Remove spaces before colons.

diff --git a/paper.tex b/paper.tex
index 5e8031280e8313e9da0e5d5c1f80fd79d1c52093..dc963b5280f10dcfd97227dc285dfc16a5538cb2 100644 (file)
--- a/paper.tex
+++ b/paper.tex
@@ -200,7 +200,7 @@ 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 $C_L$,
 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 :
+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
 \begin{equation}
   \label{eq:pd}
   P_\textit{dyn} = \alpha \cdot C_L \cdot V^2 \cdot f


@@ -342,7 +342,7 @@ In our cluster there are 18 available frequency states for each processor from
 2.5 GHz to 800 MHz, there is 100 MHz difference between two successive
 frequencies. For more details on the characteristics of the platform refer to
 table~(\ref{table:platform}). This lead to 18 run states for each program. We
 2.5 GHz to 800 MHz, there is 100 MHz difference between two successive
 frequencies. For more details on the characteristics of the platform refer to
 table~(\ref{table:platform}). 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
+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
 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


@@ -360,7 +360,7 @@ 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
 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 :
+without scaled frequency:

 \begin{multline}
   \label{eq:enorm}
   E_\textit{Norm} = \frac{ E_\textit{Reduced}}{E_\textit{Original}} \\
 \begin{multline}
   \label{eq:enorm}
   E_\textit{Norm} = \frac{ E_\textit{Reduced}}{E_\textit{Original}} \\


@@ -370,7 +370,7 @@ without scaled frequency :
               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}
               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 :
+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}}
 \begin{equation}
   \label{eq:pnorm}
   P_\textit{Norm} = \frac{T_\textit{New}}{T_\textit{Old}}


@@ -391,7 +391,7 @@ 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
 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 :
+performance as follows:

 \begin{equation}
   \label{eq:pnorm_en}
   P^{-1}_\textit{Norm} = \frac{ T_\textit{Old}}{ T_\textit{New}}
 \begin{equation}
   \label{eq:pnorm_en}
   P^{-1}_\textit{Norm} = \frac{ T_\textit{Old}}{ T_\textit{New}}


@@ -495,7 +495,7 @@ in the MPI program.
 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 the EQ~(\ref{eq:s}) in the EQ~(\ref{eq:si}), we
 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 the EQ~(\ref{eq:s}) in the EQ~(\ref{eq:si}), we

-can calculate the new frequency $F_i$ as follows :
+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}}
 \begin{equation}
   \label{eq:fi}
   F_i = \frac{F_\textit{max} \cdot T_i}{S_\textit{optimal} \cdot T_\textit{max}}