-\documentclass[conference]{IEEEtran}
+\documentclass[review]{elsarticle}
+
+\usepackage{lineno,hyperref}
+\modulolinenumbers[5]
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+\journal{Journal of Computational Science}
+
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\usepackage{graphicx}
\usepackage{algorithm}
+\usepackage{setspace}
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\usepackage{amsmath}
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\newcommand{\Tnew}{\Xsub{T}{New}}
\newcommand{\Told}{\Xsub{T}{Old}}
+
+
+
\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{Energy Consumption Reduction with DVFS for Message \\
+ Passing Iterative Applications on \\
+ Grid Architecture}
+
+
+
+
+\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}
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
- 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
+ The proposed algorithm is evaluated on a real grid, the Grid'5000 platform, while
+ running the NAS parallel benchmarks. The experiments on 16 nodes, distributed on three clusters, show that it reduces on average the
+ energy consumption by \np[\%]{30} while the performance is on average only degraded
+ 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}
-
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
computing platforms must be more energy efficient and offer the highest number
of FLOPS per watt possible, such as the Shoubu-ExaScaler from RIKEN
which became the top of the Green500 list in June 2015 \cite{Green500_List}.
-This heterogeneous platform executes more than 7 GFLOPS per watt while consuming
+This heterogeneous platform executes more than 7 GFlops per watt while consuming
50.32 kilowatts.
Besides platform improvements, there are many software and hardware techniques
time of the application running over that processor. Therefore, researchers use
different optimization strategies to select the frequency that gives the best
trade-off between the energy reduction and performance degradation ratio. In
-\cite{Our_first_paper} and \cite{pdsec2015} , a frequencies selecting algorithm was proposed to reduce
+\cite{Our_first_paper} and \cite{pdsec2015} , a frequency selecting algorithm was proposed to reduce
the energy consumption of message passing iterative applications running over
homogeneous and heterogeneous clusters respectively.
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,
+consumption reductions. All the experimental results were conducted over the
+SimGrid simulator \cite{SimGrid}, which offers easy tools to create homogeneous and heterogeneous platforms and runs message passing parallel applications over them. In this paper, a new frequency selecting algorithm,
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
+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
minimum performance degradation ratios. The algorithm has a very small overhead,
consumption while minimizing the degradation of the program's performance.
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
+NAS parallel benchmarks and executing them on the Grid'5000 testbed.
+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}
where $N$ is the number of clusters in the grid, $M$ is the number of nodes in
each cluster, $\TcpOld[ij]$ is the computation time of processor $j$ in the cluster $i$
and $\Tcm[hj]$ is the communication time of processor $j$ in the cluster $h$ during the
-first iteration. The model computes the maximum computation time with scaling factor
-from each node added to the communication time of the slowest node in the slowest cluster $h$.
+first iteration. the execution time for one iteration is equal to the sum of the maximum computation time for all nodes with the new scaling factors
+ and the slowest communication time without slack time during one iteration.
+The latter is equal to the communication time of the slowest node in the slowest cluster $h$.
It means only the communication time without any slack time is taken into account.
Therefore, the execution time of the iterative application is equal to
the execution time of one iteration as in (\ref{eq:perf}) multiplied by the
The main objective of DVFS operation is to reduce the overall energy
consumption~\cite{Le_DVFS.Laws.of.Diminishing.Returns}. The operational
frequency $F$ depends linearly on the supply voltage $V$, i.e., $V = \beta \cdot
-F$ with some constant $\beta$.~This equation is used to study the change of the
+F$ with some constant $\beta$. This equation is used to study the change of the
dynamic voltage with respect to various frequency values
in~\cite{Rauber_Analytical.Modeling.for.Energy}. The reduction process of the
frequency can be expressed by the scaling factor $S$ which is the ratio between
+\mathop{\min_{j=1,\dots M}} (\Tcm[hj]) ))
\end{multline}
+
Reducing the frequencies of the processors according to the vector of scaling
factors $(S_{11}, S_{12},\dots, S_{NM})$ may degrade the performance of the application
and thus, increase the static energy because the execution time is
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
+Both methods selects the frequencies that gives the best trade-off 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.
While the main goal is to optimize the energy and execution time at the same
time, the normalized energy and execution time curves do not evolve (increase/decrease) in the same way.
-According to the equations~(\ref{eq:pnorm}) and (\ref{eq:enorm}), the
+According to Equations~\ref{eq:pnorm} and \ref{eq:enorm}, the
vector of frequency scaling factors $S_1,S_2,\dots,S_N$ reduce both the energy
and the execution time simultaneously. But the main objective is to produce
maximum energy reduction with minimum execution time reduction.
\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=.48\textwidth]{fig/homo}\label{fig:r1}} \hspace{0.4cm}%
\subfloat[Heterogeneous grid]{%
- \includegraphics[width=.33\textwidth]{fig/heter}\label{fig:r2}}
+ \includegraphics[width=.48\textwidth]{fig/heter}\label{fig:r2}}
\label{fig:rel}
\caption{The energy and performance relation}
\end{figure}
\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.
\item[{$\Ps[ij]$}] array of the static powers for all nodes.
\item[{$\Fdiff[ij]$}] array of the differences between two successive frequencies for all nodes.
\end{description}
- \Ensure $\Sopt[11],\Sopt[12] \dots, \Sopt[NM_i]$, a vector of scaling factors that gives the optimal tradeoff between energy consumption and execution time
+ \Ensure $\Sopt[11],\Sopt[12] \dots, \Sopt[NM_i]$, a vector of scaling factors that gives the optimal trade-off between energy consumption and execution time
\State $\Scp[ij] \gets \frac{\max_{i=1,2,\dots,N}(\max_{j=1,2,\dots,M_i}(\Tcp[ij]))}{\Tcp[ij]} $
\State $F_{ij} \gets \frac{\Fmax[ij]}{\Scp[i]},~{i=1,2,\cdots,N},~{j=1,2,\dots,M_i}.$
\If{(not the first frequency)}
\State $F_{ij} \gets F_{ij}+\Fdiff[ij],~i=1,\dots,N,~{j=1,\dots,M_i}.$
\EndIf
- \State $\Told \gets $ computed as in equations (\ref{eq:told}).
- \State $\Eoriginal \gets $ computed as in equations (\ref{eq:eorginal}) .
+ \State $\Told \gets $ computed as in Equation \ref{eq:told}.
+ \State $\Eoriginal \gets $ computed as in Equation \ref{eq:eorginal}.
\State $\Sopt[ij] \gets 1,~i=1,\dots,N,~{j=1,\dots,M_i}. $
\State $\Dist \gets 0 $
\While {(all nodes have not reached their minimum \newline\hspace*{2.5em} frequency \textbf{or} $\Pnorm - \Enorm < 0 $)}
\State $F_{ij} \gets F_{ij} - \Fdiff[ij],~{i=1,\dots,N},~{j=1,\dots,M_i}$.
\State $S_{ij} \gets \frac{\Fmax[ij]}{F_{ij}},~{i=1,\dots,N},~{j=1,\dots,M_i}.$
\EndIf
- \State $\Tnew \gets $ computed as in equations (\ref{eq:perf}).
- \State $\Ereduced \gets $ computed as in equations (\ref{eq:energy}).
- \State $\Pnorm \gets \frac{\Told}{\Tnew}$
- \State $\Enorm\gets \frac{\Ereduced}{\Eoriginal}$
+ \State $\Tnew \gets $ computed as in Equation \ref{eq:perf}.
+ \State $\Ereduced \gets $ computed as in Equation \ref{eq:energy}.
+ \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 $\Dist \gets \Pnorm - \Enorm$
\State Computations section.
\State Communications section.
\If {$(k=1)$}
- \State Gather all times of computation and\newline\hspace*{3em}%
- communication from each node.
+ \State Gather all times of computation and communication from each node.
\State Call Algorithm \ref{HSA}.
\State Compute the new frequencies from the\newline\hspace*{3em}%
returned optimal scaling factors.
\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}
of the slowest node and the computation time of the node $i$ as follows:
\begin{equation}
\label{eq:Scp}
- \Scp[ij] = \frac{ \mathop{\max_{i=1,\dots N}}_{j=1,\dots,M}(\Tcp[ij])} {\Tcp[ij]}
+ \Scp[ij] = \frac{ \mathop{\max\limits_{i=1,\dots N}}\limits_{j=1,\dots,M}(\Tcp[ij])} {\Tcp[ij]}
\end{equation}
Using the initial frequency scaling factors computed in (\ref{eq:Scp}), the
algorithm computes the initial frequencies for all nodes as a ratio between the
bound until all nodes reach their minimum frequencies or their lower bounds, to compute the overall
energy consumption and performance and selects the optimal vector of the frequency scaling
factors. At each iteration the algorithm determines the slowest node
-according to the equation (\ref{eq:perf}) and keeps its frequency unchanged,
+according to Equation~\ref{eq:perf} and keeps its frequency unchanged,
while it lowers the frequency of all other nodes by one gear. The new overall
energy consumption and execution time are computed according to the new scaling
factors. The optimal set of frequency scaling factors is the set that gives the
-highest distance according to the objective function (\ref{eq:max}).
+highest distance according to the objective function~\ref{eq:max}.
Figures~\ref{fig:r1} and \ref{fig:r2} illustrate the normalized performance and
consumed energy for an application running on a homogeneous cluster and a
\section{Experimental results}
\label{sec.expe}
While in~\cite{pdsec2015} the energy model and the scaling factors selection algorithm were applied to a heterogeneous cluster and evaluated over the SimGrid simulator~\cite{SimGrid},
-in this paper real experiments were conducted over the grid'5000 platform.
+in this paper real experiments were conducted over the Grid'5000 platform.
-\subsection{Grid'5000 architature and power consumption}
+\subsection{Grid'5000 architecture and power consumption}
\label{sec.grid5000}
-Grid'5000~\cite{grid5000} is a large-scale testbed that consists of ten sites distributed over all metropolitan France and Luxembourg. All the sites are connected together via a special long distance network called RENATER,
+Grid'5000~\cite{grid5000} is a large-scale testbed that consists of ten sites distributed all over metropolitan France and Luxembourg. All the sites are connected together via a special long distance network called RENATER,
which is the French National Telecommunication Network for Technology.
-Each site of the grid is composed of few heterogeneous
+Each site of the grid is composed of a few heterogeneous
computing clusters and each cluster contains many homogeneous nodes. In total,
-grid'5000 has about one thousand heterogeneous nodes and eight thousand cores. In each site,
+Grid'5000 has about one thousand heterogeneous nodes and eight thousand cores. In each site,
the clusters and their nodes are connected via high speed local area networks.
Two types of local networks are used, Ethernet or Infiniband networks which have different characteristics in terms of bandwidth and latency.
-Since grid'5000 is dedicated for testing, contrary to production grids it allows a user to deploy its own customized operating system on all the booked nodes. The user could have root rights and thus apply DVFS operations while executing a distributed application. Moreover, the grid'5000 testbed provides at some sites a power measurement tool to capture
-the power consumption for each node in those sites. The measured power is the overall consumed power by by all the components of a node at a given instant, such as CPU, hard drive, main-board, memory, ... For more details refer to
-\cite{Energy_measurement}. To just measure the CPU power of one core in a node $j$,
- firstly, the power consumed by the node while being idle at instant $y$, noted as $\Pidle[jy]$, was measured. Then, the power was measured while running a single thread benchmark with no communication (no idle time) over the same node with its CPU scaled to the maximum available frequency. The latter power measured at time $x$ with maximum frequency for one core of node $j$ is noted $\Pmax[jx]$. The difference between the two measured power consumption represents the
-dynamic power consumption of that core with the maximum frequency, see figure(\ref{fig:power_cons}).
+Since Grid'5000 is dedicated to testing, contrary to production grids it allows a user to deploy its own customized operating system on all the booked nodes. The user could have root rights and thus apply DVFS operations while executing a distributed application. Moreover, the Grid'5000 testbed provides at some sites a power measurement tool to capture
+the power consumption for each node in those sites. The measured power is the overall consumed power by all the components of a node at a given instant, such as CPU, hard drive, main-board, memory, ... For more details refer to
+\cite{Energy_measurement}. In order to correctly measure the CPU power of one core in a node $j$,
+ firstly, the power consumed by the node while being idle at instant $y$, noted as $\Pidle[jy]$, was measured. Then, the power was measured while running a single thread benchmark with no communication (no idle time) over the same node with its CPU scaled to the maximum available frequency. The latter power measured at time $x$ with maximum frequency for one core of node $j$ is noted $\Pmax[jx]$. The difference between the two measured power consumptions represents the
+dynamic power consumption of that core with the maximum frequency, see Figure~\ref{fig:power_cons}.
-The dynamic power $\Pd[j]$ is computed as in equation (\ref{eq:pdyn})
+The dynamic power $\Pd[j]$ is computed as in Equation~\ref{eq:pdyn}
\begin{equation}
\label{eq:pdyn}
\Pd[j] = \max_{x=\beta_1,\dots \beta_2} (\Pmax[jx]) - \min_{y=\Theta_1,\dots \Theta_2} (\Pidle[jy])
Therefore, the dynamic power of one core is computed as the difference between the maximum
measured value in maximum powers vector and the minimum measured value in the idle powers vector.
-On the other hand, the static power consumption by one core is a part of the measured idle power consumption of the node. Since in grid'5000 there is no way to measure precisely the consumed static power and in~\cite{Our_first_paper,pdsec2015,Rauber_Analytical.Modeling.for.Energy} it was assumed that the static power represents a ratio of the dynamic power, the value of the static power is assumed as 20\% of dynamic power consumption of the core.
+On the other hand, the static power consumption by one core is a part of the measured idle power consumption of the node. Since in Grid'5000 there is no way to measure precisely the consumed static power and in~\cite{Our_first_paper,pdsec2015,Rauber_Analytical.Modeling.for.Energy} it was assumed that the static power represents a ratio of the dynamic power, the value of the static power is assumed as 20\% of dynamic power consumption of the core.
-In the experiments presented in the following sections, two sites of grid'5000 were used, Lyon and Nancy sites. These two sites have in total seven different clusters as in figure (\ref{fig:grid5000}).
+In the experiments presented in the following sections, two sites of Grid'5000 were used, Lyon and Nancy sites. These two sites have in total seven different clusters as in Figure~\ref{fig:grid5000}.
Four clusters from the two sites were selected in the experiments: one cluster from
-Lyon's site, Taurus cluster, and three clusters from Nancy's site, Graphene,
+Lyon's site, Taurus, and three clusters from Nancy's site, Graphene,
Griffon and Graphite. Each one of these clusters has homogeneous nodes inside, while nodes from different clusters are heterogeneous in many aspects such as: computing power, power consumption, available
-frequency ranges and local network features: the bandwidth and the latency. Table \ref{table:grid5000} shows
-the details characteristics of these four clusters. Moreover, the dynamic powers were computed using the equation (\ref{eq:pdyn}) for all the nodes in the
-selected clusters and are presented in table \ref{table:grid5000}.
+frequency ranges and local network features: the bandwidth and the latency. Table~\ref{table:grid5000} shows
+the detailed characteristics of these four clusters. Moreover, the dynamic powers were computed using Equation~\ref{eq:pdyn} for all the nodes in the
+selected clusters and are presented in Table~\ref{table:grid5000}.
\begin{figure}[!t]
\centering
\includegraphics[scale=1]{fig/grid5000}
- \caption{The selected two sites of grid'5000}
+ \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}
\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, class D was used for all benchmarks in all the experiments presented in the next sections.
+
\begin{table}[!t]
\centering
\begin{tabular}{|*{7}{c|}}
\hline
- Cluster & CPU & Max & Min & Diff. & no. of cores & dynamic power \\
- Name & model & Freq. & Freq. & Freq. & per CPU & of one core \\
- & & GHz & GHz & GHz & & \\
+ & & Max & Min & Diff. & & \\
+ Cluster & CPU & Freq. & Freq. & Freq. & No. of cores & Dynamic power \\
+ Name & model & GHz & GHz & GHz & per CPU & of one core \\
\hline
- Taurus & Intel & 2.3 & 1.2 & 0.1 & 6 & \np[W]{35} \\
- & Xeon & & & & & \\
- & E5-2630 & & & & & \\
+ & Intel & & & & & \\
+ Taurus & Xeon & 2.3 & 1.2 & 0.1 & 6 & \np[W]{35} \\
+ & E5-2630 & & & & & \\
\hline
- Graphene & Intel & 2.53 & 1.2 & 0.133 & 4 & \np[W]{23} \\
- & Xeon & & & & & \\
- & X3440 & & & & & \\
+ & Intel & & & & & \\
+ Graphene & Xeon & 2.53 & 1.2 & 0.133 & 4 & \np[W]{23} \\
+ & X3440 & & & & & \\
\hline
- Griffon & Intel & 2.5 & 2 & 0.5 & 4 & \np[W]{46} \\
- & Xeon & & & & & \\
- & L5420 & & & & & \\
+ & Intel & & & & & \\
+ Griffon & Xeon & 2.5 & 2 & 0.5 & 4 & \np[W]{46} \\
+ & L5420 & & & & & \\
\hline
- Graphite & Intel & 2 & 1.2 & 0.1 & 8 & \np[W]{35} \\
- & Xeon & & & & & \\
- & E5-2650 & & & & & \\
+ & Intel & & & & & \\
+ Graphite & Xeon & 2 & 1.2 & 0.1 & 8 & \np[W]{35} \\
+ & E5-2650 & & & & & \\
\hline
\end{tabular}
\label{table:grid5000}
to the NAS parallel benchmarks are presented.
As mentioned previously, the experiments
-were conducted over two sites of grid'5000, Lyon and Nancy sites.
+were conducted over two sites of Grid'5000, Lyon and Nancy sites.
Two scenarios were considered while selecting the clusters from these two sites :
\begin{itemize}
\item In the first scenario, nodes from two sites and three heterogeneous clusters were selected. The two sites are connected
via a long distance network.
-\item In the second scenario nodes from three clusters that are located in one site, Nancy site.
+\item In the second scenario nodes from three clusters located in one site, Nancy site, were selected.
\end{itemize}
The main reason
-behind using these two scenarios is to evaluate the influence of long distance communications (higher latency) on the performance of the
+for using these two scenarios is to evaluate the influence of long distance communications (higher latency) on the performance of the
scaling factors selection algorithm. Indeed, in the first scenario the computations to communications ratio
-is very low due to the higher communication times which reduces the effect of DVFS operations.
+is very low due to the higher communication times which reduce the effect of DVFS operations.
The NAS parallel benchmarks are executed over
-16 and 32 nodes for each scenario. The number of participating computing nodes form each cluster
-are different because all the selected clusters do not have the same available number of nodes and all benchmarks do not require the same number of computing nodes.
-Table \ref{tab:sc} shows the number of nodes used from each cluster for each scenario.
+16 and 32 nodes for each scenario. The number of participating computing nodes from each cluster
+is different because all the selected clusters do not have the same available number of nodes and all benchmarks do not require the same number of computing nodes.
+Table~\ref{tab:sc} shows the number of nodes used from each cluster for each scenario.
\begin{table}[h]
& 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 consumption by the nodes wile executing the 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 the 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}.
+ 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
using the proposed frequency selection algorithm is measured
-using the equation of the reduced energy consumption, equation
-(\ref{eq:energy}). This model uses the measured dynamic and static
-power values showed in Table \ref{table:grid5000}. The execution
+using the equation of the reduced energy consumption, Equation~\ref{eq:energy}. This model uses the measured dynamic power showed in Table~\ref{table:grid5000}
+and the static
+power is assumed to be equal to 20\% of the dynamic power. The execution
time is measured for all the benchmarks over these different scenarios.
The energy consumptions and the execution times for all the benchmarks are
-presented in the plots \ref{fig:eng_sen} and \ref{fig:time_sen} respectively.
+presented in Plots~\ref{fig:eng_sen} and \ref{fig:time_sen} respectively.
For the majority of the benchmarks, the energy consumed while executing the NAS benchmarks over one site scenario
for 16 and 32 nodes is lower than the energy consumed while using two sites.
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 reduction while executing the 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 the 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 between the energy reduction and the performance of the 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,
-equation (\ref{eq:eorginal}), for all benchmarks as in figure \ref{fig:eng_s}.
+energy consumption, Equation~\ref{eq:energy}, and the original energy consumption,
+Equation~\ref{eq:eorginal}, for all benchmarks as in Figure~\ref{fig:eng_s}.
This figure shows that the energy saving percentages of one site scenario for
16 and 32 nodes are bigger than those of the two sites scenario which is due
to the higher computations to communications ratio in the first scenario
than in the second one. Moreover, the frequency selecting algorithm selects smaller frequencies when the computations times are bigger than the communication times which
results in a lower energy consumption. Indeed, the dynamic consumed power
-is exponentially related to the CPU's frequency value. On the other side, the increase in the number of computing nodes can
+is exponentially related to the CPU's frequency value. On the other hand, the increase in the number of computing nodes can
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
+benchmarks being executed. The results of 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=.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=.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}
+
+
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
+scenario, except for the EP benchmark which has no communication. 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
-in the one site scenario, the graphite cluster is selected but in the two sits scenario
-this cluster is replaced with Taurus cluster which is more powerful.
-Therefore, the energy saving of EP benchmarks are bigger in the two sites scenario due
+in the one site scenario, the graphite cluster is selected but in the two sites scenario
+this cluster is replaced with the Taurus cluster which is more powerful.
+Therefore, the energy savings of the EP benchmark are bigger in the two sites scenario due
to the higher maximum difference between the computing powers of the nodes.
In fact, high differences between the nodes' computing powers make the proposed frequencies selecting
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*}[t]
+ \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 trade-off 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\% 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 contrary 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.
The rest of the benchmarks showed different performance degradation percentages, which decrease
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 comparing to the two sites scenario because the former has high speed local communications
+Figure \ref{fig:dist} presents the distance percentage between the energy saving and the performance degradation for each benchmark over both scenarios. The trade-off distance percentage can be
+computed as in Equation~\ref{eq:max}. The one site scenario with 16 nodes gives the best energy and performance
+trade-off, on average it is equal to 26.8\%. The one site scenario using both 16 and 32 nodes had better energy and performance
+trade-off 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:
+ Finally, the best energy and performance trade-off 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{Experiments with different static and dynamic powers consumption scenarios}
+\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 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 class D of the NAS parallel
+benchmarks over these two different scenarios are presented
+in 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=.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=.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}
+
+
+
+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.
+\begin{figure*}[t]
+ \centering
+ \subfloat[The energy saving of running NAS benchmarks over one core and multicores scenarios]{%
+ \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=.48\textwidth]{fig/per_d_mc.eps}\label{fig:per-d-mc}}\hspace{0.4cm}%
+ \subfloat[The trade-off distance of running NAS benchmarks over one core and multicores scenarios]{%
+ \includegraphics[width=.48\textwidth]{fig/dist_mc.eps}\label{fig:dist-mc}}
+ \label{fig:exp-res2}
+ \caption{The experimental results of one core and multi-cores scenarios}
+\end{figure*}
+
+The energy saving percentages of all NAS benchmarks running over these two scenarios are presented in 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 are 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 are lower because the computations to communications ratios are smaller than the ratios of the other scenario.
+
+The trade-off distances percentages of the NAS benchmarks over the two scenarios are presented
+in ~Figure~\ref{fig:dist-mc}. These trade-off distances 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 trade-off distance percentages are on average bigger over the multi-cores scenario (17.6\%) than over the one core per node scenario (15.3\%).
+
+
+
+
+
+
+
+\subsection{Experiments with different static power scenarios}
\label{sec.pow_sen}
-In section \ref{sec.grid5000}, since it was not possible to measure the static power consumed by a CPU, the static power was assumed to be equal to 20\% of the measured dynamic power. This power is consumed during the whole execution time, during computation and communication times. Therefore, when the DVFS operations are applied by the scaling algorithm and the CPUs' frequencies lowered, the execution time might increase and consequently the consumed static energy will be increased too.
+In Section~\ref{sec.grid5000}, since it was not possible to measure the static power consumed by a CPU, the static power was assumed to be equal to 20\% of the measured dynamic power. This power is consumed during the whole execution time, during computation and communication times. Therefore, when the DVFS operations are applied by the scaling algorithm and the CPUs' frequencies lowered, the execution time might increase and consequently the consumed static energy will be increased too.
The aim of this section is to evaluate the scaling algorithm while assuming different values of static powers.
In addition to the previously used percentage of static power, two new static power ratios, 10\% and 30\% of the measured dynamic power of the core, are used in this section.
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.
+In these experiments, class D of the NAS parallel benchmarks are executed over the Nancy 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 the nodes executing the NAS benchmarks over the three power scenarios}
- \label{fig:eng-pow}
-\end{figure}
-\begin{figure}
+\begin{figure*}[t]
\centering
- \includegraphics[scale=0.5]{fig/per_pow.eps}
- \caption{The performance degradation percentages for the NAS benchmarks over the three power scenarios}
- \label{fig:per-pow}
-\end{figure}
+ \subfloat[The energy saving percentages for the nodes executing the NAS benchmarks over the three power scenarios]{%
+ \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=.48\textwidth]{fig/per_pow.eps}\label{fig:per-pow}}\hspace{0.4cm}%
+ \subfloat[The trade-off distance between the energy reduction and the performance of the NAS benchmarks over the three power scenarios]{%
+
+ \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
- \includegraphics[scale=0.5]{fig/dist_pow.eps}
- \caption{The tradeoff distance between the energy reduction and the performance of the NAS benchmarks over the three power scenarios}
- \label{fig:dist-pow}
-\end{figure}
\begin{figure}
\centering
- \includegraphics[scale=0.47]{fig/three_scenarios.pdf}
+ \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}
The energy saving percentages of the NAS benchmarks with the three static power scenarios are presented
-in figure \ref{fig:eng_sen}. This figure shows that the 10\% of static power scenario
+in Figure\ref{fig:eng_sen}. This figure shows that the 10\% of static power scenario
gives the biggest energy saving percentages in comparison to the 20\% and 30\% static power
scenarios. The small value of the static power consumption makes the proposed
scaling algorithm select smaller frequencies for the CPUs.
These smaller frequencies reduce the dynamic energy consumption more than increasing the consumed static energy which gives less overall energy consumption.
The energy saving percentages of the 30\% static power scenario is the smallest between the other scenarios, because the scaling algorithm selects bigger frequencies for the CPUs which increases the energy consumption. Figure \ref{fig:fre-pow} demonstrates that the proposed scaling algorithm selects the best frequency scaling factors according to the static power consumption ratio being used.
-The performance degradation percentages are presented in the figure \ref{fig:per-pow}.
+The performance degradation percentages are presented in Figure\ref{fig:per-pow}.
The 30\% static power scenario had less performance degradation percentage because the scaling algorithm
had selected big frequencies for the CPUs. While,
-the inverse happens in the 10\% and 20\% scenarios because the scaling algorithm had selected CPUs' frequencies smaller than those of the 30\% scenario. The tradeoff distance percentage for the NAS benchmarks with these three static power scenarios
-are presented in the figure \ref{fig:dist}.
-It shows that the best tradeoff
+the inverse happens in the 10\% and 20\% scenarios because the scaling algorithm had selected CPUs' frequencies smaller than those of the 30\% scenario. The trade-off distance percentage for the NAS benchmarks with these three static power scenarios
+are presented in Figure~\ref{fig:dist}.
+It shows that the best trade-off
distance percentage is obtained with the 10\% static power scenario and this percentage
is decreased for the other two scenarios because the scaling algorithm had selected different frequencies according to the static power values.
-In the EP benchmark, the energy saving, performance degradation and tradeoff
-distance percentages for the these static power scenarios are not significantly different because there is no communication in this benchmark. Therefore, the static power is only consumed during computation and the proposed scaling algorithm selects similar frequencies for the three scenarios. On the other hand, for the rest of the benchmarks, the scaling algorithm selects the values of the frequencies according to the communication times of each benchmark because the static energy consumption increases proportionally to the communication times.
+In the EP benchmark, the energy saving, performance degradation and trade-off
+distance percentages for these static power scenarios are not significantly different because there is no communication in this benchmark. Therefore, the static power is only consumed during computation and the proposed scaling algorithm selects similar frequencies for the three scenarios. On the other hand, for the rest of the benchmarks, the scaling algorithm selects the values of the frequencies according to the communication times of each benchmark because the static energy consumption increases proportionally to the communication times.
-\subsection{The comparison of the proposed frequencies selecting algorithm }
+\subsection{Comparison of the proposed frequencies selecting algorithm }
\label{sec.compare_EDP}
-Finding the frequencies that gives the best tradeoff between the energy consumption and the performance for a parallel
+Finding the frequencies that give the best trade-off between the energy consumption and the performance for a parallel
application is not a trivial task. Many algorithms have been proposed to tackle this problem.
In this section, the proposed frequencies selecting algorithm is compared to a method that uses the well known energy and delay product objective function, $EDP=energy \times delay$, that has been used by many researchers \cite{EDP_for_multi_processors,Energy_aware_application_scheduling,Exploring_Energy_Performance_TradeOffs}.
This objective function was also used by Spiliopoulos et al. algorithm \cite{Spiliopoulos_Green.governors.Adaptive.DVFS} where they select the frequencies that minimize the EDP product and apply them with DVFS operations to the multi-cores
architecture. Their online algorithm predicts the energy consumption and execution time of a processor before using the EDP method.
-To fairly compare the proposed frequencies scaling algorithm to Spiliopoulos et al. algorithm, called Maxdist and EDP respectively, both algorithms use the same energy model, equation \ref{eq:energy} and
-execution time model, equation \ref{eq:perf}, to predict the energy consumption and the execution time for each computing node.
-Moreover, both algorithms start the search space from the upper bound computed as in equation \ref{eq:Fint}.
+To fairly compare the proposed frequencies scaling algorithm to Spiliopoulos et al. algorithm, called Maxdist and EDP respectively, both algorithms use the same energy model, Equation~\ref{eq:energy} and
+execution time model, Equation~\ref{eq:perf}, to predict the energy consumption and the execution time for each computing node.
+Moreover, both algorithms start the search space from the upper bound computed as in Equation~\ref{eq:Fint}.
Finally, the resulting EDP algorithm is an exhaustive search algorithm that tests all the possible frequencies, starting from the initial frequencies (upper bound),
and selects the vector of frequencies that minimize the EDP product.
-Both algorithms were applied to the class D of the NAS benchmarks over 16 nodes.
-The participating computing nodes are distributed according to the two scenarios described in section \ref{sec.res}.
-The experimental results, the energy saving, performance degradation and tradeoff distance percentages, are
-presented in the figures \ref{fig:edp-eng}, \ref{fig:edp-perf} and \ref{fig:edp-dist} respectively.
+Both algorithms were applied to class D of the NAS benchmarks over 16 nodes.
+The participating computing nodes are distributed according to the two scenarios described in Section~\ref{sec.res}.
+The experimental results, the energy saving, performance degradation and trade-off distance percentages, are
+presented in Figures~\ref{fig:edp-eng}, \ref{fig:edp-perf} and \ref{fig:edp-dist} respectively.
-\begin{figure}
- \centering
- \includegraphics[scale=0.5]{fig/edp_eng}
- \caption{The energy reduction induced by the Maxdist method and the EDP method}
- \label{fig:edp-eng}
-\end{figure}
-\begin{figure}
+\begin{figure*}[t]
\centering
- \includegraphics[scale=0.5]{fig/edp_per}
- \caption{The performance degradation induced by the Maxdist method and the EDP method}
- \label{fig:edp-perf}
-\end{figure}
-\begin{figure}
- \centering
- \includegraphics[scale=0.5]{fig/edp_dist}
- \caption{The tradeoff distance between the energy consumption reduction and the performance for the Maxdist method and the EDP method}
- \label{fig:edp-dist}
-\end{figure}
-
-
+ \subfloat[The energy reduction induced by the Maxdist method and the EDP method]{%
+ \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=.48\textwidth]{fig/edp_per}\label{fig:edp-perf}}\hspace{0.4cm}%
+ \subfloat[The trade-off distance between the energy consumption reduction and the performance for the Maxdist method and the EDP method]{%
+ \includegraphics[width=.48\textwidth]{fig/edp_dist}\label{fig:edp-dist}}
+ \label{fig:edp-comparison}
+ \caption{The comparison results}
+\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
maximizes the energy saving and the performance at the same time.
Moreover, the proposed scaling algorithm gives the same weight for these two metrics.
-Whereas, the EDP algorithm gives sometimes negative tradeoff values for some benchmarks in the two sites scenarios.
-These negative tradeoff values mean that the performance degradation percentage is higher than energy saving percentage.
-The high positive values of the tradeoff distance percentage mean that the energy saving percentage is much higher than the performance degradation percentage.
+Whereas, the EDP algorithm gives sometimes negative trade-off values for some benchmarks in the two sites scenarios.
+These negative trade-off values mean that the performance degradation percentage is higher than the energy saving percentage.
+The high positive values of the trade-off distance percentage mean that the energy saving percentage is much higher than the performance degradation percentage.
The time complexity of both Maxdist and EDP algorithms are $O(N \cdot M \cdot F)$ and
$O(N \cdot M \cdot F^2)$ respectively, where $N$ is the number of the clusters, $M$ is the number of nodes and $F$ is the
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.
+This paper presents a new online frequencies selection algorithm.
The algorithm selects the best vector of
-frequencies that maximizes the tradeoff distance
+frequencies that maximizes the trade-off distance
between the predicted energy consumption and the predicted execution time of the distributed
iterative applications running over a heterogeneous grid. A new energy model
is used by the proposed algorithm to predict the energy consumption
of the distributed iterative message passing application running over a grid architecture.
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
+ NAS parallel benchmarks and the class D instance was executed over the Grid'5000 testbed platform.
+ The experiments on 16 nodes, distributed over three clusters, showed that the algorithm on average reduces by 30\% the energy consumption
+for all the NAS benchmarks while on average only degrading by 3.2\% 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 use multi-cores per node architecture or consume different static power values. The algorithm selects different vectors 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
-that the proposed algorithm outperforms the latter by selecting a vector of frequencies that gives a better tradeoff between energy consumption reduction and performance.
+that the proposed algorithm outperforms the latter by selecting a vector of frequencies that gives a better trade-off between energy consumption reduction and performance.
In the near future, we would like to develop a similar method that is adapted to
asynchronous iterative applications where iterations are not synchronized and communications are overlapped with computations.
- The development of
-such a method might require a new energy model because the
+The development of such a method might require a new energy model because the
number of iterations is not known in advance and depends on
the global convergence of the iterative system.
Mr. Ahmed Fanfakh, would like to thank the University of Babylon (Iraq) for
supporting his work.
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