Define and use new command for JC's comments.

diff --git a/paper.tex b/paper.tex
index 1ecfb3ab550da6aca470707437c4be66948b228d..350a3032e05b4d61e2c950918e0a7e47d415c187 100644 (file)
--- a/paper.tex
+++ b/paper.tex
@@ -16,6 +16,7 @@
 \usepackage{xspace}
 \usepackage[textsize=footnotesize]{todonotes}
 \newcommand{\AG}[2][inline]{\todo[color=green!50,#1]{\sffamily\textbf{AG:} #2}\xspace}
 \usepackage{xspace}
 \usepackage[textsize=footnotesize]{todonotes}
 \newcommand{\AG}[2][inline]{\todo[color=green!50,#1]{\sffamily\textbf{AG:} #2}\xspace}

+\newcommand{\JC}[2][inline]{\todo[color=red!10,#1]{\sffamily\textbf{JC:} #2}\xspace}

 
 \begin{document}
 
 
 \begin{document}
 


@@ -39,7 +40,7 @@
 
 \maketitle
 
 
 \maketitle
 

-\AG{Use Capital letters for only the first letter in the title of a section, table, figure, ...} 
+\JC{Use Capital letters for only the first letter in the title of a section, table, figure, ...} 

 \begin{abstract}
   Dynamic Voltage Frequency Scaling (DVFS) can be applied to modern CPUs. 
 This technique is usually used to reduce the energy consumed by a CPU while
 \begin{abstract}
   Dynamic Voltage Frequency Scaling (DVFS) can be applied to modern CPUs. 
 This technique is usually used to reduce the energy consumed by a CPU while


@@ -167,7 +168,7 @@ The primary contribution of this paper is presenting a new online scaling factor
 
 \section{Execution and Energy of Parallel Tasks on Homogeneous Platform} 
 \label{sec.exe}
 
 \section{Execution and Energy of Parallel Tasks on Homogeneous Platform} 
 \label{sec.exe}

-\AG{The whole subsection ``Parallel Tasks Execution on Homogeneous Platform'', can be deleted if we need space, we can just say we are interested in this paper in homogeneous clusters}
+\JC{The whole subsection ``Parallel Tasks Execution on Homogeneous Platform'', can be deleted if we need space, we can just say we are interested in this paper in homogeneous clusters}

 \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
 \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


@@ -241,7 +242,7 @@ lower a core's frequency.  This factor reduces
 quadratically the dynamic power which may cause degradation in performance and thus, the increase of the static energy because the execution time is increased~\cite{36}. If the tasks are sorted according to their execution times before scaling in a descending order,   the total energy consumption model for a parallel
 homogeneous platform, as  presented by Rauber et al.~\cite{3}, can be written as a function of   the scaling factor \emph S,   as in EQ~(\ref{eq:energy}).
 
 quadratically the dynamic power which may cause degradation in performance and thus, the increase of the static energy because the execution time is increased~\cite{36}. If the tasks are sorted according to their execution times before scaling in a descending order,   the total energy consumption model for a parallel
 homogeneous platform, as  presented by Rauber et al.~\cite{3}, can be written as a function of   the scaling factor \emph S,   as in EQ~(\ref{eq:energy}).
 

-\AG{Are you sure of the following equation}
+\JC{Are you sure of the following equation}

 \begin{equation}
   \label{eq:energy}
   E = P_\textit{dyn} \cdot S_1^{-2} \cdot
 \begin{equation}
   \label{eq:energy}
   E = P_\textit{dyn} \cdot S_1^{-2} \cdot


@@ -251,19 +252,19 @@ homogeneous platform, as  presented by Rauber et al.~\cite{3}, can be written as
 \end{equation}
 where \emph N is the number of parallel nodes,  $T_i  \  and  \  S_i \  for \  i=1,...,N$ are the execution times and scaling factors of the sorted tasks. Therefore,  $T1$ is the time of the slowest task,  and $S_1$ its scaling factor which should be the highest because they are proportional to
 the time values $T_i$.  The scaling factors are computed as in EQ~(\ref{eq:si}).
 \end{equation}
 where \emph N is the number of parallel nodes,  $T_i  \  and  \  S_i \  for \  i=1,...,N$ are the execution times and scaling factors of the sorted tasks. Therefore,  $T1$ is the time of the slowest task,  and $S_1$ its scaling factor which should be the highest because they are proportional to
 the time values $T_i$.  The scaling factors are computed as in EQ~(\ref{eq:si}).

-\AG{This equation does not make sense either, what's S? there is no F}
+\JC{This equation does not make sense either, what's S? there is no F}

 \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}
 \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}

-\AG{The Rauber model was used for a parallel machine not a homogeneous platform}
+\JC{The Rauber model was used for a parallel machine not a homogeneous platform}

 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 factor selection method which is based on
 EQ~(\ref{eq:energy}). The optimal scaling factor is computed by minimizing the derivation for this equation which produces EQ~(\ref{eq:sopt}).
 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 factor selection method which is based on
 EQ~(\ref{eq:energy}). The optimal scaling factor is computed by minimizing the derivation for this equation which produces EQ~(\ref{eq:sopt}).

-\AG{what's the small n in the equation}
+\JC{what's the small n in the equation}

 
 \begin{equation}
   \label{eq:sopt}
 
 \begin{equation}
   \label{eq:sopt}


@@ -271,7 +272,7 @@ EQ~(\ref{eq:energy}). The optimal scaling factor is computed by minimizing the d
     \left( 1 + \sum_{i=2}^{N} \frac{T_i^3}{T_1^3} \right) }
 \end{equation}
 
     \left( 1 + \sum_{i=2}^{N} \frac{T_i^3}{T_1^3} \right) }
 \end{equation}
 

-\AG{The following 2 sections can be merged easily}
+\JC{The following 2 sections can be merged easily}

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


@@ -486,7 +487,7 @@ 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. Figure~(\ref{fig:pred}) presents plots of the real execution times and the simulated ones. 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. 
  NAS parallel benchmarks: CG, MG, EP, FT, BT, LU
 and SP. Figure~(\ref{fig:pred}) presents plots of the real execution times and the simulated ones. 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. 

-\AG{why compute the average error not the max}
+\JC{why compute the average error not the max}

 \subsection{The  experimental results}
 The proposed algorithm was applied to seven MPI programs of the NAS
 benchmarks (EP, CG, MG, FT, BT, LU and SP) which were run with three classes (A, B and
 \subsection{The  experimental results}
 The proposed algorithm was applied to seven MPI programs of the NAS
 benchmarks (EP, CG, MG, FT, BT, LU and SP) which were run with three classes (A, B and


@@ -716,7 +717,7 @@ In the near future, we would like to adapt this scaling factor selection method
 As a PhD student, M. Ahmed Fanfakh, would like to thank the University of
 Babylon (Iraq) for supporting his work.
 
 As a PhD student, M. Ahmed Fanfakh, would like to thank the University of
 Babylon (Iraq) for supporting his work.
 

-\AG{delete the online paths for each reference}
+\JC{delete the online paths for each reference}

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