
-algorithm to selects smaller scaling factor values (inducing smaller energy
+
+As shown in Table~(\ref{table:compareC}), when the optimal scaling
+factor has a big value we can gain more energy savings  as in CG and
+FT benchmarks.  The opposite happens when the optimal scaling factor has a  
+small value as in BT and EP benchmarks.  Our algorithm selects a big scaling 
+factor value when the communication and the other slacks times are big and smaller 
+ones in opposite cases.  In EP there are no communication inside the iterations.  
+This leads our algorithm to select smaller scaling factor values (inducing smaller energy
 savings).
 
 \subsection{Results comparison}
@@ -663,101 +575,17 @@ is similar to the first except setting the slower task to the maximum frequency
 (when the scale $S=1$) to keep the performance from degradation as mush as
 possible.  We refer to this scenario as $R_{E-P}$.  While we refer to our
 algorithm as EPSA (Energy to Performance Scaling Algorithm).  The comparison is
-made in tables \ref{table:compareA}, \ref{table:compareB},
-and~\ref{table:compareC}.  These tables show the results of our method and
-Rauber and RÃ¼nger scenarios for all the NAS benchmarks programs for classes A, B
-and C.
-\begin{table}[p]
-  \caption{Comparing results for  the NAS class A}
-  % title of Table
-  \centering
-  \begin{tabular}{|l|l|*{4}{r|}}
-    \hline
-    Method    & Program & Factor & Energy    & Performance    & Energy-Perf. \\
-    Name      & Name    & Value  & Saving \% & Degradation \% & Distance     \\
-    \hline
-    % \rowcolor[gray]{0.85}
-    $EPSA$    & CG      & 1.56   & 37.02     & 13.88          &  23.14 \\ \hline
-    $R_{E-P}$ & CG      & 2.14   & 42.77     & 25.27          &  17.50 \\ \hline
-    $R_{E}$   & CG      & 2.14   & 42.77     & 26.46          &  16.31 \\ \hline
-
-    $EPSA$    & MG      & 1.47   & 27.66     & 16.82          &  10.84 \\ \hline
-    $R_{E-P}$ & MG      & 2.14   & 34.45     & 31.84          &   2.61 \\ \hline
-    $R_{E}$   & MG      & 2.14   & 34.48     & 33.65          &   0.80 \\ \hline
-
-    $EPSA$    & EP      & 1.19   & 25.32     & 20.79          &   4.53 \\ \hline
-    $R_{E-P}$ & EP      & 2.05   & 41.45     & 55.67          & -14.22 \\ \hline
-    $R_{E}$   & EP      & 2.05   & 42.09     & 57.59          & -15.50 \\ \hline
-
-    $EPSA$    & LU      & 1.56   & 39.55     & 19.38          &  20.17 \\ \hline
-    $R_{E-P}$ & LU      & 2.14   & 45.62     & 27.00          &  18.62 \\ \hline
-    $R_{E}$   & LU      & 2.14   & 45.66     & 33.01          &  12.65 \\ \hline
-
-    $EPSA$    & BT      & 1.31   & 29.60     & 20.53          &   9.07 \\ \hline
-    $R_{E-P}$ & BT      & 2.10   & 45.53     & 49.63          &  -4.10 \\ \hline
-    $R_{E}$   & BT      & 2.10   & 43.93     & 52.86          &  -8.93 \\ \hline
-
-    $EPSA$    & SP      & 1.38   & 33.51     & 15.65          &  17.86 \\ \hline
-    $R_{E-P}$ & SP      & 2.11   & 45.62     & 42.52          &   3.10 \\ \hline
-    $R_{E}$   & SP      & 2.11   & 45.78     & 43.09          &   2.69 \\ \hline
-
-    $EPSA$    & FT      & 1.25   & 25.00     & 10.80          &  14.20 \\ \hline
-    $R_{E-P}$ & FT      & 2.10   & 39.29     & 34.30          &   4.99 \\ \hline
-    $R_{E}$   & FT      & 2.10   & 37.56     & 38.21          &  -0.65 \\ \hline
-  \end{tabular}
-  \label{table:compareA}
-  % is used to refer this table in the text
-\end{table}
-\begin{table}[p]
-  \caption{Comparing results for the NAS class B}
-  % title of Table
-  \centering
-  \begin{tabular}{|l|l|*{4}{r|}}
-    \hline
-    Method    & Program & Factor & Energy    & Performance    & Energy-Perf. \\
-    Name      & Name    & Value  & Saving \% & Degradation \% & Distance     \\
-    \hline
-    % \rowcolor[gray]{0.85}
-    $EPSA$    & CG      & 1.66   & 39.23     & 16.63          &  22.60 \\ \hline
-    $R_{E-P}$ & CG      & 2.15   & 45.34     & 27.60          &  17.74 \\ \hline
-    $R_{E}$   & CG      & 2.15   & 45.34     & 28.88          &  16.46 \\ \hline
-
-    $EPSA$    & MG      & 1.47   & 34.98     & 18.35          &  16.63 \\ \hline
-    $R_{E-P}$ & MG      & 2.14   & 43.55     & 36.42          &   7.13 \\ \hline
-    $R_{E}$   & MG      & 2.14   & 43.56     & 37.07          &   6.49 \\ \hline
-
-    $EPSA$    & EP      & 1.08   & 20.29     & 17.15          &   3.14 \\ \hline
-    $R_{E-P}$ & EP      & 2.00   & 42.38     & 56.88          & -14.50 \\ \hline
-    $R_{E}$   & EP      & 2.00   & 39.73     & 59.94          & -20.21 \\ \hline
+made in Table ~\ref{table:compareC}.  This table shows the results of our method and
+Rauber and RÃ¼nger scenarios for all the NAS benchmarks programs for class C.
 
-    $EPSA$    & LU      & 1.47   & 38.57     & 21.34          &  17.23 \\ \hline
-    $R_{E-P}$ & LU      & 2.10   & 43.62     & 36.51          &   7.11 \\ \hline
-    $R_{E}$   & LU      & 2.10   & 43.61     & 38.54          &   5.07 \\ \hline
-
-    $EPSA$    & BT      & 1.31   & 29.59     & 20.88          &   8.71 \\ \hline
-    $R_{E-P}$ & BT      & 2.10   & 44.53     & 53.05          &  -8.52 \\ \hline
-    $R_{E}$   & BT      & 2.10   & 42.93     & 52.80          &  -9.87 \\ \hline
-
-    $EPSA$    & SP      & 1.38   & 33.44     & 19.24          &  14.20 \\ \hline
-    $R_{E-P}$ & SP      & 2.15   & 45.69     & 43.20          &   2.49 \\ \hline
-    $R_{E}$   & SP      & 2.15   & 45.41     & 44.47          &   0.94 \\ \hline
-
-    $EPSA$    & FT      & 1.38   & 34.40     & 14.57          &  19.83 \\ \hline
-    $R_{E-P}$ & FT      & 2.13   & 42.98     & 37.35          &   5.63 \\ \hline
-    $R_{E}$   & FT      & 2.13   & 43.04     & 37.90          &   5.14 \\ \hline
-  \end{tabular}
-  \label{table:compareB}
-  % is used to refer this table in the text
-\end{table}
-
-\begin{table}[p]
+\begin{table}
   \caption{Comparing results for the NAS class C}
   % title of Table
   \centering
   \begin{tabular}{|l|l|*{4}{r|}}
     \hline
     Method    & Program & Factor & Energy    & Performance    & Energy-Perf. \\
-    Name      & Name    & Value  & Saving \% & Degradation \% & Distance     \\
+    Name      & Name    & Value  & Saving \% & Degradation  \% & Distance     \\
     \hline
     % \rowcolor[gray]{0.85}
     $EPSA$    & CG      & 1.56   & 39.23     & 14.88          &  24.35 \\ \hline
@@ -791,28 +619,28 @@ and C.
   \label{table:compareC}
   % is used to refer this table in the text
 \end{table}
-As shown in tables~\ref{table:compareA},~\ref{table:compareB}
-and~\ref{table:compareC}, the ($R_{E-P}$) method outperforms the ($R_{E}$)
+As shown in Table~\ref{table:compareC}, the ($R_{E-P}$) method outperforms the ($R_{E}$)
 method in terms of performance and energy reduction.  The ($R_{E-P}$) method
 also gives better energy savings than our method.  However, although our scaling
-factor is not optimal for energy reduction, the results in these tables prove
+factor is not optimal for energy reduction, the results in this table prove
 that our algorithm returns the best scaling factor that satisfy our objective
 method: the largest distance between energy reduction and performance
-degradation.  Negative values in the energy-performance column mean that one of
+degradation. Figure~\ref{fig:compare}  illustrates even better the distance between 
+the energy reduction and performance degradation. The negative values mean that one of
 the two objectives (energy or performance) have been degraded more than the
 other.  The positive trade-offs with the highest values lead to maximum energy
 savings while keeping the performance degradation as low as possible.  Our
 algorithm always gives the highest positive energy to performance trade-offs
-while Rauber and RÃ¼nger method ($R_{E-P}$) gives in some time negative
+while Rauber and RÃ¼nger's method, ($R_{E-P}$), gives sometimes negative
 trade-offs such as in BT and EP.
-%\begin{figure*}[t]
-%  \centering
+\begin{figure}[t]
+  \centering
 %  \includegraphics[width=.328\textwidth]{fig/compare_class_A}
 %  \includegraphics[width=.328\textwidth]{fig/compare_class_B}
-%  \includegraphics[width=.328\textwidth]{fig/compare_class_C}
-%  \caption{Comparing our method to Rauber and RÃ¼nger methods}
-%  \label{fig:compare}
-%\end{figure*}
+  \includegraphics[width=.49\textwidth]{fig/compare_class_C}
+  \caption{Comparing our method to Rauber and RÃ¼nger's methods}
+  \label{fig:compare}
+\end{figure}
 
 \section{Conclusion}
 \label{sec.concl}
@@ -827,8 +655,8 @@ between energy reduction and performance which is the maximum distance between
 the energy and the inverted performance curves.  To evaluate this method, we
 have applied it to the NAS benchmarks and it was compared to Rauber and RÃ¼nger
 methods while being executed on the simulator SimGrid.  The results showed that
-our method, outperforms Rauber and RÃ¼nger methods in terms of energy-performance
-ratio.
+our method, outperforms Rauber and RÃ¼nger's methods in terms of energy-performance
+ratio. 
 
 In the near future, we would like to adapt this scaling factor selection method
 to heterogeneous platforms where each node has different characteristics.  In
@@ -839,7 +667,7 @@ known in advance and depends on the global convergence of the iterative system.
 
 \section*{Acknowledgment}
 
-This work has been supported by the Labex ACTION project (contract
+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 MÃ©socentre de calcul de Franche-ComtÃ©.  As a PhD student,
 Mr. Ahmed Fanfakh, would like to thank the University of Babylon (Iraq) for
