some changes

diff --git a/compare_class_c.pdf b/compare_class_c.pdf
index 5170c6045359a1aa4a84376a843bff7e5e7f580a..99d9365087f10645a9ab3d9f2ed6ce502ff229fa 100644 (file)
Binary files a/compare_class_c.pdf and b/compare_class_c.pdf differ

diff --git a/paper.tex b/paper.tex
index 9ab93a9343fd828904d81f725644652f1471553e..aecd79e988047729e67ec389b2af8e1d5fe99b00 100644 (file)
--- a/paper.tex
+++ b/paper.tex
@@ -362,7 +362,7 @@ 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
 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
+paper we 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
 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


@@ -381,7 +381,7 @@ performance as follows:
   \subfloat[Real Relation.]{%
     \includegraphics[width=.4\textwidth]{file3.eps}\label{fig:r2}}
   \label{fig:rel}
   \subfloat[Real Relation.]{%
     \includegraphics[width=.4\textwidth]{file3.eps}\label{fig:r2}}
   \label{fig:rel}

-  \caption{The Energy and Performance Relation}
+  \caption{The Relation of Energy and Performance }

 \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
 \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


@@ -454,14 +454,14 @@ in the MPI program.
   \caption{DVFS}
   \label{dvfs}
   \begin{algorithmic}[1]
   \caption{DVFS}
   \label{dvfs}
   \begin{algorithmic}[1]

- \For {$J:=1$ to $Some-Iterations \; $}
+ \For {$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 -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.
+     \State -Change the new frequency of the system.

    \EndIf
 \EndFor
 \end{algorithmic}
    \EndIf
 \EndFor
 \end{algorithmic}


@@ -494,7 +494,7 @@ frequencies. We increased this range to verify the EPSA algorithm takes small ex
 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]
 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}
+  \caption{SimGrid Platform File Parameters}

   % title of Table
   \centering
   \begin{tabular}{|*{7}{l|}}
   % title of Table
   \centering
   \begin{tabular}{|*{7}{l|}}


@@ -530,7 +530,7 @@ 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
  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
+example, we present the execution times of the NAS benchmarks as in the

 figure~(\ref{fig:pred}).
 
 \subsection{The EPSA Results}
 figure~(\ref{fig:pred}).
 
 \subsection{The EPSA Results}


@@ -569,7 +569,7 @@ same time over all available scales.
   \includegraphics[width=.33\textwidth]{lu.eps}\hfill%
   \includegraphics[width=.33\textwidth]{bt.eps}\hfill%
   \includegraphics[width=.33\textwidth]{ft.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}
+  \caption{The Discovered  scaling factors for NAS MPI Programs}

   \label{fig:nas}
 \end{figure*}
 \begin{table}[htb]
   \label{fig:nas}
 \end{figure*}
 \begin{table}[htb]


@@ -737,7 +737,7 @@ NAS benchmarks programs for classes A,B and C.
 \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
 \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
+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
 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


@@ -761,12 +761,12 @@ than the first.
   \includegraphics[width=.33\textwidth]{compare_class_A.pdf}
   \includegraphics[width=.33\textwidth]{compare_class_B.pdf}
   \includegraphics[width=.33\textwidth]{compare_class_c.pdf}
   \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}
+  \caption {Comparing Our EPSA with Rauber and Rünger Methods}

   \label{fig:compare}
 \end{figure}
 \section{Conclusion}
 \label{sec.concl}
   \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.
+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