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\begin{document}

\title{Optimal Dynamic Frequency Scaling for Energy-Performance of Parallel MPI Programs}

\author{%
  \IEEEauthorblockN{%
    Jean-Claude Charr,
    Raphaël Couturier,
    Ahmed Fanfakh and
    Arnaud Giersch
  }
  \IEEEauthorblockA{%
    FEMTO-ST Institute\\
    University of Franche-Comté
  }
}

\maketitle

\AG{``Optimal'' is a bit pretentious in the title.\\
  Complete affiliation, add an email address, etc.}

\begin{abstract}
  The important technique for energy reduction of parallel systems is CPU
  frequency scaling. This operation is used by many researchers to reduce energy
  consumption in many ways. Frequency scaling operation also has a big impact on
  the performances. In some cases, the performance degradation ratio is bigger
  than energy saving ratio when the frequency is scaled to low level. Therefore,
  the trade offs between the energy and performance becomes more important topic
  when using this technique. In this paper we developed an algorithm that select
  the frequency scaling factor for both energy and performance simultaneously.
  This algorithm takes into account the communication times when selecting the
  frequency scaling factor. It works online without training or profiling to
  have a very small overhead.  The algorithm has better energy-performance trade
  offs compared to other methods.
\end{abstract}

\section{Introduction}
\label{sec.intro}

The need for computing power is still increasing and it is not expected to slow
down in the coming years. To satisfy this demand, researchers and supercomputers
constructors have been regularly increasing the number of computing cores 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
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
tackle this problem, many researchers used DVFS (Dynamic Voltage Frequency
Scaling) operations which reduce dynamically the frequency and voltage of cores
and thus their energy consumption. However, this operation also degrades the
performance of computation. Therefore researchers try to reduce the frequency to
minimum when processors are idle (waiting for data from other processors or
communicating with other processors). Moreover, depending on their objectives
they use heuristics to find the best scaling factor during the computation. If
they aim for performance they choose the best scaling factor that reduces the
consumed energy while affecting as little as possible the performance. On the
other hand, if they aim for energy reduction, the chosen scaling factor must
produce the most energy efficient execution without considering the degradation
of the performance. It is important to notice that lowering the frequency to
minimum value does not always give the most efficient execution due to energy
leakage. The best scaling factor might be chosen during execution (online) or
during a pre-execution phase.  In this paper we emphasize to develop an
algorithm that selects a frequency scaling factor that simultaneously takes into
consideration  the energy consumption and the performance. The
main objective of HPC systems is to run the application with less execution
time. Therefore, our algorithm selects the scaling factor online with
very small footprint. The proposed algorithm takes into account the
communication times of the MPI program to choose the scaling factor. This
algorithm has ability to predict both energy consumption and execution time over
all available scaling factors.  The prediction achieved depends on some
computing time information, gathered at the beginning of the runtime.  We apply
this algorithm to seven MPI benchmarks. These MPI programs are the NAS parallel
benchmarks (NPB v3.3) developed by NASA~\cite{44}. Our experiments are executed
using the simulator SimGrid/SMPI v3.10~\cite{Casanova:2008:SGF:1397760.1398183}
over an homogeneous distributed memory architecture. Furthermore, we compare the
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 the 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}.
\section{Related Works}
\label{sec.relwork}

\AG{Consider introducing the models sec.~\ref{sec.exe} maybe before related works}

In the this section some heuristics to compute the scaling factor are
presented and classified in two parts: offline and online methods.

\subsection{The offline DVFS orientations}

The DVFS offline methods are static and are not executed during the runtime of
the program. Some approaches used heuristics to select the best DVFS state
during the compilation phases as for example in Azevedo et al.~\cite{40}. They
use dynamic voltage scaling (DVS) algorithm to choose the DVS setting when there
are dependency points between tasks. While in~\cite{29}, Xie et al. used
breadth-first search algorithm to do that. Their goal is to save energy with
time limits. Another approach gathers and stores the runtime information for
each DVFS state, then selects the suitable DVFS offline to optimize energy-time
trade offs. As an example, Rountree et al.~\cite{8} use liner programming
algorithm, while in~\cite{38,34}, Cochran et al. use multi logistic regression
algorithm for the same goal. The offline study that shows the DVFS impact on the
communication time of the MPI program is~\cite{17}, where Freeh et al. show that
these times do not change when the frequency is scaled down.

\subsection{The online DVFS orientations}

The objective of the online DVFS orientations is to dynamically compute and set
the frequency of the CPU for saving energy during the runtime of the
programs. Estimating and predicting approaches for the energy-time trade offs
are developed by Kimura, Peraza, Yu-Liang et al.  ~\cite{11,2,31}. These works
select the best DVFS setting depending on the slack times. These times happen
when the processors have to wait for data from other processors to compute their
task. For example, during the synchronous communications that take place in MPI
programs, some processors are idle. The optimal DVFS can be selected using
learning methods. Therefore, in Dhiman, Hao Shen et al.  ~\cite{39,19} used
machine learning to converge to the suitable DVFS configuration. Their learning
algorithms take big time to converge when the number of available frequencies is
high. Also, the communication sections of the MPI program can be used to save
energy. In~\cite{1}, Lim et al. developed an algorithm that detects the
communication sections and changes the frequency during these sections
only. This approach changes the frequency many times because an iteration may
contain more than one communication section. The domain of analytical modeling
can also be used for choosing the optimal frequency as in Rauber and
Rünger~\cite{3}. They developed an analytical mathematical model to determine
the optimal frequency scaling factor for any number of concurrent tasks. They
set the slowest task to maximum frequency for maintaining performance.  In this
paper we compare our algorithm with Rauber and Rünger model~\cite{3}, because
their model can be used for any number of concurrent tasks for homogeneous
platforms. The primary contributions of this paper are:
\begin{enumerate}
\item Selecting the frequency scaling factor for simultaneously optimizing energy and performance,
   while taking into account the communication time.
\item Adapting our scaling factor to take into account the imbalanced tasks.
\item The execution time of our algorithm is very small when compared to other
  methods (e.g.,~\cite{19}).
\item The proposed algorithm works online without profiling or training as
  in~\cite{38,34}.
\end{enumerate}
\section{Execution and Energy of Parallel Tasks on Homogeneous Platform} 
\label{sec.exe}

\subsection{Parallel Tasks Execution on Homogeneous Platform}
A homogeneous cluster consists of identical nodes in terms of hardware and software. 
Each node has its own memory and at least one processor which can
be a multi-core. The nodes are connected via a high bandwidth network. Tasks
executed on this model can be either synchronous or asynchronous. In this paper
we consider execution of the synchronous tasks on distributed homogeneous
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}}
  \caption{Parallel Tasks on Homogeneous Platform}
  \label{fig:homo}
\end{figure*}
Therefore, the execution time of a task consists of the computation time and the
communication time. Moreover, the synchronous communications between tasks can
lead to idle time while tasks wait at the synchronization barrier for other tasks to
finish their communications (see figure~(\ref{fig:h1})). The imbalanced communications 
happen when nodes have to send/receive different amount of data or each node is communicates 
with different number of nodes. Another source for idle 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 tasks to finish their job. In both cases the overall execution time 
of the program is the execution time of the slowest task as: 
\begin{equation}
  \label{eq:T1}
  \textit{Program Time} = \max_{i=1,2,\dots,N} T_i
\end{equation}
where $T_i$ is the execution time of task $i$.

\subsection{Energy Model for Homogeneous Platform}

The energy consumption by the processor consists of two power metrics: the
dynamic and the static power. This general power formulation is used by many
researchers~\cite{9,3,15,26}. The dynamic power of the CMOS processors
$P_{dyn}$ is related to the switching activity $\alpha$, load capacitance $C_L$,
the supply voltage $V$ and operational frequency $f$ respectively as follow:
\begin{equation}
  \label{eq:pd}
  P_\textit{dyn} = \alpha \cdot C_L \cdot V^2 \cdot f
\end{equation}
The static power $P_{static}$ captures the leakage power consumption as well as
the power consumption of peripheral devices like the I/O subsystem.
\begin{equation}
  \label{eq:ps}
   P_\textit{static}  = V \cdot N \cdot K_{design} \cdot I_{leak}
\end{equation}
where V is the supply voltage, N 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 power multiplied by the execution time~\cite{36,15}.
\begin{equation}
  \label{eq:eind}
   E_\textit{ind} = ( P_\textit{dyn} + P_\textit{static} ) \cdot T
\end{equation}
The dynamic voltage and frequency scaling (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
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 are
expressed by the scaling factor \emph S. The scale \emph S 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 scale $S$ is greater than 1 when changing the frequency to any
new frequency value~(\emph {P-state}) in governor, the CPU governor is an
interface driver supplied by the operating system kernel (e.g. Linux) to
lowering core's frequency.  The scaling factor is equal to 1 when the frequency
set is to the maximum frequency.  The energy consumption model for parallel
homogeneous platform depends on the scaling factor \emph S. This factor reduces
quadratically the dynamic power.  Also, this factor increases the static energy
linearly because the execution time is increased~\cite{36}.  The energy model
depending on the frequency scaling factor for homogeneous platform for any
number of concurrent tasks was developed by Rauber and Rünger~\cite{3}. This
model considers the two power metrics for measuring the energy of the parallel
tasks as in EQ~(\ref{eq:energy}):
\begin{equation}
  \label{eq:energy}
  E = 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
 \hfill
\end{equation}
where \emph N is the number of parallel nodes, $T_1 $ is the time of the slowest
task, $T_i$ is the time of the task $i$ and $S_1$ is the maximum scaling factor
for the slower task. The scaling factor $S_1$, as in EQ~(\ref{eq:s1}), selects
from the set of scales values $S_i$. Each of these scales are proportional to
the time value $T_i$ depends on the new frequency value as in EQ~(\ref{eq:si}).
\begin{equation}
  \label{eq:s1}
  S_1 = \max_{i=1,2,\dots,F} S_i
\end{equation}
\begin{equation}
  \label{eq:si}
  S_i = S \cdot \frac{T_1}{T_i}
      = \frac{F_\textit{max}}{F_\textit{new}} \cdot \frac{T_1}{T_i}
\end{equation}
where $F$ is the number of available frequencies. In this paper we depend on
Rauber and Rünger energy model EQ~(\ref{eq:energy}) for two reasons: (1) this
model is used for homogeneous platform that we work on in this paper, and (2) we
compare our algorithm with Rauber and Rünger scaling model.  Rauber and Rünger
scaling factor that reduce energy consumption derived from the
EQ~(\ref{eq:energy}). They take the derivation for this equation (to be
minimized) and set it to zero to produce the scaling factor as in
EQ~(\ref{eq:sopt}).
\begin{equation}
  \label{eq:sopt}
  S_\textit{opt} = \sqrt[3]{\frac{2}{n} \cdot \frac{P_\textit{dyn}}{P_\textit{static}} \cdot
    \left( 1 + \sum_{i=2}^{N} \frac{T_i^3}{T_1^3} \right) }
\end{equation}

\section{Performance Evaluation of MPI Programs}
\label{sec.mpip}

The performance (execution time) of  parallel MPI applications depend on
the time of the slowest task as in figure~(\ref{fig:homo}). Normally the
execution time of the parallel programs are proportional to the operational
frequency. Therefore, any DVFS operation for the energy reduction increases the
execution time of the parallel program. As shown in EQ~(\ref{eq:energy}) the
energy is affected by the scaling factor $S$. This factor also has a great impact
on the performance. When scaling down the frequency to the new value according
to EQ~(\ref{eq:s}), the value of the scale $S$ has inverse relation with
new frequency value ($S \propto \frac{1}{F_{new}}$). Also when decreasing the
frequency value, the execution time increases. Then the new frequency value has
inverse relation with time ($F_{new} \propto \frac{1}{T}$). This leads to the
frequency scaling factor $S$ proportional linearly with execution time ($S
\propto T$). Large scale MPI applications such as NAS benchmarks have
considerable amount of communications embedded in these programs. During the
communication process the processors remain idle until the communication has
finished. For that reason any change in the frequency has no impact on the time
of communication but it has obvious impact on the time of
computation~\cite{17}. To predict the execution time of MPI program, the communication time and 
the computation time for the slower task must be first precisely specified. Secondly, 
these times are used to predict the execution time for any MPI program as a function of 
the new scaling factor as in EQ~(\ref{eq:tnew}).
\begin{equation}
  \label{eq:tnew}
 \textit  T_\textit{new} = T_\textit{Max Comp Old} \cdot S + T_{\textit{Max Comm Old}}
\end{equation}
The above equation shows that the scaling factor \emph S has linear relation
with the computation time without affecting the communication time. The
communication time consists of the beginning times which an MPI calls for
sending or receiving till the message is synchronously sent or received. In this
paper we predict the execution time of the program for any new scaling factor
value. Depending on this prediction we can produce our energy-performance scaling
method as we will show in the coming sections. In section~\ref{sec.expe} we make an
investigation study for EQ~(\ref{eq:tnew}).



\section{Performance to Energy Competition}
\label{sec.compet}

This section demonstrates our approach for choosing the optimal scaling
factor. This factor gives maximum energy reduction taking into account the
execution time for both computation and communication times. The relation
between the energy and the performance are nonlinear and complex, because the
relation of the energy with scaling factor is nonlinear and with the performance
it is linear see~\cite{17}. The relation between the energy and the performance
is not straightforward. Moreover, they are not measured using the same metric.
For solving this problem, we normalize the energy by calculating the ratio
between the consumed energy with scaled frequency and the consumed energy
without scaled frequency:
\begin{multline}
  \label{eq:enorm}
  E_\textit{Norm} = \frac{ E_\textit{Reduced}}{E_\textit{Original}} \\
        {} = \frac{P_\textit{dyn} \cdot S_i^{-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_i \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 }
\end{multline}
By the same way we can normalize the performance as follows:
\begin{equation}
  \label{eq:pnorm}
  P_\textit{Norm} = \frac{T_\textit{New}}{T_\textit{Old}}
          = \frac{T_\textit{Max Comp Old} \cdot S +
           T_\textit{Max Comm Old}}{ T_\textit{Old}}
\end{equation}
The second problem is the optimization operation for both energy and performance
is not in the same direction. In other words, the normalized energy and the
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
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 are present a method to find the optimal scaling factor \emph S for
optimize 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
performance as follows:
\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 +
                 T_\textit{Max Comm Old}}
\end{equation}
\begin{figure*}
  \centering
  \subfloat[Converted Relation.]{%
    \includegraphics[width=.4\textwidth]{file.eps}\label{fig:r1}}%
  \qquad%
  \subfloat[Real Relation.]{%
    \includegraphics[width=.4\textwidth]{file3.eps}\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
the minimum energy consumption with minimum execution time (better performance)
in the same time, see figure~(\ref{fig:r1}). Then our objective function has the
following form:
\begin{equation}
  \label{eq:max}
  \textit{MaxDist} = \max (\overbrace{P^{-1}_\textit{Norm}}^{\text{Maximize}} -
                           \overbrace{E_\textit{Norm}}^{\text{Minimize}} )
\end{equation}
Then we can select the optimal scaling factor that satisfy the
EQ~(\ref{eq:max}).  Our objective function can works with any energy model or
static power values stored in a data file. Moreover, this function works in
optimal way when the energy function has a convex form with frequency scaling
factor as shown in~\cite{15,3,19}. Energy measurement model is not the
objective of this paper and we choose Rauber and Rünger model as an example with two
reasons that mentioned before.

\section{Optimal Scaling Factor for Performance and Energy}
\label{sec.optim}

In the previous section we described the objective function that satisfy our
goal in discovering optimal scaling factor for both performance and energy at
the same time. Therefore, we develop an energy to performance scaling algorithm
($EPSA$). This algorithm is simple and has a direct way to calculate the optimal
scaling factor for both energy and performance at the same time.
\begin{algorithm}[tp]
  \caption{EPSA}
  \label{EPSA}
  \begin{algorithmic}[1]
    \State  Initialize the variable $Dist=0$
    \State Set dynamic and static power values.
    \State Set $P_{states}$ to the number of available frequencies.
    \State Set the variable $F_{new}$ to max. frequency,  $F_{new} = F_{max} $
    \State Set the variable $F_{diff}$ to the scale value between each two frequencies.
    \For {$i=1$   to   $P_{states} $}
      \State - Calculate the new frequency as $F_{new}=F_{new} - F_{diff} $
      \State - Calculate the scale factor $S$ as in EQ~(\ref{eq:s}).
      \State - Calculate all available scales $S_i$  depend on $S$ as\par\hspace{1 pt} in EQ~(\ref{eq:si}).
      \State - Select the maximum scale factor $S_1$ from the set\par\hspace{1 pt} of scales $S_i$.
      \State - Calculate the normalize energy $E_{Norm}=E_{R}/E_{O}$ 
               \par\hspace{1 pt}  as in EQ~(\ref{eq:enorm}).
      \State - Calculate the normalize inverse of performance\par\hspace{1 pt}
               $P_{NormInv}=T_{old}/T_{new}$ as in EQ~(\ref{eq:pnorm_en}).
      \If{  $(P_{NormInv}-E_{Norm} > Dist$) }
        \State $S_{optimal} = S$
        \State $Dist = P_{NormInv} - E_{Norm}$
      \EndIf
    \EndFor
    \State  Return $S_{optimal}$
  \end{algorithmic}
\end{algorithm}
The proposed EPSA algorithm works online during the execution time of the MPI
program. It selects the optimal scaling factor by gathering the computation and communication times 
from the program after one iteration.
 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 data required 
by this algorithm is the computation time and the communication time for each task from the first iteration only. 
When these times are measured, the MPI program calls the EPSA algorithm to choose the new frequency using the
optimal scaling factor. Then the program changes the new frequency of the system. The algorithm is called just 
one time during the execution of the program. The DVFS algorithm~(\ref{dvfs}) shows where and when the EPSA algorithm is called
in the MPI program.
%\begin{minipage}{\textwidth}
%\AG{Use the same format as for Algorithm~\ref{$EPSA$}}

\begin{algorithm}[tp]
  \caption{DVFS}
  \label{dvfs}
  \begin{algorithmic}[1]
 \For {$J:=1$ to $Some-Iterations \; $}
  \State -Computations Section.
   \State -Communications Section.
   \If {$(J=1)$} 
     \State -Gather all times of computation and\par\hspace{13 pt} communication from each node.
     \State -Call EPSA with these times.
     \State -Calculate the new frequency from optimal scale.
     \State -Set the new frequency to the system.
   \EndIf
\EndFor
\end{algorithmic}
\end{algorithm}
After obtaining the optimal scale factor from the EPSA algorithm, the program
calculates the new frequency $F_i$ for each task proportionally to its time
value $T_i$. By substitution of  EQ~(\ref{eq:s}) in EQ~(\ref{eq:si}), we
can calculate the new frequency $F_i$ as follows:
\begin{equation}
  \label{eq:fi}
  F_i = \frac{F_\textit{max} \cdot T_i}{S_\textit{optimal} \cdot T_\textit{max}}
\end{equation}
According to this equation all the nodes may have the same frequency value if
they have balanced workloads. Otherwise, they take different frequencies when
have imbalanced workloads. Then EQ~(\ref{eq:fi}) works in adaptive way to change
the frequency according to the nodes workloads.

\section{Experimental Results}
\label{sec.expe}
Our experiments are executed on the simulator SimGrid/SMPI
v3.10. We design a platform file that simulates a cluster with one core per
node. This cluster is a homogeneous architecture with distributed memory. The
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. Each core simulates the real Intel core i5-3210M processor. 
This processor has frequencies from 2.5 GHz to 1.2 GHz with 100 MHz difference between each two successive 
frequencies. We increased this range to verify the EPSA algorithm takes small execution 
time while it has a big number of available frequencies. The simulated network link is 1 GB 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 methods
on the NAS benchmarks. We use EQ~(\ref{eq:tnew}) that predicts the execution
time for any scale value. The NAS programs run the class B for comparing the
real execution time with the predicted execution time. Each program runs offline
with all available scaling factors on 8 or 9 nodes to produce real execution
time values. These scaling factors are computed by dividing the maximum
frequency by the new one see EQ~(\ref{eq:s}). 
\begin{figure*}[t]
  \centering
  \includegraphics[width=.4\textwidth]{cg_per.eps}\qquad%
  \includegraphics[width=.4\textwidth]{mg_pre.eps}
  \includegraphics[width=.4\textwidth]{bt_pre.eps}\qquad%
  \includegraphics[width=.4\textwidth]{lu_pre.eps}
  \caption{Fitting 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
 NAS parallel benchmarks: CG, MG, EP, FT, BT, LU
and SP. The average normalized errors between the predicted execution time and
the real time (SimGrid time) for all programs is between 0.0032 to 0.0133. AS an
example, we are present the execution times of the NAS benchmarks as in the
figure~(\ref{fig:pred}).

\subsection{The EPSA Results}
The proposed EPSA algorithm was applied to seven MPI programs of the NAS
benchmarks (EP, CG, MG, FT, BT, LU and SP). We work on three classes (A, B and
C) for each program. Each program runs on specific number of processors
proportional to the size of the class.  Each class represents the problem size
ascending from the class A to C. Additionally, depending on some speed up points
for each class we run the classes A, B and C on 4, 8 or 9 and 16 nodes
respectively. 
Depending on EQ~(\ref{eq:energy}), we measure the energy consumption for all
the NAS MPI programs while assuming the power dynamic is equal to \np[W]{20} and
the power static is equal to \np[W]{4} for all experiments. These power values 
used by Rauber and Rünger~\cite{3}.  We run the proposed EPSA
algorithm for all these programs. The results showed that the algorithm selected
different scaling factors for each program depending on the communication
features of the program as in the figure~(\ref{fig:nas}). This figure shows that
there are different distances between the normalized energy and the normalized
inversed performance curves, because there are different communication features
for each MPI program.  When there are little or not communications, the inversed
performance curve is very close to the energy curve. Then the distance between
the two curves is very small. This leads to small energy savings. The opposite
happens when there are a lot of communication, the distance between the two
curves is big.  This leads to more energy savings (e.g. CG and FT), see
table~(\ref{table:factors results}). All discovered frequency scaling factors
optimize both the energy and the performance simultaneously for all the NAS
programs. In table~(\ref{table:factors results}), we record all optimal scaling
factors results for each program on class C. These factors give the maximum
energy saving percent and the minimum performance degradation percent in the
same time over all available scales.
\begin{figure*}[t]
  \centering
  \includegraphics[width=.33\textwidth]{ep.eps}\hfill%
  \includegraphics[width=.33\textwidth]{cg.eps}\hfill%
  \includegraphics[width=.33\textwidth]{sp.eps}
  \includegraphics[width=.33\textwidth]{lu.eps}\hfill%
  \includegraphics[width=.33\textwidth]{bt.eps}\hfill%
  \includegraphics[width=.33\textwidth]{ft.eps}
  \caption{Optimal scaling factors for The NAS MPI Programs}
  \label{fig:nas}
\end{figure*}
\begin{table}[htb]
  \caption{The EPSA Scaling Factors Results}
  % title of Table
  \centering
  \begin{tabular}{|l|*{4}{r|}}
    \hline
    Program & Optimal & Energy  & Performance&Energy-Perf.\\
    Name & Scaling Factor& Saving \%&Degradation \% &Distance  \\ \hline
    CG & 1.56 &39.23&14.88 &24.35\\ \hline
    MG & 1.47 &34.97&21.70 &13.27 \\ \hline
    EP & 1.04 &22.14&20.73 &1.41\\ \hline
    LU & 1.38 &35.83&22.49 &13.34\\ \hline
    BT & 1.31 &29.60&21.28 &8.32\\ \hline
    SP & 1.38 &33.48&21.36 &12.12\\ \hline
    FT & 1.47 &34.72&19.00 &15.72\\ \hline
  \end{tabular}	
  \label{table:factors results}
  % is used to refer this table in the text
\end{table}
As shown in the table~(\ref{table:factors results}), when the optimal scaling
factor has big value we can gain more energy savings for example as in CG and
FT. The opposite happens when the optimal scaling factor is small value as
example BT and EP. Our algorithm selects big scaling factor value when the
communication and the other slacks times are big and smaller ones in opposite
cases. In EP there are no communications inside the iterations. This make our
EPSA to selects smaller scaling factor values (inducing smaller energy savings).

\subsection{Comparing Results}

In this section, we compare our EPSA algorithm results with Rauber and Rünger
methods~\cite{3}. They had two scenarios, the first is to reduce energy to
optimal level without considering the performance as in EQ~(\ref{eq:sopt}). We
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 $R_{E-P}$. The comparison is made in tables~(\ref{table:compare
  Class A},\ref{table:compare Class B},\ref{table:compare Class C}). These
tables show the results of our EPSA and Rauber and Rünger scenarios for all the
NAS benchmarks programs for classes A,B and C.
\begin{table}[p]
  \caption{Comparing Results for  The NAS Class A}
  % title of Table
  \centering
  \begin{tabular}{|l|l|*{4}{r|}}
    \hline
    Method&Program&Factor& Energy& Performance &Energy-Perf.\\
    Name &Name&Value& Saving \%&Degradation \% &Distance
    \\ \hline
    % \rowcolor[gray]{0.85}
    $EPSA$&CG & 1.56 &37.02 & 13.88 & 23.14\\ \hline
    $R_{E-P}$&CG &2.14 &42.77 & 25.27 & 17.50\\ \hline
    $R_{E}$&CG &2.14 &42.77&26.46&16.31\\ \hline

    $EPSA$&MG & 1.47 &27.66&16.82&10.84\\ \hline
    $R_{E-P}$&MG &2.14&34.45&31.84&2.61\\ \hline
    $R_{E}$&MG &2.14&34.48&33.65&0.80 \\ \hline

    $EPSA$&EP &1.19 &25.32&20.79&4.53\\ \hline
    $R_{E-P}$&EP&2.05&41.45&55.67&-14.22\\ \hline
    $R_{E}$&EP&2.05&42.09&57.59&-15.50\\ \hline

    $EPSA$&LU&1.56& 39.55 &19.38& 20.17\\ \hline
    $R_{E-P}$&LU&2.14&45.62&27.00&18.62 \\ \hline
    $R_{E}$&LU&2.14&45.66&33.01&12.65\\ \hline

    $EPSA$&BT&1.31& 29.60&20.53&9.07 \\ \hline
    $R_{E-P}$&BT&2.10&45.53&49.63&-4.10\\ \hline
    $R_{E}$&BT&2.10&43.93&52.86&-8.93\\ \hline

    $EPSA$&SP&1.38& 33.51&15.65&17.86 \\ \hline
    $R_{E-P}$&SP&2.11&45.62&42.52&3.10\\ \hline
    $R_{E}$&SP&2.11&45.78&43.09&2.69\\ \hline

    $EPSA$&FT&1.25&25.00&10.80&14.20 \\ \hline
    $R_{E-P}$&FT&2.10&39.29&34.30&4.99 \\ \hline
    $R_{E}$&FT&2.10&37.56&38.21&-0.65\\ \hline
  \end{tabular}
  \label{table:compare Class A}
  % is used to refer this table in the text
\end{table}
\begin{table}[p]
  \caption{Comparing Results for The NAS Class B}
  % title of Table
  \centering
  \begin{tabular}{|l|l|*{4}{r|}}
    \hline
    Method&Program&Factor& Energy& Performance &Energy-Perf.\\
    Name &Name&Value& Saving \%&Degradation \% &Distance
    \\ \hline
    % \rowcolor[gray]{0.85}
    $EPSA$&CG & 1.66 &39.23&16.63&22.60   \\ \hline
    $R_{E-P}$&CG &2.15 &45.34&27.60&17.74\\ \hline
    $R_{E}$&CG &2.15 &45.34&28.88&16.46\\ \hline

    $EPSA$ &MG & 1.47 &34.98&18.35&16.63\\ \hline
    $R_{E-P}$&MG &2.14&43.55&36.42&7.13 \\ \hline
    $R_{E}$&MG &2.14&43.56&37.07&6.49 \\ \hline

    $EPSA$&EP &1.08 &20.29&17.15&3.14 \\ \hline
    $R_{E-P}$&EP&2.00&42.38&56.88&-14.50\\ \hline
    $R_{E}$&EP&2.00&39.73&59.94&-20.21\\ \hline

    $EPSA$&LU&1.47&38.57&21.34&17.23 \\ \hline
    $R_{E-P}$&LU&2.10&43.62&36.51&7.11 \\ \hline
    $R_{E}$&LU&2.10&43.61&38.54&5.07 \\ \hline

    $EPSA$&BT&1.31& 29.59&20.88&8.71\\ \hline
    $R_{E-P}$&BT&2.10&44.53&53.05&-8.52\\ \hline
    $R_{E}$&BT&2.10&42.93&52.80&-9.87\\ \hline

    $EPSA$&SP&1.38&33.44&19.24&14.20 \\ \hline
    $R_{E-P}$&SP&2.15&45.69&43.20&2.49\\ \hline
    $R_{E}$&SP&2.15&45.41&44.47&0.94\\ \hline

    $EPSA$&FT&1.38&34.40&14.57&19.83 \\ \hline
    $R_{E-P}$&FT&2.13&42.98&37.35&5.63 \\ \hline
    $R_{E}$&FT&2.13&43.04&37.90&5.14\\ \hline
  \end{tabular}
  \label{table:compare Class B}
  % is used to refer this table in the text
\end{table}

\begin{table}[p]
  \caption{Comparing Results for The NAS Class C}
  % title of Table
  \centering
  \begin{tabular}{|l|l|*{4}{r|}}
    \hline
    Method&Program&Factor& Energy& Performance &Energy-Perf.\\
    Name &Name&Value& Saving \%&Degradation \% &Distance
    \\ \hline
    % \rowcolor[gray]{0.85}
    $EPSA$&CG & 1.56 &39.23&14.88&24.35  \\ \hline
    $R_{E-P}$&CG &2.15 &45.36&25.89&19.47\\ \hline
    $R_{E}$&CG &2.15 &45.36&26.70&18.66\\ \hline

    $EPSA$&MG & 1.47 &34.97&21.69&13.27\\ \hline
    $R_{E-P}$&MG &2.15&43.65&40.45&3.20 \\ \hline
    $R_{E}$&MG &2.15&43.64&41.38&2.26 \\ \hline

    $EPSA$&EP &1.04 &22.14&20.73&1.41 \\ \hline
    $R_{E-P}$&EP&1.92&39.40&56.33&-16.93\\ \hline
    $R_{E}$&EP&1.92&38.10&56.35&-18.25\\ \hline

    $EPSA$&LU&1.38&35.83&22.49&13.34 \\ \hline
    $R_{E-P}$&LU&2.15&44.97&41.00&3.97 \\ \hline
    $R_{E}$&LU&2.15&44.97&41.80&3.17 \\ \hline

    $EPSA$&BT&1.31& 29.60&21.28&8.32\\ \hline
    $R_{E-P}$&BT&2.13&45.60&49.84&-4.24\\ \hline
    $R_{E}$&BT&2.13&44.90&55.16&-10.26\\ \hline

    $EPSA$&SP&1.38&33.48&21.35&12.12\\ \hline
    $R_{E-P}$&SP&2.10&45.69&43.60&2.09\\ \hline
    $R_{E}$&SP&2.10&45.75&44.10&1.65\\ \hline

    $EPSA$&FT&1.47&34.72&19.00&15.72 \\ \hline
    $R_{E-P}$&FT&2.04&39.40&37.10&2.30\\ \hline
    $R_{E}$&FT&2.04&39.35&37.70&1.65\\ \hline
  \end{tabular}
\label{table:compare Class C}
% is used to refer this table in the text
\end{table}
As shown in these tables our scaling factor is not optimal for energy saving
such as Rauber and Rünger scaling factor EQ~(\ref{eq:sopt}), but it is optimal for both
the energy and the performance simultaneously. Our $EPSA$ optimal scaling factors
has better simultaneous optimization for both the energy and the performance
compared to Rauber and Rünger energy-performance method ($R_{E-P}$). Also, in
($R_{E-P}$) method when setting the frequency to maximum value for the
slower task lead to a small improvement of the performance. Also the results
show that this method keep or improve energy saving. Because of the energy
consumption decrease when the execution time decreased while the frequency value
increased.

Figure~(\ref{fig:compare}) shows the maximum distance between the energy saving
percent and the performance degradation percent. Therefore, this means it is the
same resultant of our objective function EQ~(\ref{eq:max}). Our algorithm always
gives 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. The positive trade offs with highest values lead to maximum energy savings
concatenating with less performance degradation and this the objective of this
paper. While the negative trade offs refers to improving energy saving (or may
be the performance) while degrading the performance (or may be the energy) more
than the first.
\begin{figure}[t]
  \centering
  \includegraphics[width=.33\textwidth]{compare_class_A.pdf}
  \includegraphics[width=.33\textwidth]{compare_class_B.pdf}
  \includegraphics[width=.33\textwidth]{compare_class_c.pdf}
  \caption{Comparing Our EPSA with Rauber and Rünger Methods}
  \label{fig:compare}
\end{figure}
\section{Conclusion}
\label{sec.concl}
In this paper we develop the simultaneous energy-performance algorithm. It works based on the trade off relation between the energy and performance. The results showed that when the scaling factor is big value refer to more energy saving. Also, when the scaling factor is smaller value, Then it has bigger impact on performance than energy. The algorithm optimizes the energy saving and performance in the same time to have positive trade off. The optimal trade off represents the maximum distance between the energy and the inversed performance curves. Also, the results explained when setting the slowest task to maximum frequency usually not have a big improvement on performance. In future, we will apply the EPSA algorithm on heterogeneous platform.

\section*{Acknowledgment}
Computations have been performed on the supercomputer facilities of the
Mésocentre de calcul de Franche-Comté.
As a PhD student, M. Ahmed Fanfakh, would like to thank the University of
Babylon (Iraq) for supporting his work.


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%  LocalWords:  Fanfakh Charr FIXME Tianhe DVFS HPC NAS NPB SMPI Rauber's Rauber
%  LocalWords:  CMOS EQ EPSA Franche Comté Tflop Rünger
