some changes

diff --git a/paper.tex b/paper.tex
index aecd79e988047729e67ec389b2af8e1d5fe99b00..bec8af60d3db4c8d810429d6bd5aa69b6080738d 100644 (file)
--- a/paper.tex
+++ b/paper.tex
@@ -67,7 +67,7 @@ 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
 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
+tackle this problem, many researchers used DVFS (Dynamic Voltage and 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
 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


@@ -101,7 +101,7 @@ 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
 
 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
+tasks and sources of idle times. Also, 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
 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


@@ -231,7 +231,7 @@ 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
 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
+expressed by the scaling factor \emph S. This scaling factor is the ratio between the

 maximum and the new frequency as in EQ~(\ref{eq:s}).
 \begin{equation}
   \label{eq:s}
 maximum and the new frequency as in EQ~(\ref{eq:s}).
 \begin{equation}
   \label{eq:s}


@@ -240,8 +240,8 @@ maximum and the new frequency as in EQ~(\ref{eq:s}).
 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
 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
+lowering core's frequency.  The scaling factor is equal to 1 when the new frequency is 
+set 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
 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


@@ -310,7 +310,7 @@ these times are used to predict the execution time for any MPI program as a func
 the new scaling factor as in EQ~(\ref{eq:tnew}).
 \begin{equation}
   \label{eq:tnew}
 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}}
+ \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
 \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


@@ -408,7 +408,7 @@ reasons that mentioned before.
 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
 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
+(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}
 scaling factor for both energy and performance at the same time.
 \begin{algorithm}[tp]
   \caption{EPSA}


@@ -766,7 +766,7 @@ than the first.
 \end{figure}
 \section{Conclusion}
 \label{sec.concl}
 \end{figure}
 \section{Conclusion}
 \label{sec.concl}

-In this paper we developed 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.
+In this paper we developed 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
 
 \section*{Acknowledgment}
 Computations have been performed on the supercomputer facilities of the