X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy.git/blobdiff_plain/59347d7a8ba988cf21316caec932c1eb5b56278b..5d16721b368257188bbe8059e25772619bdf72da:/paper.tex

diff --git a/paper.tex b/paper.tex
index 9310147..e3efb32 100644
--- a/paper.tex
+++ b/paper.tex
@@ -32,25 +32,15 @@
   \IEEEauthorblockA{%
     FEMTO-ST Institute\\
     University of Franche-Comté\\
-<<<<<<< HEAD
-    IUT de Belfort-Montb\'{e}liard, Rue Engel Gros, BP 27, 90016 Belfort, France\\
-   Fax  : (+33)~3~84~58~77~32\\
-   Email: \{jean-claude.charr, raphael.couturier, ahmed.fanfakh, arnaud.giersch\}@univ-fcomte.fr
-=======
     IUT de Belfort-Montbéliard, 19 avenue du Maréchal Juin, BP 527, 90016 Belfort cedex, France\\
     Fax  : +33~3~84~58~77~32\\
     Email: \{jean-claude.charr,raphael.couturier,ahmed.fanfakh\_badri\_muslim,arnaud.giersch\}@univ-fcomte.fr
->>>>>>> a61f50d29efe27025514a6a5e34444845de7fe9b
    }
   }
 
 \maketitle
 
-<<<<<<< HEAD
-=======
 \AG{Is the fax number correct? Shall we add a telephone number?}
-\JC{Use Capital letters for only the first letter in the title of a section, table, figure, ...} 
->>>>>>> a61f50d29efe27025514a6a5e34444845de7fe9b
 \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
@@ -176,13 +166,8 @@ 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}
-<<<<<<< HEAD
-%\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}
-=======
-\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}
->>>>>>> a61f50d29efe27025514a6a5e34444845de7fe9b
 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
 be a multi-core. The nodes are connected via a high bandwidth network. Tasks
@@ -255,10 +240,6 @@ 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}).
 
-<<<<<<< HEAD
-=======
-\JC{Are you sure of the following equation}
->>>>>>> a61f50d29efe27025514a6a5e34444845de7fe9b
 \begin{equation}
   \label{eq:energy}
   E = P_\textit{dyn} \cdot S_1^{-2} \cdot
@@ -268,29 +249,16 @@ 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}).
-<<<<<<< HEAD
-=======
-\JC{This equation does not make sense either, what's S? there is no F}
->>>>>>> a61f50d29efe27025514a6a5e34444845de7fe9b
 \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}
-<<<<<<< HEAD
 In this paper we depend on
-=======
-\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
->>>>>>> a61f50d29efe27025514a6a5e34444845de7fe9b
 Rauber and Rünger energy model EQ~(\ref{eq:energy}) for two reasons: (1) this
 model is used for any number of concurrent tasks, 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}).
-<<<<<<< HEAD
-=======
-\JC{what's the small n in the equation}
->>>>>>> a61f50d29efe27025514a6a5e34444845de7fe9b
 
 \begin{equation}
   \label{eq:sopt}
@@ -511,15 +479,8 @@ frequency by the new one see EQ~(\ref{eq:s}).
 In our cluster there are 18 available frequency states for each processor. 
 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
-<<<<<<< HEAD
 and SP. Figure~(\ref{fig:pred}) presents plots of the real execution times and the simulated ones. The maximum normalized error between the predicted execution time and the real time (SimGrid time) for all programs is between 0.0073 to 0.031. The  better case is for CG and the worse case is for LU. 
 \subsection{The experimental results for the scaling algorithm }
-=======
-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. 
-\JC{why compute the average error not the max}
-\subsection{The  experimental results}
->>>>>>> a61f50d29efe27025514a6a5e34444845de7fe9b
 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
 C). For each instance the benchmarks were executed on a number of processors
@@ -592,12 +553,9 @@ optimal level without considering the performance as in EQ~(\ref{eq:sopt}). We
 refer to this scenario as $R_{E}$. The second scenario is similar to the first
 except setting the slower task to the maximum frequency (when the scale $S=1$)
 to keep the performance from degradation as mush as possible. We refer to this
-<<<<<<< HEAD
-scenario as $R_{E-P}$. While we refer to our algorithm as EPSA. The comparison is made in tables~(\ref{table:compareA},\ref{table:compareB},\ref{table:compareC}). These
-=======
-scenario as $R_{E-P}$. The comparison is made in tables~(\ref{table:compareA},
-\ref{table:compareB}, and \ref{table:compareC}). These
->>>>>>> a61f50d29efe27025514a6a5e34444845de7fe9b
+scenario as $R_{E-P}$. While we refer to our algorithm as EPSA. The comparison
+is made in tables \ref{table:compareA}, \ref{table:compareB},
+and~\ref{table:compareC}. These
 tables show the results of our method and Rauber and Rünger scenarios for all the
 NAS benchmarks programs for classes A,B and C.
 \begin{table}[p]
@@ -749,13 +707,10 @@ In the near future, we would like to adapt this scaling factor selection method
 
 
 \section*{Acknowledgment}
+\AG{Jean-Claude, why did you remove the Mésocentre here?}
 As a PhD student, M. Ahmed Fanfakh, would like to thank the University of
 Babylon (Iraq) for supporting his work.
 
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-=======
-\JC{delete the online paths for each reference\AG{except for TOP500 and the NPB}}
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