-\documentclass[conference]{IEEEtran}
-
+\documentclass[3p,times]{elsarticle-1}
+\usepackage{ecrc}
\usepackage[T1]{fontenc}
\usepackage[utf8]{inputenc}
\usepackage[english]{babel}
\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}
-\textcolor{blue}{
- In recent years, green computing topic has being became an important topic in
- the domain of the research. The increase in computing power of the computing
- platforms is increased the energy consumption and the carbon dioxide emissions.
- Many techniques have being used to minimize the cost of the energy consumption
- and reduce environmental pollution. Dynamic voltage and frequency scaling (DVFS)
- is one of these techniques. It used to reduce the power consumption of the CPU
- while computing by lowering its frequency. Moreover, lowering the frequency of
+
+ 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
+them. To minimize the operating costs of these platforms many
+techniques have been used. Dynamic voltage and frequency
+scaling (DVFS) is one of them. It can be used to reduce the power consumption of the CPU
+ while computing, by lowering its frequency. However, lowering the frequency of
a CPU may increase the execution time of an application running on that
processor. Therefore, the frequency that gives the best trade-off between
the energy consumption and the performance of an application must be selected.
- In this paper, a new online frequency selecting algorithm for heterogeneous
- grid (heterogeneous CPUs) is presented. It selects the frequencies and tries to give the best
+ In this paper, a new online frequency selecting algorithm for grids, composed of heterogeneous clusters, is presented.
+ It selects the frequencies and tries to give the best
trade-off between energy saving and performance degradation, for each node
- computing the message passing iterative application. The algorithm has a small
+ computing the message passing iterative application.
+ The algorithm has a small
overhead and works without training or profiling. It uses a new energy model
- for message passing iterative applications running on a heterogeneous
- grid. The proposed algorithm is evaluated on real testbed, grid'5000 platform, while
+ for message passing iterative applications running on a grid.
+ 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 up to \np[\%]{30} while declines the performance
+ energy consumption on average by \np[\%]{30} while the performance is only degraded
on average by \np[\%]{3}. Finally, the algorithm is
- compared to an existing method, the comparison results show that it outperforms the
- latter in term of energy and performance trade-off.}
+ 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
The results of the experiments showed significant energy
consumption reductions. All the experimental results were conducted over
Simgrid simulator \cite{SimGrid}, which offers easy tools to create a homogeneous and heterogeneous platforms and run message passing parallel applications over them. In this paper, a new frequencies selecting algorithm,
-adapted to grid platforms, is presented. It is applied to the NAS parallel benchmarks and evaluated over a real testbed,
+adapted to grid platforms composed of heterogeneous clusters, is presented. It is applied to the NAS parallel benchmarks and evaluated over a real testbed,
the grid'5000 platform \cite{grid5000}. It selects for a grid platform running a message passing iterative
application the vector of
frequencies that simultaneously tries to offer the maximum energy reduction and
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 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
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.
\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.
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
dynamic and static powers) and the task executed (computation/communication
ratio). The aim being to reduce the overall energy consumption and to avoid
increasing significantly the execution time.
-\textcolor{blue}{ In our previous
-works~\cite{Our_first_paper} and \cite{pdsec2015}, we proposed a methods that select the optimal
-frequency scaling factors for a homogeneous and a heterogeneous clusters respectively.
-Both of the two methods executing a message passing
-iterative synchronous application while giving the best trade-off between the
-energy consumption and the performance for such applications. In this work we
-are interested in heterogeneous grid as described above.}
+In our previous
+works, \cite{Our_first_paper} and \cite{pdsec2015}, two methods that select the optimal
+frequency scaling factors for a homogeneous and a heterogeneous cluster respectively, were proposed.
+Both methods selects the frequencies that gives the best tradeoff between
+energy consumption reduction and performance for message passing
+iterative synchronous applications. In this work we
+are interested in grids that are composed of heterogeneous clusters were the nodes have different characteristics such as dynamic power, static power, computation power, frequencies range, network latency and bandwidth.
Due to the
heterogeneity of the processors, a vector of scaling factors should be selected
and it must give the best trade-off between energy consumption and performance.
\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=.33\textwidth]{fig/homo}\label{fig:r1}} \hspace{2cm}%
\subfloat[Heterogeneous grid]{%
\includegraphics[width=.33\textwidth]{fig/heter}\label{fig:r2}}
\label{fig:rel}
\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
\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}
\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]
& 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}
+ \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}
The NAS parallel benchmarks are executed over these two platforms
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}
+ \subfloat[The energy reduction while executing the NAS benchmarks over different scenarios ]{%
+ \includegraphics[width=.33\textwidth]{fig/eng_s.eps}\label{fig:eng_s}} \hspace{0.08cm}%
+ \subfloat[The performance degradation of the NAS benchmarks over different scenarios]{%
+ \includegraphics[width=.33\textwidth]{fig/per_d.eps}\label{fig:per_d}}\hspace{0.08cm}%
+ \subfloat[The tradeoff distance between the energy reduction and the performance of the NAS benchmarks
+ over different scenarios]{%
+ \includegraphics[width=.33\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 computed as the ratio between the reduced
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}
+\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}. The cores of each node can exchange
-data via the shared memory \cite{rauber_book}. 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
+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 NAS parallel
-benchmarks, class D, over these four different scenarios are presented
+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.
-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.
-
- \textcolor{blue}{On the other hand, the execution times for most of the NAS benchmarks are lower over
-the two sites multi-cores scenario than those over the two sites one core scenario. ???????
-}
-
-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 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
+\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
- \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}
+ \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}
- \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}
+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
- \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}
+ \subfloat[The energy saving of running NAS benchmarks over one core and multicores scenarios]{%
+ \includegraphics[width=.33\textwidth]{fig/eng_s_mc.eps}\label{fig:eng-s-mc}} \hspace{0.08cm}%
+ \subfloat[The performance degradation of running NAS benchmarks over one core and multicores scenarios
+ ]{%
+ \includegraphics[width=.33\textwidth]{fig/per_d_mc.eps}\label{fig:per-d-mc}}\hspace{0.08cm}%
+ \subfloat[The tradeoff distance of running NAS benchmarks over one core and multicores scenarios]{%
+ \includegraphics[width=.33\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}
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=.33\textwidth]{fig/eng_pow.eps}\label{fig:eng-pow}} \hspace{0.08cm}%
+ \subfloat[The performance degradation percentages for the NAS benchmarks over the three power scenarios]{%
+ \includegraphics[width=.33\textwidth]{fig/per_pow.eps}\label{fig:per-pow}}\hspace{0.08cm}%
+ \subfloat[The tradeoff distance between the energy reduction and the performance of the NAS benchmarks over the three power scenarios]{%
+ \includegraphics[width=.33\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}
\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=.33\textwidth]{fig/edp_eng}\label{fig:edp-eng}} \hspace{0.08cm}%
+ \subfloat[The performance degradation induced by the Maxdist method and the EDP method]{%
+ \includegraphics[width=.33\textwidth]{fig/edp_per}\label{fig:edp-perf}}\hspace{0.08cm}%
+ \subfloat[The tradeoff distance between the energy consumption reduction and the performance for the Maxdist method and the EDP method]{%
+ \includegraphics[width=.33\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
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.
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, use one core or multi-cores per node or assume different values for 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 \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
Mr. Ahmed Fanfakh, would like to thank the University of Babylon (Iraq) for
supporting his work.
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+
