-\documentclass[3p,times]{elsarticle-1}
-\usepackage{ecrc}
+\documentclass[review]{elsarticle}
+
+\usepackage{lineno,hyperref}
+\modulolinenumbers[5]
+
+\journal{Journal of Computational Science}
+
+%%%%%%%%%%%%%%%%%%%%%%%
+%% Elsevier bibliography styles
+%%%%%%%%%%%%%%%%%%%%%%%
+%% To change the style, put a % in front of the second line of the current style and
+%% remove the % from the second line of the style you would like to use.
+%%%%%%%%%%%%%%%%%%%%%%%
+
+%% Numbered
+%\bibliographystyle{model1-num-names}
+
+%% Numbered without titles
+%\bibliographystyle{model1a-num-names}
+
+%% Harvard
+%\bibliographystyle{model2-names.bst}\biboptions{authoryear}
+
+%% Vancouver numbered
+%\usepackage{numcompress}\bibliographystyle{model3-num-names}
+
+%% Vancouver name/year
+%\usepackage{numcompress}\bibliographystyle{model4-names}\biboptions{authoryear}
+
+%% APA style
+%\bibliographystyle{model5-names}\biboptions{authoryear}
+
+%% AMA style
+%\usepackage{numcompress}\bibliographystyle{model6-num-names}
+
+%% `Elsevier LaTeX' style
+\bibliographystyle{elsarticle-num}
+%%%%%%%%%%%%%%%%%%%%%%%
+
\usepackage[T1]{fontenc}
\usepackage[utf8]{inputenc}
\usepackage[english]{babel}
\usepackage{algpseudocode}
\usepackage{graphicx}
\usepackage{algorithm}
+\usepackage{setspace}
\usepackage{subfig}
\usepackage{amsmath}
\usepackage{url}
\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}
\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}
+
+
+\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,
Section~\ref{sec.optim} details the proposed frequencies selecting algorithm.
Section~\ref{sec.expe} presents the results of applying the algorithm on the
NAS parallel benchmarks and executing them on the grid'5000 testbed.
-%It shows the results of running different scenarios using multi-cores and one core per node and comparing them.
-It also evaluates the algorithm over three different power scenarios. Moreover, it shows the
+It also evaluates the algorithm over multi-cores per node architectures and over three different power scenarios. Moreover, it 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}
\end{equation}
Replacing $\Fnew$ in (\ref{eq:pd}) as in (\ref{eq:fnew}) gives the following
equation for dynamic power consumption:
-\begin{equation}
+\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{equation}
+ \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
new frequency and the maximum frequency respectively.
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{equation}
+\begin{multline}
\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{equation}
+\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
\label{sec.optim}
\begin{algorithm}
+\setstretch{1}
\begin{algorithmic}[1]
% \footnotesize
+
\Require ~
\begin{description}
- \item [{$N$}] number of clusters in the grid.
+ \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.
\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 $\Enorm\gets \frac{\Ereduced}{\Eoriginal}$
+ \State $\Pnorm \gets \frac{\Told}{\Tnew}$, $\Enorm\gets \frac{\Ereduced}{\Eoriginal}$
\If{$(\Pnorm - \Enorm > \Dist)$}
\State $\Sopt[ij] \gets S_{ij},~i=1,\dots,N,~j=1,\dots,M_i. $
\State $\Dist \gets \Pnorm - \Enorm$
Name & model & Freq. & Freq. & Freq. & per CPU & of one core \\
& & GHz & GHz & GHz & & \\
\hline
- Taurus & Intel & 2.3 & 1.2 & 0.1 & 6 & \np[W]{35} \\
- & Xeon & & & & & \\
+ & Intel & 2.3 & 1.2 & 0.1 & 6 & \np[W]{35} \\
+ Taurus & Xeon & & & & & \\
& E5-2630 & & & & & \\
\hline
- Graphene & Intel & 2.53 & 1.2 & 0.133 & 4 & \np[W]{23} \\
- & Xeon & & & & & \\
+ & Intel & 2.53 & 1.2 & 0.133 & 4 & \np[W]{23} \\
+ Graphene & Xeon & & & & & \\
& X3440 & & & & & \\
\hline
- Griffon & Intel & 2.5 & 2 & 0.5 & 4 & \np[W]{46} \\
- & Xeon & & & & & \\
+ & Intel & 2.5 & 2 & 0.5 & 4 & \np[W]{46} \\
+ Griffon & Xeon & & & & & \\
& L5420 & & & & & \\
\hline
- Graphite & Intel & 2 & 1.2 & 0.1 & 8 & \np[W]{35} \\
- & Xeon & & & & & \\
+ & Intel & 2 & 1.2 & 0.1 & 8 & \np[W]{35} \\
+ Graphite & Xeon & & & & & \\
& E5-2650 & & & & & \\
\hline
\end{tabular}
\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}%
+ \includegraphics[width=.48\textwidth]{fig/eng_con_scenarios.eps}\label{fig:eng_sen}} \hspace{0.4cm}%
\subfloat[The execution times of the NAS benchmarks over different scenarios]{%
- \includegraphics[width=.4\textwidth]{fig/time_scenarios.eps}\label{fig:time_sen}}
+ \includegraphics[width=.48\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
produces less energy consumption and thus more energy saving.
The best energy saving percentage was obtained in the one site scenario with 16 nodes, the energy consumption was on average reduced up to 30\%.
-
+\begin{figure}
+ \centering
+ \subfloat[The energy reduction while executing the NAS benchmarks over different scenarios ]{%
+ \includegraphics[width=.48\textwidth]{fig/eng_s.eps}\label{fig:eng_s}} \hspace{0.4cm}%
+ \subfloat[The performance degradation of the NAS benchmarks over different scenarios]{%
+ \includegraphics[width=.48\textwidth]{fig/per_d.eps}\label{fig:per_d}}\hspace{0.4cm}%
+ \subfloat[The tradeoff distance between the energy reduction and the performance of the NAS benchmarks
+ over different scenarios]{%
+ \includegraphics[width=.48\textwidth]{fig/dist.eps}\label{fig:dist}}
+ \label{fig:exp-res}
+ \caption{The experimental results of different scenarios}
+\end{figure}
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 8.3\% or 4.7\% respectively.
\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}%
+ \includegraphics[width=.48\textwidth]{fig/time.eps}\label{fig:time-mc}} \hspace{0.4cm}%
\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}}
+ \includegraphics[width=.48\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}
\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}%
+ \includegraphics[width=.48\textwidth]{fig/eng_s_mc.eps}\label{fig:eng-s-mc}} \hspace{0.4cm}%
\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}%
+ \includegraphics[width=.48\textwidth]{fig/per_d_mc.eps}\label{fig:per-d-mc}}\hspace{0.4cm}%
\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}}
+ \includegraphics[width=.48\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}
\begin{figure}
\centering
\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}%
+ \includegraphics[width=.48\textwidth]{fig/eng_pow.eps}\label{fig:eng-pow}} \hspace{0.4cm}%
\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}%
+ \includegraphics[width=.48\textwidth]{fig/per_pow.eps}\label{fig:per-pow}}\hspace{0.4cm}%
\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}}
+ \includegraphics[width=.48\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
\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}%
+ \includegraphics[width=.48\textwidth]{fig/edp_eng}\label{fig:edp-eng}} \hspace{0.4cm}%
\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}%
+ \includegraphics[width=.48\textwidth]{fig/edp_per}\label{fig:edp-perf}}\hspace{0.4cm}%
\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}}
+ \includegraphics[width=.48\textwidth]{fig/edp_dist}\label{fig:edp-dist}}
\label{fig:edp-comparison}
\caption{The comparison results}
\end{figure}
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 3.2\% 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
+The Maxdist algorithm was also evaluated in different scenarios that vary in the distribution of the computing nodes between different clusters' sites or use multi-cores per node architecture or consume different static power values. 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
Mr. Ahmed Fanfakh, would like to thank the University of Babylon (Iraq) for
supporting his work.
-
-\bibliographystyle{elsarticle-num}
+%\section*{References}
\bibliography{my_reference}
\end{document}
