From: afanfakh <afanfakh@fanfakh.afanfakh>
Date: Wed, 26 Mar 2014 09:55:46 +0000 (+0100)
Subject: updates
X-Git-Tag: ispa14_submission~13^2
X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy.git/commitdiff_plain/2c7938418a149d56964ef8ea4457bd2e8277e5c6

updates
---

diff --git a/fig/compare_class_A.pdf b/fig/compare_class_A.pdf
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diff --git a/fig/compare_class_B.pdf b/fig/compare_class_B.pdf
index a879c0b..cdcd665 100644
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diff --git a/fig/compare_class_C.pdf b/fig/compare_class_C.pdf
index 019dad9..805353c 100644
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diff --git a/paper.tex b/paper.tex
index bcbfb5d..61d95c6 100644
--- a/paper.tex
+++ b/paper.tex
@@ -157,7 +157,7 @@ To maintain the performance of the parallel program , they
 set the  processor with the biggest load to the highest gear and then compute the scaling  factor values for the rest of the processors. Although this model was built for parallel architectures, it can be adapted  to distributed architectures by taking into account the communications. 
 The primary contribution of this paper is presenting a new online scaling factor selection method which has the following characteristics :
 \begin{enumerate}
-\item Based on Rauber's analytical model to predict the energy consumption and the execution time of the application with different frequency gears. 
+\item Based on Rauber and Rünger analytical model to predict the energy consumption and the execution time of the application with different frequency gears. 
 \item Selects the frequency scaling factor for simultaneously optimizing energy reduction and maintaining performance.
 \item Well adapted to distributed architectures because it takes into account the communication time.
 \item Well adapted to distributed applications with imbalanced tasks.
@@ -241,7 +241,7 @@ new frequency value~(\emph {P-state}) in the governor. The CPU governor is an
 interface driver supplied by the operating system's kernel to
 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}).
+homogeneous platform, as  presented by Rauber and Rünger~\cite{3}, can be written as a function of   the scaling factor \emph S,   as in EQ~(\ref{eq:energy}).
 
 \begin{equation}
   \label{eq:energy}
@@ -494,7 +494,7 @@ respectively.
 Depending on EQ~(\ref{eq:energy}), we measure the energy consumption for all
 the NAS MPI programs while assuming the power dynamic with the highest frequency is equal to \np[W]{20} and
 the power static is equal to \np[W]{4} for all experiments. These power values were also
-used by Rauber and Rünger in~\cite{3}.   The results showed that the algorithm selected
+used by Rauber and Rünger in~\cite{3}.  The results showed that the algorithm selected
 different scaling factors for each program depending on the communication
 features of the program as in the plots~(\ref{fig:nas}). These plots illustrate that
 there are different distances between the normalized energy and the normalized
@@ -710,8 +710,8 @@ 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
+Computations have been performed on the supercomputer facilities of the
+Mésocentre de calcul de Franche-Comté. As a PhD student, M. Ahmed Fanfakh, would like to thank the University of
 Babylon (Iraq) for supporting his work.
 
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