section IV corrected

[mpi-energy2.git] / Heter_paper.tex

diff --git a/Heter_paper.tex b/Heter_paper.tex
index c6f30214f485d650ed81ea78d9373e6c1a0705e9..8c329a8adc36e134fdd364eee4cdfed79f04fd4d 100644 (file)
--- a/Heter_paper.tex
+++ b/Heter_paper.tex
@@ -267,20 +267,25 @@ In the same way, we normalize the energy by computing the ratio between the cons
 \end{multline} 
 Where $T_{New}$ and $T_{Old}$ are computed as in EQ(\ref{eq:pnorm}).
 
 \end{multline} 
 Where $T_{New}$ and $T_{Old}$ are computed as in EQ(\ref{eq:pnorm}).
 

-  The normalized energy and execution time curves are not in the same direction.   While the main
-goal is to optimize the energy and execution time at the same time.  According
+ While the main
+goal is to optimize the energy and execution time at the same time, the normalized energy and execution time curves are not in the same direction.     According

 to the equations~(\ref{eq:enorm}) and~(\ref{eq:pnorm}), the set 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
 to the equations~(\ref{eq:enorm}) and~(\ref{eq:pnorm}), the set 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.  Many researchers used
+reduction with minimum execution time reduction.  
+
+Many researchers used

 different strategies to solve this nonlinear problem for example
 different strategies to solve this nonlinear problem for example

-see~\cite{19,42}, their methods add big overheads to the algorithm to select the
-suitable frequency.  In this paper we are present a method to find the optimal
-set of frequency scaling factors to optimize both energy and execution time
-simultaneously without adding a big overhead.  Our solution for this problem is
+in~\cite{19,42}, their methods add big overheads to the algorithm to select the
+suitable frequency.  In this paper we  present a method to find the optimal
+set of frequency scaling factors to simultaneously optimize both energy and execution time
+ without adding a big overhead. \textbf{put the last two phrases in the related work section}
+ 
+  
+    Our solution for this problem is

 to make the optimization process for energy and execution time follow the same
 to make the optimization process for energy and execution time follow the same

-direction.  Therefore, we inverse the equation of the normalized execution time,
-the normalized performance, as follows:
+direction.  Therefore, we inverse the equation of the normalized execution time which gives 
+the normalized performance equation, as follows:

 \begin{multline}
   \label{eq:pnorm_inv}
   P_\textit{Norm} = \frac{T_\textit{Old}}{T_\textit{New}}\\
 \begin{multline}
   \label{eq:pnorm_inv}
   P_\textit{Norm} = \frac{T_\textit{Old}}{T_\textit{New}}\\


@@ -315,9 +320,8 @@ function has the following form:
 \end{equation}
 where $N$ is the number of nodes and $F$ is the  number of available frequencies for each nodes. 
 Then we can select the optimal set of scaling factors that satisfies EQ~(\ref{eq:max}).  Our objective function can
 \end{equation}
 where $N$ is the number of nodes and $F$ is the  number of available frequencies for each nodes. 
 Then we can select the optimal set of scaling factors that satisfies EQ~(\ref{eq:max}).  Our objective function can

-work with any energy model or energy 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 factors as shown in~\cite{15,3,19}.
+work with any energy model or any power values for each node (static and dynamic powers).
+However, the most energy reduction gain can be achieved the energy curve has a convex form as shown in~\cite{15,3,19}.

 
 \section{The heterogeneous scaling algorithm }
 \label{sec.optim}
 
 \section{The heterogeneous scaling algorithm }
 \label{sec.optim}