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+\usepackage{url}
+\DeclareUrlCommand\email{\urlstyle{same}}
+
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-\renewcommand*\npunitcommand[1]{\text{#1}}
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FEMTO-ST Institute\\
University of Franche-Comté\\
IUT de Belfort-Montbéliard, 19 avenue du Maréchal Juin, BP 527, 90016 Belfort cedex, France\\
- Fax : +33~3~84~58~77~32\\
- Email: \{jean-claude.charr,raphael.couturier,ahmed.fanfakh\_badri\_muslim,arnaud.giersch\}@univ-fcomte.fr
+ % 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}
}
}
\maketitle
-\AG{Is the fax number correct? Shall we add a telephone number?}
\begin{abstract}
Dynamic Voltage Frequency Scaling (DVFS) can be applied to modern CPUs.
This technique is usually used to reduce the energy consumed by a CPU while
-computing . Indeed, power consumption by a processor at a given instant is
+computing. Indeed, power consumption by a processor at a given instant is
exponentially related to its frequency. Thus, decreasing the frequency reduces
the power consumed by the CPU. However, it can also significantly affect the
-performance of the executed program if it is compute bound and a low CPU
+performance of the executed program if it is compute bound and if a low CPU
frequency is selected. The performance degradation ratio can even be higher than
the saved energy ratio. Therefore, the chosen scaling factor must give the best possible trade-off
between energy reduction and performance.
constructors have been regularly increasing the number of computing cores and processors in
supercomputers (for example in November 2013, according to the TOP500
list~\cite{43}, the Tianhe-2 was the fastest supercomputer. It has more than 3
-millions of cores and delivers more than 33 Tflop/s while consuming 17808
-kW). This large increase in number of computing cores has led to large energy
+millions of cores and delivers more than \np[Tflop/s]{33} while consuming
+\np[kW]{17808}). This large increase in number of computing cores has led to large energy
consumption by these architectures. Moreover, the price of energy is expected to
continue its ascent according to the demand. For all these reasons energy
reduction became an important topic in the high performance computing field. To
proposed algorithm with Rauber and Rünger methods~\cite{3}.
The comparison's results show that our algorithm gives better energy-time trade-off.
-This paper is organized as follows: Section~\ref{sec.relwork} presents related works
-from other authors. Section~\ref{sec.exe} shows the execution of parallel
-tasks and sources of idle times. It resumes the energy
-model of homogeneous platform. Section~\ref{sec.mpip} evaluates the performance
-of MPI program. Section~\ref{sec.compet} presents the energy-performance trade-offs
-objective function. Section~\ref{sec.optim} demonstrates the proposed energy-performance algorithm. Section~\ref{sec.expe} verifies the performance prediction
-model and presents the results of the proposed algorithm. Also, It shows the comparison results. Finally,
-we conclude in Section~\ref{sec.concl}.
+This paper is organized as follows: Section~\ref{sec.relwork} presents some related works
+from other authors. Section~\ref{sec.exe} explains the execution of parallel
+tasks and the sources of slack times. It also presents an energy
+model for homogeneous platforms. Section~\ref{sec.mpip} describes how the performance
+of MPI programs can be predicted. Section~\ref{sec.compet} presents the energy-performance
+objective function that maximizes the reduction of energy consumption while minimizing the degradation of the program's performance. Section~\ref{sec.optim} details the proposed energy-performance algorithm. Section~\ref{sec.expe} verifies the accuracy of the performance prediction
+model and presents the results of the proposed algorithm. It also shows the comparison results between our method and other existing methods. Finally,
+we conclude in Section~\ref{sec.concl} with a summary and some future works.
\section{Related works}
\label{sec.relwork}
-\AG{Consider introducing the models (sec.~\ref{sec.exe}) before related works}
In this section, some heuristics to compute the scaling factor are
presented and classified into two categories: offline and online methods.
participating in the execution of the parallel program. On one hand, the scaling
factor
values could be computed based on information retrieved by analyzing the code of
-the program and the computing system that will execute it. In ~\cite{40},
+the program and the computing system that will execute it. In~\cite{40},
Azevedo et
al. detect during compilation the dependency points between
-tasks in a parallel program. This information is then used to lower the frequency of
+tasks in a multi-task program. This information is then used to lower the frequency of
some processors in order to eliminate slack times. A slack time is the period of time during which a processor that have already finished its computation, have to wait
for a set of processors to finish their computations and send their results to the
waiting processor in order to continue its task that is
dependent on the results of computations being executed on other processors.
-Freeh et al. showed in ~\cite{17} that the
+Freeh et al. showed in~\cite{17} that the
communication times of MPI programs do not change when the frequency is scaled down.
On the other hand, some offline scaling factor selection methods use the
information gathered from previous full or
The main drawback for these methods is that they all require executing a part or the whole program on all frequency gears for each new instance of the same program.
\subsection{Online scaling factor selection methods}
-The online scaling factor selection methods are executed during the runtime of the program. They are usually integrated into iterative programs where the same block of instructions is executed many times. During the first few iterations, many informations are measured such as the execution time, the energy consumed using a multimeter, the slack times, ... Then a method will exploit these measurements to compute the scaling factor values for each processor. This operation, measurements and computing new scaling factor, can be repeated as much as needed if the iterations are not regular. Kimura, Peraza, Yu-Liang et al. ~\cite{11,2,31} used learning methods to select the appropriate scaling factor values to eliminate the slack times during runtime. However, as seen in ~\cite{39,19}, machine learning methods can take a lot of time to converge when the number of available gears is big. To reduce the impact of slack times, in~\cite{1}, Lim et al. developed an algorithm that detects the
+The online scaling factor selection methods are executed during the runtime of the program. They are usually integrated into iterative programs where the same block of instructions is executed many times. During the first few iterations, many informations are measured such as the execution time, the energy consumed using a multimeter, the slack times, \dots{} Then a method will exploit these measurements to compute the scaling factor values for each processor. This operation, measurements and computing new scaling factor, can be repeated as much as needed if the iterations are not regular. Kimura, Peraza, Yu-Liang et al.~\cite{11,2,31} used learning methods to select the appropriate scaling factor values to eliminate the slack times during runtime. However, as seen in~\cite{39,19}, machine learning methods can take a lot of time to converge when the number of available gears is big. To reduce the impact of slack times, in~\cite{1}, Lim et al. developed an algorithm that detects the
communication sections and changes the frequency during these sections
only. This approach might change the frequency of each processor many times per iteration if an iteration
-contains more than one communication section. In ~\cite{3}, Rauber and Rünger used an analytical model that after measuring the energy consumed and the execution time with the highest frequency gear, it can predict the energy consumed and the execution time for every frequency gear . These predictions may be used to choose the optimal gear for each processor executing the parallel program to reduce energy consumption.
+contains more than one communication section. In~\cite{3}, Rauber and Rünger used an analytical model that can predict the consumed energy and the execution time for every frequency gear after measuring the consumed energy and the execution time with the highest frequency gear. These predictions may be used to choose the optimal gear for each processor executing the parallel program to reduce energy consumption.
To maintain the performance of the parallel program , they
set the processor with the biggest load to the highest gear and then compute the scaling factor values for the rest of the processors. Although this model was built for parallel architectures, it can be adapted to distributed architectures by taking into account the communications.
-The primary contribution of this paper is presenting a new online scaling factor selection method which has the following characteristics :
+The primary contribution of our paper is presenting a new online scaling factor selection method which has the following characteristics:
\begin{enumerate}
-\item Based on Rauber's analytical model to predict the energy consumption and the execution time of the application with different frequency gears.
-\item Selects the frequency scaling factor for simultaneously optimizing energy reduction and maintaining performance.
-\item Well adapted to distributed architectures because it takes into account the communication time.
-\item Well adapted to distributed applications with imbalanced tasks.
-\item Has very small footprint when compared to other
+\item It is based on Rauber and Rünger analytical model to predict the energy consumption of the application with different frequency gears.
+\item It selects the frequency scaling factor for simultaneously optimizing energy reduction and maintaining performance.
+\item It is well adapted to distributed architectures because it takes into account the communication time.
+\item It is well adapted to distributed applications with imbalanced tasks.
+\item it has very small footprint when compared to other
methods (e.g.,~\cite{19}) and does not require profiling or training as
in~\cite{38,34}.
\end{enumerate}
platform. These tasks can exchange the data via synchronous message passing.
\begin{figure*}[t]
\centering
- \subfloat[Sync. imbalanced communications]{\includegraphics[scale=0.67]{commtasks}\label{fig:h1}}
- \subfloat[Sync. imbalanced computations]{\includegraphics[scale=0.67]{compt}\label{fig:h2}}
+ \subfloat[Sync. imbalanced communications]{\includegraphics[scale=0.67]{fig/commtasks}\label{fig:h1}}
+ \subfloat[Sync. imbalanced computations]{\includegraphics[scale=0.67]{fig/compt}\label{fig:h2}}
\caption{Parallel tasks on homogeneous platform}
\label{fig:homo}
\end{figure*}
lead to slack times while tasks wait at the synchronization barrier for other tasks to
finish their tasks (see figure~(\ref{fig:h1})). The imbalanced communications
happen when nodes have to send/receive different amount of data or they communicate
-with different number of nodes. Another source of idle times is the imbalanced computations.
+with different number of nodes. Another source of slack times is the imbalanced computations.
This happens when processing different amounts of data on each processor (see figure~(\ref{fig:h2})).
In this case the fastest tasks have to wait at the synchronization barrier for the
slowest ones to begin the next task. In both cases the overall execution time
\end{equation}
where V is the supply voltage, $N_{trans}$ is the number of transistors, $K_{design}$ is a
design dependent parameter and $I_{leak}$ is a technology-dependent
-parameter. Energy consumed by an individual processor $E_{ind}$ is the summation
-of the dynamic and the static powers multiplied by the execution time~\cite{36,15}.
+parameter. The energy consumed by an individual processor to execute a given program can be computed as:
\begin{equation}
\label{eq:eind}
- E_\textit{ind} = ( P_\textit{dyn} + P_\textit{static} ) \cdot T
+ E_\textit{ind} = P_\textit{dyn} \cdot T_{Comp} + P_\textit{static} \cdot T
\end{equation}
+where $T$ is the execution time of the program, $T_{Comp}$ is the computation time and $T_{Comp} \leq T$. $T_{Comp}$ may be equal to $T$ if there is no communications, no slack times and no synchronizations.
+
DVFS is a process that is allowed in
modern processors to reduce the dynamic power by scaling down the voltage and
frequency. Its main objective is to reduce the overall energy
-consumption~\cite{37}. The operational frequency \emph f depends linearly on the
+consumption~\cite{37}. 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 dynamic voltage with respect to
various frequency values in~\cite{3}. The reduction process of the frequency can be
-expressed by the scaling factor \emph S which is the ratio between the
+expressed by the scaling factor $S$ which is the ratio between the
maximum and the new frequency as in EQ~(\ref{eq:s}).
\begin{equation}
\label{eq:s}
S = \frac{F_\textit{max}}{F_\textit{new}}
\end{equation}
The value of the scaling factor $S$ is greater than 1 when changing the frequency of the CPU to any
-new frequency value~(\emph {P-state}) in the governor. The CPU governor is an
+new frequency value~(\emph{P-state}) in the governor. The CPU governor is an
interface driver supplied by the operating system's kernel to
lower a core's frequency. This factor reduces
quadratically the dynamic power which may cause degradation in performance and thus, the increase of the static energy because the execution time is increased~\cite{36}. If the tasks are sorted according to their execution times before scaling in a descending order, the total energy consumption model for a parallel
-homogeneous platform, as presented by Rauber et al.~\cite{3}, can be written as a function of the scaling factor \emph S, as in EQ~(\ref{eq:energy}).
+homogeneous platform, as presented by Rauber and Rünger~\cite{3}, can be written as a function of the scaling factor $S$, as in EQ~(\ref{eq:energy}).
\begin{equation}
\label{eq:energy}
P_\textit{static} \cdot T_1 \cdot S_1 \cdot N
\hfill
\end{equation}
-where \emph N is the number of parallel nodes, $T_i \ and \ S_i \ for \ i=1,...,N$ are the execution times and scaling factors of the sorted tasks. Therefore, $T1$ is the time of the slowest task, and $S_1$ its scaling factor which should be the highest because they are proportional to
+where $N$ is the number of parallel nodes, $T_i$ and $S_i$ for $i=1,\dots,N$ are the execution times and scaling factors of the sorted tasks. Therefore, $T1$ is the time of the slowest task, and $S_1$ its scaling factor which should be the highest because they are proportional to
the time values $T_i$. The scaling factors are computed as in EQ~(\ref{eq:si}).
\begin{equation}
\label{eq:si}
\left( 1 + \sum_{i=2}^{N} \frac{T_i^3}{T_1^3} \right) }
\end{equation}
-\JC{The following 2 sections can be merged easily}
\section{Performance evaluation of MPI programs}
\label{sec.mpip}
performance curves are not in the same direction see figure~(\ref{fig:r2}).
While the main goal is to optimize the energy and performance in the same
time. According to the equations~(\ref{eq:enorm}) and~(\ref{eq:pnorm}), the
-scaling factor \emph S reduce both the energy and the performance
+scaling factor $S$ reduce both the energy and the performance
simultaneously. But the main objective is to produce maximum energy reduction
with minimum performance reduction. Many researchers used different strategies
to solve this nonlinear problem for example see~\cite{19,42}, their methods add
big overhead to the algorithm for selecting the suitable frequency. In this
-paper we present a method to find the optimal scaling factor \emph S for
+paper we present a method to find the optimal scaling factor $S$ for
optimizing both energy and performance simultaneously without adding big
overheads. Our solution for this problem is to make the optimization process
have the same direction. Therefore, we inverse the equation of normalize
\begin{equation}
\label{eq:pnorm_en}
P^{-1}_\textit{Norm} = \frac{ T_\textit{Old}}{ T_\textit{New}}
- = \frac{ T_\textit{Old}}{T_\textit{Max Comp Old} \cdot S +
+ = \frac{T_\textit{Max Comp Old} +
+ T_\textit{Max Comm Old}}{T_\textit{Max Comp Old} \cdot S +
T_\textit{Max Comm Old}}
\end{equation}
\begin{figure*}
\centering
\subfloat[Converted relation.]{%
- \includegraphics[width=.4\textwidth]{file.eps}\label{fig:r1}}%
+ \includegraphics[width=.4\textwidth]{fig/file}\label{fig:r1}}%
\qquad%
\subfloat[Real relation.]{%
- \includegraphics[width=.4\textwidth]{file3.eps}\label{fig:r2}}
+ \includegraphics[width=.4\textwidth]{fig/file3}\label{fig:r2}}
\label{fig:rel}
\caption{The energy and performance relation}
\end{figure*}
Then, we can modelize our objective function as finding the maximum distance
between the energy curve EQ~(\ref{eq:enorm}) and the inverse of performance
-curve EQ~(\ref{eq:pnorm_en}) over all available scaling factors. This represent
+curve EQ~(\ref{eq:pnorm_en}) over all available scaling factors. This represents
the minimum energy consumption with minimum execution time (better performance)
at the same time, see figure~(\ref{fig:r1}). Then our objective function has the
following form:
Max Dist = \max_{j=1,2,\dots,F} (\overbrace{P^{-1}_\textit{Norm}(S_j)}^{\text{Maximize}} -
\overbrace{E_\textit{Norm}(S_j)}^{\text{Minimize}} )
\end{equation}
-where F is the number of available frequencies. Then we can select the optimal scaling factor that satisfy
+where $F$ is the number of available frequencies. Then we can select the optimal scaling factor that satisfies
EQ~(\ref{eq:max}). Our objective function can work with any energy model or
static power values stored in a data file. Moreover, this function works in
optimal way when the energy curve has a convex form over the available frequency scaling
\section{Optimal scaling factor for performance and energy}
\label{sec.optim}
- Algorithm~\ref{EPSA} compute the optimal scaling factor according to the objective function described above.
+ Algorithm~\ref{EPSA} computes the optimal scaling factor according to the objective function described above.
\begin{algorithm}[tp]
\caption{Scaling factor selection algorithm}
\label{EPSA}
\State Set the variable $F_{new}$ to max. frequency, $F_{new} = F_{max} $
\State Set the variable $F_{diff}$ to the difference between two successive frequencies.
\For {$j:=1$ to $P_{states} $}
- \State - $F_{new}=F_{new} - F_{diff} $
- \State - $S = \frac{F_\textit{max}}{F_\textit{new}}$
- \State - $S_i = S \cdot \frac{T_1}{T_i}= \frac{F_\textit{max}}{F_\textit{new}} \cdot \frac{T_1}{T_i} \
- for \ i=1,...,N$
- \State - $E_\textit{Norm} = \frac{P_\textit{dyn} \cdot S_1^{-2} \cdot
+ \State $F_{new}=F_{new} - F_{diff} $
+ \State $S = \frac{F_\textit{max}}{F_\textit{new}}$
+ \State $S_i = S \cdot \frac{T_1}{T_i}= \frac{F_\textit{max}}{F_\textit{new}} \cdot \frac{T_1}{T_i}$ for $i=1,\dots,N$
+ \State $E_\textit{Norm} = \frac{P_\textit{dyn} \cdot S_1^{-2} \cdot
\left( T_1 + \sum_{i=2}^{N}\frac{T_i^3}{T_1^2}\right) +
P_\textit{static} \cdot T_1 \cdot S_1 \cdot N }{
P_\textit{dyn} \cdot \left(T_1+\sum_{i=2}^{N}\frac{T_i^3}{T_1^2}\right) +
P_\textit{static} \cdot T_1 \cdot N }$
- \State - $P_{NormInv}=T_{old}/T_{new}$
- \If{ $(P_{NormInv}-E_{Norm} > Dist$) }
+ \State $P_{NormInv}=T_{old}/T_{new}$
+ \If{$(P_{NormInv}-E_{Norm} > Dist)$}
\State $S_{opt} = S$
\State $Dist = P_{NormInv} - E_{Norm}$
\EndIf
The proposed algorithm works online during the execution time of the MPI
program. It selects the optimal scaling factor after gathering the computation and communication times
-from the program after one iteration. Then the program changes the new frequencies of the CPUs according to the computed scaling factors. This algorithm has small execution time
-(between 0.00152 $ms$ for 4 nodes to 0.00665 $ms$ for 32 nodes). The algorithm complexity is O(F$\cdot$N),
-where F is the number of available frequencies and N is the number of computing nodes. The algorithm is called just
+from the program after one iteration. Then the program changes the new frequencies of the CPUs according to the computed scaling factors. This algorithm has a small execution time: for a homogeneous cluster composed of nodes having the characteristics presented in table~\ref{table:platform}, it takes \np[ms]{0.00152} on average for 4 nodes and \np[ms]{0.00665} on average for 32 nodes. The algorithm complexity is $O(F\cdot N)$,
+where $F$ is the number of available frequencies and $N$ is the number of computing nodes. The algorithm is called just
once during the execution of the program. The DVFS algorithm~(\ref{dvfs}) shows where and when the algorithm is called
in the MPI program.
+\begin{table}[htb]
+ \caption{Platform file parameters}
+ % title of Table
+ \centering
+ \begin{tabular}{|*{7}{l|}}
+ \hline
+ Max & Min & Backbone & Backbone&Link &Link& Sharing \\
+ Freq. & Freq. & Bandwidth & Latency & Bandwidth& Latency&Policy \\ \hline
+ \np{2.5} & \np{800} & \np[GBps]{2.25} &\np[$\mu$s]{0.5}& \np[GBps]{1} & \np[$\mu$s]{50} &Full \\
+ GHz& MHz& & & & &Duplex \\\hline
+ \end{tabular}
+ \label{table:platform}
+\end{table}
+
%\begin{minipage}{\textwidth}
\begin{algorithm}[tp]
\caption{DVFS}
\label{dvfs}
\begin{algorithmic}[1]
- \For {$k:=1$ to $Some-Iterations \; $}
- \State -Computations Section.
- \State -Communications Section.
+ \For {$k:=1$ to \textit{some iterations}}
+ \State Computations section.
+ \State Communications section.
\If {$(k=1)$}
- \State -Gather all times of computation and\par\hspace{13 pt} communication from each node.
- \State -Call algorithm~\ref{EPSA} with these times.
- \State -Compute the new frequency from the \par\hspace{13 pt} returned optimal scaling factor.
- \State -Set the new frequency to the CPU.
+ \State Gather all times of computation and\newline\hspace*{3em}%
+ communication from each node.
+ \State Call algorithm~\ref{EPSA} with these times.
+ \State Compute the new frequency from the\newline\hspace*{3em}%
+ returned optimal scaling factor.
+ \State Set the new frequency to the CPU.
\EndIf
\EndFor
\end{algorithmic}
detailed characteristics of our platform file are shown in the
table~(\ref{table:platform}).
Each node in the cluster has 18 frequency values
-from 2.5 GHz to 800 MHz with 100 MHz difference between each two successive
-frequencies. The simulated network link is 1 GB Ethernet (TCP/IP).
+from \np[GHz]{2.5} to \np[MHz]{800} with \np[MHz]{100} difference between each two successive
+frequencies. The simulated network link is \np[GB]{1} Ethernet (TCP/IP).
The backbone of the cluster simulates a high performance switch.
-\begin{table}[htb]
- \caption{Platform file parameters}
- % title of Table
- \centering
- \begin{tabular}{|*{7}{l|}}
- \hline
- Max & Min & Backbone & Backbone&Link &Link& Sharing \\
- Freq. & Freq. & Bandwidth & Latency & Bandwidth& Latency&Policy \\ \hline
- \np{2.5} & \np{800} & \np[GBps]{2.25} &\np[$\mu$s]{0.5}& \np[GBps]{1} & \np[$\mu$s]{50} &Full \\
- GHz& MHz& & & & &Duplex \\\hline
- \end{tabular}
- \label{table:platform}
-\end{table}
+
\subsection{Performance prediction verification}
In this section we evaluate the precision of our performance prediction method based on EQ~(\ref{eq:tnew}) by applying it the NAS benchmarks. The NAS programs are executed with the class B option for comparing the
frequency by the new one see EQ~(\ref{eq:s}).
\begin{figure*}[t]
\centering
- \includegraphics[width=.328\textwidth]{cg_per.eps}\hfill%
- \includegraphics[width=.328\textwidth]{mg_pre.eps}\hfill%
- % \includegraphics[width=.4\textwidth]{bt_pre.eps}\qquad%
- \includegraphics[width=.328\textwidth]{lu_pre.eps}\hfill%
+ \includegraphics[width=.328\textwidth]{fig/cg_per}\hfill%
+ \includegraphics[width=.328\textwidth]{fig/mg_pre}\hfill%
+ % \includegraphics[width=.4\textwidth]{fig/bt_pre}\qquad%
+ \includegraphics[width=.328\textwidth]{fig/lu_pre}\hfill%
\caption{Comparing predicted to real execution time}
\label{fig:pred}
\end{figure*}
%see Figure~\ref{fig:pred}
In our cluster there are 18 available frequency states for each processor.
-This lead to 18 run states for each program. We use seven MPI programs of the
+This leads to 18 run states for each program. We use seven MPI programs of the
NAS parallel benchmarks: CG, MG, EP, FT, BT, LU
-and SP. Figure~(\ref{fig:pred}) presents plots of the real execution times and the simulated ones. The maximum normalized error between the predicted execution time and the real time (SimGrid time) for all programs is between 0.0073 to 0.031. The better case is for CG and the worse case is for LU.
+and SP. Figure~(\ref{fig:pred}) presents plots of the real execution times and the simulated ones. The maximum normalized error between these two execution times varies between \np{0.0073}\AG[]{unit?} to \np{0.031} dependent on the executed benchmark. The smallest prediction error was for CG and the worst one was for LU.
\subsection{The experimental results for the scaling algorithm }
The proposed algorithm was applied to seven MPI programs of the NAS
benchmarks (EP, CG, MG, FT, BT, LU and SP) which were run with three classes (A, B and
Depending on EQ~(\ref{eq:energy}), we measure the energy consumption for all
the NAS MPI programs while assuming the power dynamic with the highest frequency is equal to \np[W]{20} and
the power static is equal to \np[W]{4} for all experiments. These power values were also
-used by Rauber and Rünger in~\cite{3}. The results showed that the algorithm selected
+used by Rauber and Rünger in~\cite{3}. The results showed that the algorithm selected
different scaling factors for each program depending on the communication
features of the program as in the plots~(\ref{fig:nas}). These plots illustrate that
there are different distances between the normalized energy and the normalized
same time from all available scaling factors.
\begin{figure*}[t]
\centering
- \includegraphics[width=.328\textwidth]{ep.eps}\hfill%
- \includegraphics[width=.328\textwidth]{cg.eps}\hfill%
- \includegraphics[width=.328\textwidth]{sp.eps}
- \includegraphics[width=.328\textwidth]{lu.eps}\hfill%
- \includegraphics[width=.328\textwidth]{bt.eps}\hfill%
- \includegraphics[width=.328\textwidth]{ft.eps}
+ \includegraphics[width=.328\textwidth]{fig/ep}\hfill%
+ \includegraphics[width=.328\textwidth]{fig/cg}\hfill%
+ \includegraphics[width=.328\textwidth]{fig/sp}
+ \includegraphics[width=.328\textwidth]{fig/lu}\hfill%
+ \includegraphics[width=.328\textwidth]{fig/bt}\hfill%
+ \includegraphics[width=.328\textwidth]{fig/ft}
\caption{Optimal scaling factors for the predicted energy and performance of NAS benchmarks}
\label{fig:nas}
\end{figure*}
refer to this scenario as $R_{E}$. The second scenario is similar to the first
except setting the slower task to the maximum frequency (when the scale $S=1$)
to keep the performance from degradation as mush as possible. We refer to this
-scenario as $R_{E-P}$. While we refer to our algorithm as EPSA. The comparison
+scenario as $R_{E-P}$. While we refer to our algorithm as EPSA (Energy to Performance Scaling Algorithm). The comparison
is made in tables \ref{table:compareA}, \ref{table:compareB},
and~\ref{table:compareC}. These
tables show the results of our method and Rauber and Rünger scenarios for all the
-NAS benchmarks programs for classes A,B and C.
+NAS benchmarks programs for classes A, B and C.
\begin{table}[p]
\caption{Comparing results for the NAS class A}
% title of Table
\label{table:compareC}
% is used to refer this table in the text
\end{table}
-As shown in tables~\ref{table:compareA},~\ref{table:compareB} and~\ref{table:compareC}, the ($R_{E-P}$) method outperforms the ($R_{E}$) method in terms of performance and energy reduction. The ($R_{E-P}$) method also gives better energy savings than our method. However, although our scaling factor is not optimal for energy reduction, the results in these tables prove that our algorithm returns the best scaling factor that satisfy our objective method : the largest distance between energy reduction and performance degradation.
-
-Figure~(\ref{fig:compare}) shows the maximum distance between the energy saving
-percent and the performance degradation percent.
-Negative values mean that one of the two objectives (energy or performance) have been degraded more than the other. The positive trade-offs with the highest values lead to maximum energy savings
+As shown in tables~\ref{table:compareA},~\ref{table:compareB} and~\ref{table:compareC}, the ($R_{E-P}$) method outperforms the ($R_{E}$) method in terms of performance and energy reduction. The ($R_{E-P}$) method also gives better energy savings than our method. However, although our scaling factor is not optimal for energy reduction, the results in these tables prove that our algorithm returns the best scaling factor that satisfy our objective method: the largest distance between energy reduction and performance degradation. Negative values in the energy-performance column mean that one of the two objectives (energy or performance) have been degraded more than the other. The positive trade-offs with the highest values lead to maximum energy savings
while keeping the performance degradation as low as possible. Our algorithm always
gives the highest positive energy to performance trade-offs while Rauber and Rünger method
($R_{E-P}$) gives in some time negative trade-offs such as in BT and
EP.
-\begin{figure*}[t]
- \centering
- \includegraphics[width=.328\textwidth]{compare_class_A.pdf}
- \includegraphics[width=.328\textwidth]{compare_class_B.pdf}
- \includegraphics[width=.328\textwidth]{compare_class_c.pdf}
- \caption{Comparing our method to Rauber and Rünger methods}
- \label{fig:compare}
-\end{figure*}
+%\begin{figure*}[t]
+% \centering
+% \includegraphics[width=.328\textwidth]{fig/compare_class_A}
+% \includegraphics[width=.328\textwidth]{fig/compare_class_B}
+% \includegraphics[width=.328\textwidth]{fig/compare_class_C}
+% \caption{Comparing our method to Rauber and Rünger methods}
+% \label{fig:compare}
+%\end{figure*}
\section{Conclusion}
\label{sec.concl}
In this paper, we have presented a new online scaling factor selection method that optimizes simultaneously the energy and performance of a distributed application running on an homogeneous cluster. It uses the computation and communication times measured at the first iteration to predict energy consumption and the performance of the parallel application at every available frequency. Then, it selects the scaling factor that gives the best trade-off between energy reduction and performance which is the maximum distance between the energy and the inverted performance curves. To evaluate this method, we have applied it to the NAS benchmarks and it was compared to Rauber and Rünger methods while being executed on the simulator SimGrid. The results showed that our method, outperforms Rauber and Rünger methods in terms of energy-performance ratio.
\section*{Acknowledgment}
-\AG{Jean-Claude, why did you remove the Mésocentre here?}
-As a PhD student, M. Ahmed Fanfakh, would like to thank the University of
+This work has been supported by the Labex ACTION project (contract ``ANR-11-LABX-01-01''). Computations have been performed on the supercomputer facilities of the
+Mésocentre de calcul de Franche-Comté. As a PhD student, Mr. Ahmed Fanfakh, would like to thank the University of
Babylon (Iraq) for supporting his work.
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