X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy2.git/blobdiff_plain/d4aabc249b2f6bef8b9cd0dcf3b8a49fbd721bfd..08498d4d730017544934563b4b7ba98f160ae3a2:/mpi-energy2-extension/Heter_paper.tex?ds=sidebyside

diff --git a/mpi-energy2-extension/Heter_paper.tex b/mpi-energy2-extension/Heter_paper.tex
index 1caecff..caa294f 100644
--- a/mpi-energy2-extension/Heter_paper.tex
+++ b/mpi-energy2-extension/Heter_paper.tex
@@ -1,5 +1,5 @@
-\documentclass[conference]{IEEEtran}
-
+\documentclass[3p,times]{elsarticle-1}
+\usepackage{ecrc}
 \usepackage[T1]{fontenc}
 \usepackage[utf8]{inputenc}
 \usepackage[english]{babel}
@@ -60,35 +60,140 @@
 \newcommand{\Tnew}{\Xsub{T}{New}}
 \newcommand{\Told}{\Xsub{T}{Old}}
 
+
+%% The ecrc package defines commands needed for running heads and logos.
+%% For running heads, you can set the journal name, the volume, the starting page and the authors
+
+%% set the volume if you know. Otherwise `00'
+\volume{00}
+
+%% set the starting page if not 1
+\firstpage{1}
+
+%% Give the name of the journal
+\journalname{Procedia Computer Science}
+
+%% Give the author list to appear in the running head
+%% Example \runauth{C.V. Radhakrishnan et al.}
+\runauth{}
+
+%% The choice of journal logo is determined by the \jid and \jnltitlelogo commands.
+%% A user-supplied logo with the name <\jid>logo.pdf will be inserted if present.
+%% e.g. if \jid{yspmi} the system will look for a file yspmilogo.pdf
+%% Otherwise the content of \jnltitlelogo will be set between horizontal lines as a default logo
+
+%% Give the abbreviation of the Journal.
+\jid{procs}
+
+%% Give a short journal name for the dummy logo (if needed)
+\jnltitlelogo{Procedia Computer Science}
+
+%% Hereafter the template follows `elsarticle'.
+%% For more details see the existing template files elsarticle-template-harv.tex and elsarticle-template-num.tex.
+
+%% Elsevier CRC generally uses a numbered reference style
+%% For this, the conventions of elsarticle-template-num.tex should be followed (included below)
+%% If using BibTeX, use the style file elsarticle-num.bst
+
+%% End of ecrc-specific commands
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%% The amssymb package provides various useful mathematical symbols
+\usepackage{amssymb}
+%% The amsthm package provides extended theorem environments
+%% \usepackage{amsthm}
+
+%% The lineno packages adds line numbers. Start line numbering with
+%% \begin{linenumbers}, end it with \end{linenumbers}. Or switch it on
+%% for the whole article with \linenumbers after \end{frontmatter}.
+%% \usepackage{lineno}
+
+%% natbib.sty is loaded by default. However, natbib options can be
+%% provided with \biboptions{...} command. Following options are
+%% valid:
+
+%%   round  -  round parentheses are used (default)
+%%   square -  square brackets are used   [option]
+%%   curly  -  curly braces are used      {option}
+%%   angle  -  angle brackets are used    <option>
+%%   semicolon  -  multiple citations separated by semi-colon
+%%   colon  - same as semicolon, an earlier confusion
+%%   comma  -  separated by comma
+%%   numbers-  selects numerical citations
+%%   super  -  numerical citations as superscripts
+%%   sort   -  sorts multiple citations according to order in ref. list
+%%   sort&compress   -  like sort, but also compresses numerical citations
+%%   compress - compresses without sorting
+%%
+%% \biboptions{comma,round}
+
+% \biboptions{}
+
+% if you have landscape tables
+\usepackage[figuresright]{rotating}
+
+% put your own definitions here:
+%   \newcommand{\cZ}{\cal{Z}}
+%   \newtheorem{def}{Definition}[section]
+%   ...
+
+% add words to TeX's hyphenation exception list
+%\hyphenation{author another created financial paper re-commend-ed Post-Script}
+
+% declarations for front matter
+
 \begin{document}
 
-\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
-  }
-  \IEEEauthorblockA{%
-    FEMTO-ST Institute, University of Franche-Comté\\
+\begin{frontmatter}
+
+%% Title, authors and addresses
+
+%% use the tnoteref command within \title for footnotes;
+%% use the tnotetext command for the associated footnote;
+%% use the fnref command within \author or \address for footnotes;
+%% use the fntext command for the associated footnote;
+%% use the corref command within \author for corresponding author footnotes;
+%% use the cortext command for the associated footnote;
+%% use the ead command for the email address,
+%% and the form \ead[url] for the home page:
+%%
+%% \title{Title\tnoteref{label1}}
+%% \tnotetext[label1]{}
+%% \author{Name\corref{cor1}\fnref{label2}}
+%% \ead{email address}
+%% \ead[url]{home page}
+%% \fntext[label2]{}
+%% \cortext[cor1]{}
+%% \address{Address\fnref{label3}}
+%% \fntext[label3]{}
+
+\dochead{}
+%% Use \dochead if there is an article header, e.g. \dochead{Short communication}
+\title{Energy Consumption Reduction with DVFS for Message Passing \\
+       Iterative Applications on Grid Architecture} 
+  
+
+%% use optional labels to link authors explicitly to addresses:
+%% \author[label1,label2]{<author name>}
+%% \address[label1]{<address>}
+%% \address[label2]{<address>}
+
+\author{Ahmed Fanfakh,
+        Jean-Claude Charr,
+        Raphaël Couturier,
+        and Arnaud Giersch}
+
+\address{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
     % Fax: \mbox{+33 3 84 58 77 81}\\      % Dept Info
-    Email: \email{{jean-claude.charr,raphael.couturier,ahmed.fanfakh_badri_muslim,arnaud.giersch}@univ-fcomte.fr}
+    Email: \email{{ahmed.fanfakh_badri_muslim,jean-claude.charr,raphael.couturier,arnaud.giersch}@univ-fcomte.fr}
    }
-  }
-
-\maketitle
-
 
 \begin{abstract}
 
-  In recent years, green computing topic  has  become an important topic 
+  In recent years, green computing   has  become an important topic 
   in the supercomputing research domain. However, the 
   computing platforms are still  consuming more and
 more energy due to the increasing number of nodes composing
@@ -106,18 +211,32 @@ scaling (DVFS) is one of them. It can be used to reduce the power consumption of
   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  grid. 
-  The proposed algorithm is evaluated on a real grid , the grid'5000 platform, while
+  The proposed algorithm is evaluated on a real grid, the grid'5000 platform, while
   running the NAS parallel benchmarks.  The experiments show that it reduces the
   energy consumption on average by \np[\%]{30} while  the performance  is only degraded
-  on average by \np[\%]{3}. Finally, the algorithm is 
+  on average by \np[\%]{3.2}. Finally, the algorithm is 
   compared to an existing method. The comparison results show that it outperforms the
   latter in terms of energy consumption reduction and performance.
 \end{abstract}
 
 
+\begin{keyword}
+
+Dynamic voltage and frequency scaling \sep Grid computing\sep Green computing and  frequency scaling online algorithm.
+
+%% keywords here, in the form: keyword \sep keyword
+
+%% MSC codes here, in the form: \MSC code \sep code
+%% or \MSC[2008] code \sep code (2000 is the default)
+
+\end{keyword}
+
+\end{frontmatter}
+
+
+
 \section{Introduction}
 \label{sec.intro}
-\textcolor{red}{did you verify that these informations are still accurate before changing the years to 2015?}
 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 may achieve higher floating
@@ -392,11 +511,11 @@ follows:
 \end{equation}
 Replacing $\Fnew$ in (\ref{eq:pd}) as in (\ref{eq:fnew}) gives the following
 equation for dynamic power consumption:
-\begin{multline}
+\begin{equation}
   \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}
+   \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{equation}
 where $\PdNew$  and $\PdOld$ are the  dynamic power consumed with the
 new frequency and the maximum frequency respectively.
 
@@ -442,13 +561,13 @@ 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}
+\begin{equation}
   \label{eq:energy}
  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 {} \\
+ \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}
+\end{equation}
 
 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
@@ -540,13 +659,12 @@ equation, as follows:
   \Pnorm = \frac{\Told}{\Tnew}          
 \end{equation}
 
-\begin{figure}[!t]
+\begin{figure}
   \centering
   \subfloat[Homogeneous cluster]{%
-    \includegraphics[width=.33\textwidth]{fig/homo}\label{fig:r1}}%
-
+    \includegraphics[width=.4\textwidth]{fig/homo}\label{fig:r1}} \hspace{2cm}%
   \subfloat[Heterogeneous grid]{%
-    \includegraphics[width=.33\textwidth]{fig/heter}\label{fig:r2}}
+    \includegraphics[width=.4\textwidth]{fig/heter}\label{fig:r2}}
   \label{fig:rel}
   \caption{The energy and performance relation}
 \end{figure}
@@ -642,7 +760,8 @@ in~\cite{Zhuo_Energy.efficient.Dynamic.Task.Scheduling,Rauber_Analytical.Modelin
 \end{algorithm}
 
 
-In this section, the scaling factors selection algorithm for  grids, algorithm~\ref{HSA}, is presented. It selects the vector of the frequency
+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
 synchronous iterative application executed on a  grid. It works
@@ -658,7 +777,7 @@ scaling algorithm is called in the iterative MPI program.
 
 \begin{figure}[!t]
   \centering
-  \includegraphics[scale=0.45]{fig/init_freq}
+  \includegraphics[scale=0.6]{fig/init_freq}
   \caption{Selecting the initial frequencies}
   \label{fig:st_freq}
 \end{figure}
@@ -780,22 +899,18 @@ selected clusters and are presented in table  \ref{table:grid5000}.
   \caption{The selected two sites of grid'5000}
   \label{fig:grid5000}
 \end{figure}
-
-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. 
-
-
-
-
 \begin{figure}[!t]
   \centering
   \includegraphics[scale=0.6]{fig/power_consumption.pdf}
-  \caption{The power consumption by one core from Taurus cluster}
+  \caption{The power consumption by one core from the Taurus cluster}
   \label{fig:power_cons}
 \end{figure}
 
 
+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. 
+
 
   
 \begin{table}[!t]
@@ -876,29 +991,16 @@ Table \ref{tab:sc} shows the number of nodes used from each cluster for each sce
                                       & Griffon         & Nancy        & 6                      \\ 
 \hline
 \multirow{3}{*}{One site / 32 nodes}  & Graphite   & Nancy             & 4                      \\ \cline{2-4} 
-                                      & Graphene      & Nancy          & 12                     \\ \cline{2-4} 
-                                      & Griffon          & Nancy       & 12                       \\ 
+                                      & Graphene      & Nancy          & 14                     \\ \cline{2-4} 
+                                      & Griffon          & Nancy       & 14                       \\ 
 \hline
 \end{tabular}
  \label{tab:sc}
 \end{table}
 
-\begin{figure}
-  \centering
-  \includegraphics[scale=0.5]{fig/eng_con_scenarios.eps}
-  \caption{The energy consumptions of NAS benchmarks over different scenarios }
-  \label{fig:eng_sen}
-\end{figure}
 
 
 
-\begin{figure}
-  \centering
-  \includegraphics[scale=0.5]{fig/time_scenarios.eps}
-  \caption{The execution times of NAS benchmarks over different scenarios }
-  \label{fig:time_sen}
-\end{figure}
-
 The NAS parallel benchmarks are executed over these two platforms
  with different number of nodes, as in Table \ref{tab:sc}. 
 The overall energy consumption of all the benchmarks solving the class D instance and
@@ -922,28 +1024,7 @@ scenario. Moreover, most of the benchmarks running over the one site scenario th
 However, the  execution times and the energy consumptions of EP and MG benchmarks, which have no or small communications, are not significantly affected 
  in both scenarios. Even when the number of nodes is doubled. On the other hand, the communications of the rest of the benchmarks increases when using long distance communications between two sites or increasing the number of computing nodes.
 
-\begin{figure}
-  \centering
-  \includegraphics[scale=0.5]{fig/eng_s.eps}
-  \caption{The energy saving of NAS benchmarks over different scenarios }
-  \label{fig:eng_s}
-\end{figure}
-
-
-\begin{figure}
-  \centering
-  \includegraphics[scale=0.5]{fig/per_d.eps}
-  \caption{The performance degradation of NAS benchmarks over different scenarios }
-  \label{fig:per_d}
-\end{figure}
-
 
-\begin{figure}
-  \centering
-  \includegraphics[scale=0.5]{fig/dist.eps}
-  \caption{The tradeoff distance of NAS benchmarks over different scenarios }
-  \label{fig:dist}
-\end{figure}
 
 The energy saving percentage is computed as the ratio between the reduced 
 energy consumption, equation (\ref{eq:energy}), and the original energy consumption,
@@ -957,8 +1038,31 @@ is exponentially related to the CPU's frequency value. On the other side, the in
 increase the communication times and thus produces less energy saving depending on the 
 benchmarks being executed. The results of the benchmarks CG, MG, BT and FT show more 
 energy saving percentage in one site scenario when executed over 16 nodes comparing to 32 nodes. While, LU and SP consume more energy with 16 nodes than 32 in one site  because their computations to communications ratio is not affected by the increase of the number of local communications. 
+\begin{figure}
+  \centering
+  \subfloat[The energy consumption by the nodes wile executing the NAS benchmarks over different scenarios    
+           ]{%
+    \includegraphics[width=.4\textwidth]{fig/eng_con_scenarios.eps}\label{fig:eng_sen}} \hspace{1cm}%
+  \subfloat[The execution times of the NAS benchmarks over different scenarios]{%
+    \includegraphics[width=.4\textwidth]{fig/time_scenarios.eps}\label{fig:time_sen}}
+  \label{fig:exp-time-energy}
+  \caption{The  energy consumption and execution time of NAS  Benchmarks over different scenarios}
+\end{figure}
 
 
+\begin{figure}
+  \centering
+  \subfloat[The energy reduction while executing the NAS benchmarks over different scenarios ]{%
+    \includegraphics[width=.4\textwidth]{fig/eng_s.eps}\label{fig:eng_s}} \hspace{2cm}%
+  \subfloat[The performance degradation of the NAS benchmarks over different scenarios]{%
+    \includegraphics[width=.4\textwidth]{fig/per_d.eps}\label{fig:per_d}}\hspace{2cm}%
+    \subfloat[The tradeoff distance between the energy reduction and the performance of the NAS benchmarks  
+      over different scenarios]{%
+    \includegraphics[width=.4\textwidth]{fig/dist.eps}\label{fig:dist}}
+  \label{fig:exp-res}
+  \caption{The experimental results of different scenarios}
+\end{figure}
+
 The energy saving percentage is reduced for all the benchmarks because of the long distance communications in the two sites 
 scenario, except for the   EP benchmark which has  no communications. Therefore, the energy saving percentage of this benchmark is 
 dependent on the maximum difference between the computing powers of the heterogeneous computing nodes, for example 
@@ -975,9 +1079,9 @@ The best energy saving percentage was obtained in the one site scenario with 16
 
 Figure \ref{fig:per_d} presents the performance degradation percentages for all benchmarks over the two scenarios.
 The performance degradation percentage for the benchmarks running on two sites  with
-16 or 32  nodes is on average equal to 8\% or 4\% respectively. 
+16 or 32  nodes is on average equal to 8.3\% or 4.7\% respectively. 
 For this scenario, the proposed scaling algorithm selects smaller frequencies for the executions with 32 nodes  without significantly degrading their performance because the communication times are higher with 32 nodes which results in smaller  computations to communications ratio.  On the other hand, the performance degradation percentage  for the benchmarks running  on one site  with
-16 or 32  nodes is on average equal to 3\% or 10\% respectively. In opposition to the two sites scenario, when the number of computing nodes is increased in the one site scenario, the performance degradation percentage is increased. Therefore, doubling the number of computing 
+16 or 32  nodes is on average equal to 3.2\% or 10.6\% respectively. In opposition to the two sites scenario, when the number of computing nodes is increased in the one site scenario, the performance degradation percentage is increased. Therefore, doubling the number of computing 
 nodes when the communications occur in high speed network does not decrease the computations to 
 communication ratio. 
 
@@ -989,150 +1093,103 @@ when the communication times increase and vice versa.
 
 Figure \ref{fig:dist} presents the  distance percentage between the energy saving  and the performance degradation for each benchmark  over both  scenarios. The tradeoff distance percentage can be 
 computed as in equation \ref{eq:max}. The one site scenario with 16 nodes gives the best energy and performance 
-tradeoff, on average it is equal to  26\%. The one site scenario using both 16 and 32 nodes had better energy and performance 
+tradeoff, on average it is equal to  26.8\%. The one site scenario using both 16 and 32 nodes had better energy and performance 
 tradeoff comparing to the two sites scenario  because the former has high speed local communications 
 which increase the computations to communications ratio  and the latter uses long distance communications which decrease this ratio. 
 
-
  Finally, the best energy and performance tradeoff depends on all of the following:
 1) the computations to communications ratio when there are  communications and slack times, 2) the heterogeneity of the computing powers of the nodes and 3) the heterogeneity of the consumed  static and dynamic powers of the nodes.
 
 
 
 
-%\subsection{The experimental results of multi-cores clusters}
-%\label{sec.res-mc}
-%The  clusters of grid'5000 have different number of cores embedded in their nodes
-%as shown in Table \ref{table:grid5000}. In 
-%this section, the proposed scaling algorithm is evaluated over the grid'5000 grid while using multi-core nodes 
-%selected according to the two  platform scenarios described in the section \ref{sec.res}.
-%The two platform scenarios, the two sites and one site scenarios, use  32 
-%cores from multi-cores nodes instead of 32 distinct nodes. For example if 
-%the participating number of cores from a certain cluster is equal to 12, 
-%in the multi-core scenario the selected nodes is equal to 3 nodes while using 
-%4 cores from each node. The platforms with one  
-%core per node and  multi-cores nodes are  shown in Table \ref{table:sen-mc}. 
-%The energy consumptions and execution times of running the NAS parallel 
-%benchmarks, class D, over these four different scenarios are presented 
-%in the figures \ref{fig:eng-cons-mc} and \ref{fig:time-mc} respectively.
-%
-%The execution times for most of  the NAS  benchmarks are higher over the one site multi-cores per node scenario 
-% than the execution time of those running over one site single core per node  scenario. Indeed,  
-%   the communication times  are higher in the one site multi-cores scenario than in the latter scenario because all the cores of a node  share  the same node network link which can be  saturated when running communication bound applications and. Moreover, the cores of a node share the memory bus which can be also saturated and become a bottleneck. 
-%   
-%
-%The experiments showed that for most of the NAS benchmarks and between the four scenarios,  
-%the one site one core scenario gives the best execution times because the communication times are the lowest. 
-%Indeed, in this scenario each core has a dedicated network link and memory bus and all the communications are local.  
-%Moreover, the energy consumptions of the NAS benchmarks are lower over the 
-%one site one core scenario  than over the one site multi-cores scenario because 
-%the first scenario had less execution time than the latter which results in less static energy being consumed.
-%
-%The computations to communications ratios of the NAS benchmarks are higher over 
-%the one site one core scenario  when compared to the ratios of the other scenarios. 
-%More energy reduction was achieved when this ratio is increased because the proposed scaling algorithm selects smaller frequencies that decrease the dynamic power consumption. 
-%
-%  \textcolor{blue}{ Whereas, the energy consumption in the two sites one core scenario is higher than the energy consumption of the two sites multi-core scenario. This is according to the increase in the execution time of the two sites one core scenario. }
-%
-%
-%These experiments also showed that the energy 
-%consumption and the execution times of the EP and MG benchmarks do not change significantly over these four 
-%scenarios  because there are no or small communications,  
-%which could increase or decrease the static power consumptions. Contrary to EP and MG, the  energy consumptions 
-%and the execution times of the rest of the  benchmarks  vary according to the  communication times that are different from one scenario to the other.
-%
-%
-%The energy saving percentages of all NAS benchmarks running over these four scenarios are presented in the figure \ref{fig:eng-s-mc}. It shows that  the energy saving percentages   over the two sites multi-cores scenario 
-%and over the two sites one core scenario are on average  equal to 22\% and 18\%
-%respectively. The energy saving percentages   are higher in the former scenario because  its computations to communications  ratio is higher than the ratio of the latter scenario  as mentioned previously.
-%
-%In contrast, in the one site one 
-%core and one site multi-cores scenarios the energy saving percentages 
-%are approximately equivalent, on average they are up to 25\%. In both scenarios there 
-%are a small difference  in the computations to communications ratios, which leads 
-%the proposed scaling algorithm to select similar frequencies for both scenarios.  
-%
-%The performance degradation percentages of the NAS benchmarks are presented in
-%figure \ref{fig:per-d-mc}. It shows that the performance degradation percentages for the NAS benchmarks are higher over the two sites 
-%multi-cores scenario than over the  two sites  one core scenario, equal on average to 7\% and 4\% respectively. 
-%Moreover, using the two sites multi-cores scenario increased 
-%the computations to communications ratio, which may increase 
-%the overall execution time  when the proposed scaling algorithm is applied and the frequencies scaled down.  
-%
-%
-%When the benchmarks are executed  over the one 
-%site one core scenario, their performance degradation percentages are equal  on average
-%to 10\% and are higher than those executed over the one site multi-cores scenario, 
-%which on average is equal to 7\%. 
-%
-%\textcolor{blue}{
-%The performance degradation percentages over one site multi-cores is lower because  the computations to communications ratio is decreased. Therefore, selecting bigger 
-%frequencies by the scaling algorithm are proportional to this ratio, and thus the execution time do not increase significantly.}
-%
-%
-%The tradeoff distance percentages of the NAS 
-%benchmarks over all scenarios are presented in the figure \ref{fig:dist-mc}.
-%These  tradeoff distance percentages are used to verify which scenario is the best in terms of energy reduction and performance. The figure shows that using muti-cores in both of the one site and two sites scenarios gives bigger  tradeoff distance percentages, on overage equal to 17.6\% and 15.3\% respectively, than using one core per node in both of one site and two sites scenarios,  on average  equal to 14.7\% and 13.3\% respectively. 
-%
-%\begin{table}[]
-%\centering
-%\caption{The multicores scenarios}
-%
-%\begin{tabular}{|*{4}{c|}}
-%\hline
-%Scenario name                          & Cluster name & \begin{tabular}[c]{@{}c@{}}No. of  nodes\\ in each cluster\end{tabular} & 
-%                                       \begin{tabular}[c]{@{}c@{}}No. of  cores\\ for each node\end{tabular}  \\ \hline
-%\multirow{3}{*}{Two sites/ one core}   & Taurus       & 10              & 1                   \\ \cline{2-4}
-%                                       & Graphene     & 10              & 1                   \\ \cline{2-4}
-%                                       & Griffon      & 12              & 1                   \\ \hline
-%\multirow{3}{*}{Two sites/ multicores} & Taurus       & 3               & 3 or 4              \\ \cline{2-4}
-%                                       & Graphene     & 3               & 3 or 4              \\  \cline{2-4}
-%                                       & Griffon      & 3               & 4                   \\ \hline
-%\multirow{3}{*}{One site/ one core}    & Graphite     & 4               & 1                   \\  \cline{2-4}
-%                                       & Graphene     & 12              & 1                   \\  \cline{2-4}
-%                                       & Griffon      & 12              & 1                   \\ \hline
-%\multirow{3}{*}{One site/ multicores}  & Graphite     & 3               & 3 or 4              \\  \cline{2-4}
-%                                       & Graphene     & 3               & 3 or 4              \\  \cline{2-4}
-%                                       & Griffon      & 3               & 4                   \\ \hline
-%\end{tabular}
-%\label{table:sen-mc}
-%\end{table}
-%
-%\begin{figure}
-%  \centering
-%  \includegraphics[scale=0.5]{fig/eng_con.eps}
-%  \caption{Comparing the  energy consumptions of running NAS benchmarks over one core and multicores scenarios }
-%  \label{fig:eng-cons-mc}
-%\end{figure}
-%
-%
-%  \begin{figure}
-%  \centering
-%  \includegraphics[scale=0.5]{fig/time.eps}
-%  \caption{Comparing the  execution times of running NAS benchmarks over one core and multicores scenarios }
-%  \label{fig:time-mc}
-%\end{figure}
-%
-% \begin{figure}
-%  \centering
-%  \includegraphics[scale=0.5]{fig/eng_s_mc.eps}
-%  \caption{The energy saving of running NAS benchmarks over one core and multicores scenarios }
-%  \label{fig:eng-s-mc}
-%\end{figure}
-%
-%\begin{figure}
-%  \centering
-%  \includegraphics[scale=0.5]{fig/per_d_mc.eps}
-%  \caption{The performance degradation of running NAS benchmarks over one core and multicores scenarios }
-%  \label{fig:per-d-mc}
-%\end{figure}
-%
-%\begin{figure}
-%  \centering
-%  \includegraphics[scale=0.5]{fig/dist_mc.eps}
-%  \caption{The tradeoff distance of running NAS benchmarks over one core and multicores scenarios }
-%  \label{fig:dist-mc}
-%\end{figure}
+\subsection{The experimental results over multi-cores clusters}
+\label{sec.res-mc}
+
+The  clusters of grid'5000 have different number of cores embedded in their nodes
+as shown in Table \ref{table:grid5000}. In 
+this section, the proposed scaling algorithm is evaluated over the  grid'5000 platform  while using multi-cores nodes selected according to the one site scenario described in the section \ref{sec.res}.
+The one site scenario uses  32 cores from multi-cores nodes instead of 32 distinct nodes. For example if 
+the participating number of cores from a certain cluster is equal to 14, 
+in the multi-core scenario the selected nodes is equal to  4 nodes while using 
+3 or 4 cores from each node. The platforms with one  
+core per node and  multi-cores nodes are  shown in Table \ref{table:sen-mc}. 
+The energy consumptions and execution times of running the class D of the NAS parallel 
+benchmarks over these four different scenarios are presented 
+in the figures \ref{fig:eng-cons-mc} and \ref{fig:time-mc} respectively.
+
+\begin{table}[]
+\centering
+\caption{The multicores scenarios}
+\begin{tabular}{|*{4}{c|}}
+\hline
+Scenario name                          & Cluster name & \begin{tabular}[c]{@{}c@{}}No. of  nodes\\ in each cluster\end{tabular} & 
+                                       \begin{tabular}[c]{@{}c@{}}No. of  cores\\ for each node\end{tabular}  \\ \hline
+\multirow{3}{*}{One core per node}    & Graphite     & 4               & 1                   \\  \cline{2-4}
+                                       & Graphene     & 14              & 1                   \\  \cline{2-4}
+                                       & Griffon      & 14              & 1                   \\ \hline
+\multirow{3}{*}{Multi-cores per node}  & Graphite     & 1               &  4              \\  \cline{2-4}
+                                       & Graphene     & 4               & 3 or 4              \\  \cline{2-4}
+                                       & Griffon      & 4               & 3 or 4                   \\ \hline
+\end{tabular}
+\label{table:sen-mc}
+\end{table}
+
+
+\begin{figure}
+  \centering
+  \subfloat[Comparing the  execution times of running NAS benchmarks over one core and multicores scenarios]{%
+    \includegraphics[width=.4\textwidth]{fig/time.eps}\label{fig:time-mc}} \hspace{1cm}%
+  \subfloat[Comparing the  energy consumptions of running NAS benchmarks over one core and multi-cores scenarios]{%
+    \includegraphics[width=.4\textwidth]{fig/eng_con.eps}\label{fig:eng-cons-mc}}
+    \label{fig:eng-cons}
+  \caption{The energy consumptions and execution times of NAS benchmarks over one core and multi-cores per node architectures}
+\end{figure}
+
+
+
+The execution times for most of  the NAS  benchmarks are higher over the multi-cores per node scenario 
+than over single core per node  scenario. Indeed,  
+ the communication times  are higher in the one site multi-cores scenario than in the latter scenario because all the cores of a node  share  the same node network link which can be  saturated when running communication bound applications. Moreover, the cores of a node share the memory bus which can be also saturated and become a bottleneck.    
+Moreover, the energy consumptions of the NAS benchmarks are lower over the 
+ one core scenario  than over the multi-cores scenario because 
+the first scenario had less execution time than the latter which results in less static energy being consumed.
+The computations to communications ratios of the NAS benchmarks are higher over 
+the one site one core scenario  when compared to the ratio of the multi-cores scenario. 
+More energy reduction can be gained when this ratio is big because it pushes the proposed scaling algorithm to select smaller frequencies that decrease the dynamic power consumption. These experiments also showed that the energy 
+consumption and the execution times of the EP and MG benchmarks do not change significantly over these two
+scenarios  because there are no or small communications. Contrary to EP and MG, the  energy consumptions and the execution times of the rest of the  benchmarks  vary according to the  communication times that are different from one scenario to the other.
+  
+  
+The energy saving percentages of all NAS benchmarks running over these two scenarios are presented in the figure \ref{fig:eng-s-mc}. 
+The figure shows that  the energy saving percentages in the one 
+core and the multi-cores scenarios
+are approximately equivalent, on average they are equal to  25.9\% and 25.1\% respectively.
+The energy consumption is reduced at the same rate in the two scenarios when compared to the energy consumption of the executions without DVFS. 
+
+
+The performance degradation percentages of the NAS benchmarks are presented in
+figure \ref{fig:per-d-mc}. It shows that the performance degradation percentages is higher for the NAS benchmarks over the  one core per node scenario  (on average equal to 10.6\%)  than over the  multi-cores scenario (on average equal to 7.5\%). The performance degradation percentages over the multi-cores scenario is lower because  the computations to communications ratio is smaller than the ratio of the other scenario. 
+
+The tradeoff distance percentages of the NAS benchmarks over the two scenarios are presented 
+in the figure \ref{fig:dist-mc}. These  tradeoff distance between energy consumption reduction and performance  are used to verify which scenario is the best in both terms  at the same time. The figure shows that  the  tradeoff distance percentages are on average   bigger over the multi-cores scenario  (17.6\%) than over the  one core per node scenario  (15.3\%).
+
+
+
+\begin{figure}
+  \centering
+    \subfloat[The energy saving of running NAS benchmarks over one core and multicores scenarios]{%
+    \includegraphics[width=.4\textwidth]{fig/eng_s_mc.eps}\label{fig:eng-s-mc}} \hspace{2cm}%
+    \subfloat[The performance degradation of running NAS benchmarks over one core and multicores scenarios
+      ]{%
+    \includegraphics[width=.4\textwidth]{fig/per_d_mc.eps}\label{fig:per-d-mc}}\hspace{2cm}%
+    \subfloat[The tradeoff distance of running NAS benchmarks over one core and multicores scenarios]{%
+    \includegraphics[width=.4\textwidth]{fig/dist_mc.eps}\label{fig:dist-mc}}
+  \label{fig:exp-res}
+  \caption{The experimental results of one core and multi-cores scenarios}
+\end{figure}
+
+
 
 \subsection{Experiments with different static and dynamic powers consumption scenarios}
 \label{sec.pow_sen}
@@ -1144,32 +1201,26 @@ In addition to the previously used  percentage of static power, two new static p
 The experiments have been executed with these two new static power scenarios  over the one site one core per node scenario.
 In these experiments, the class D of the NAS parallel benchmarks are executed over Nancy's site. 16 computing nodes from the three clusters, Graphite, Graphene and Griffon, where used in this experiment. 
 
- \begin{figure}
-  \centering
-  \includegraphics[scale=0.5]{fig/eng_pow.eps}
-  \caption{The energy saving percentages for NAS benchmarks of the three power scenario}
-  \label{fig:eng-pow}
-\end{figure}
 
 \begin{figure}
   \centering
-  \includegraphics[scale=0.5]{fig/per_pow.eps}
-  \caption{The performance degradation percentages for NAS benchmarks of the three power scenario}
-  \label{fig:per-pow}
+  \subfloat[The energy saving percentages for the nodes executing the NAS benchmarks over the three power scenarios]{%
+    \includegraphics[width=.4\textwidth]{fig/eng_pow.eps}\label{fig:eng-pow}} \hspace{2cm}%
+  \subfloat[The performance degradation percentages for the NAS benchmarks over the three power scenarios]{%
+    \includegraphics[width=.4\textwidth]{fig/per_pow.eps}\label{fig:per-pow}}\hspace{2cm}%
+    \subfloat[The tradeoff distance between the energy reduction and the performance of the NAS benchmarks over the three power scenarios]{%
+      
+    \includegraphics[width=.4\textwidth]{fig/dist_pow.eps}\label{fig:dist-pow}}
+  \label{fig:exp-pow}
+  \caption{The experimental results of different static power scenarios}
 \end{figure}
 
 
-\begin{figure}
-  \centering
-  \includegraphics[scale=0.5]{fig/dist_pow.eps}
-  \caption{The tradeoff distance for NAS benchmarks of the three power scenario}
-  \label{fig:dist-pow}
-\end{figure}
 
 \begin{figure}
   \centering
-  \includegraphics[scale=0.47]{fig/three_scenarios.pdf}
-  \caption{Comparing the selected frequency scaling factors of MG benchmark for three static power scenarios}
+  \includegraphics[scale=0.5]{fig/three_scenarios.pdf}
+  \caption{Comparing the selected frequency scaling factors for the MG benchmark over the three static power scenarios}
   \label{fig:fre-pow}
 \end{figure}
 
@@ -1218,24 +1269,15 @@ presented in the figures \ref{fig:edp-eng}, \ref{fig:edp-perf} and \ref{fig:edp-
 
 \begin{figure}
   \centering
-  \includegraphics[scale=0.5]{fig/edp_eng}
-  \caption{Comparing of the energy saving for the proposed method with EDP method}
-  \label{fig:edp-eng}
+  \subfloat[The energy reduction induced by the Maxdist method and the EDP method]{%
+    \includegraphics[width=.4\textwidth]{fig/edp_eng}\label{fig:edp-eng}} \hspace{2cm}%
+    \subfloat[The performance degradation induced by  the Maxdist method and the EDP method]{%
+    \includegraphics[width=.4\textwidth]{fig/edp_per}\label{fig:edp-perf}}\hspace{2cm}%
+    \subfloat[The tradeoff distance between the energy consumption reduction and the performance for the Maxdist method and the  EDP method]{%
+    \includegraphics[width=.4\textwidth]{fig/edp_dist}\label{fig:edp-dist}}
+  \label{fig:edp-comparison}
+  \caption{The comparison results}
 \end{figure}
-\begin{figure}
-  \centering
-  \includegraphics[scale=0.5]{fig/edp_per}
-  \caption{Comparing of the performance degradation for the proposed method with EDP method}
-  \label{fig:edp-perf}
-\end{figure}
-\begin{figure}
-  \centering
-  \includegraphics[scale=0.5]{fig/edp_dist}
-  \caption{Comparing of the tradeoff distance for the proposed method with EDP method}
-  \label{fig:edp-dist}
-\end{figure}
-
-
 
 As shown in these figures, the proposed frequencies selection algorithm, Maxdist, outperforms the EDP algorithm in terms of energy consumption reduction and performance for all of the benchmarks executed over the two scenarios. 
 The proposed algorithm gives better results than EDP  because it 
@@ -1249,7 +1291,6 @@ $O(N \cdot M \cdot F^2)$ respectively, where $N$ is the number of the clusters,
 maximum number of available frequencies. When Maxdist is applied to a benchmark that is being executed over 32 nodes distributed between Nancy and Lyon sites, it takes on average  $0.01 ms$  to compute the best frequencies while EDP is on average ten times slower over the same architecture.  
 
 
-
 \section{Conclusion}
 \label{sec.concl}
 This paper has presented a new online frequencies selection algorithm.
@@ -1262,8 +1303,8 @@ of the distributed iterative message passing application running over a grid arc
 To evaluate the proposed method on a real heterogeneous grid platform, it was applied on the  
  NAS parallel benchmarks   and the  class D instance was executed over the  grid'5000 testbed platform. 
  The experimental results showed that the algorithm reduces  on average 30\% of the energy consumption
-for all the NAS benchmarks   while  only degrading by 3\% on average  the performance. 
-The Maxdist algorithm was also evaluated in different scenarios that vary in the distribution of the computing nodes between different clusters' sites or in the values of the consumed static power. The algorithm selects different vector of frequencies according to the 
+for all the NAS benchmarks   while  only degrading by 3.2\% on average  the performance. 
+The Maxdist algorithm was also evaluated in different scenarios that vary in the distribution of the computing nodes between different clusters' sites or \textcolor{blue}{between using one core and multi-cores per node} or in the values of the consumed static power. The algorithm selects different vector of frequencies according to the 
 computations and communication times ratios, and  the values of the static and measured dynamic powers of the CPUs. 
 Finally, the proposed algorithm was compared to another method that uses
 the well known energy and delay product as an objective function. The comparison results showed 
@@ -1285,24 +1326,10 @@ This work  has been  partially supported by  the Labex ACTION  project (contract
 Mr. Ahmed  Fanfakh, would  like to  thank the University  of Babylon  (Iraq) for
 supporting his work.
 
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-% the document is modified later
-%\IEEEtriggeratref{15}
 
-\bibliographystyle{IEEEtran}
-\bibliography{IEEEabrv,my_reference}
+\bibliographystyle{elsarticle-num}
+\bibliography{my_reference}
+
 \end{document}
 
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-% LocalWords:  Fanfakh Charr FIXME Tianhe DVFS HPC NAS NPB SMPI Rauber's Rauber
-% LocalWords:  CMOS EPSA Franche Comté Tflop Rünger IUT Maréchal Juin cedex GPU
-% LocalWords:  de badri muslim MPI SimGrid GFlops Xeon EP BT GPUs CPUs AMD
-%  LocalWords:  Spiliopoulos scalability
+