X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy.git/blobdiff_plain/93ccbadf5848d51c30ddfebe0f43794def5225c5..c00a8e3e79d7a39860d4c701f85adff1cc02788b:/paper.tex?ds=sidebyside

diff --git a/paper.tex b/paper.tex
index a405346..babc12b 100644
--- a/paper.tex
+++ b/paper.tex
@@ -1,5 +1,4 @@
-\documentclass[12pt]{article}
-%\documentclass[12pt,twocolumn]{article}
+\documentclass[conference]{IEEEtran}
 
 \usepackage[T1]{fontenc}
 \usepackage[utf8]{inputenc}
@@ -15,14 +14,26 @@
 % \usepackage{secdot}
 %\usepackage[font={footnotesize,bt}]{caption}
 %\usepackage[font=scriptsize,labelfont=bf]{caption}
-\usepackage{lmodern}
 \usepackage{todonotes}
 \newcommand{\AG}[2][inline]{\todo[color=green!50,#1]{\sffamily\small\textbf{AG:} #2}}
 
 \begin{document}
 
 \title{Optimal Dynamic Frequency Scaling for Energy-Performance of Parallel MPI Programs}
-\author{A. Badri \and J.-C. Charr \and R. Couturier \and A. Giersch}
+
+\author{%
+  \IEEEauthorblockN{%
+    Ahmed Badri,
+    Jean-Claude Charr,
+    RaphaÃ«l Couturier and
+    Arnaud Giersch
+  }
+  \IEEEauthorblockA{%
+    FEMTO-ST Institute\\
+    University of Franche-ComtÃ©
+  }
+}
+
 \maketitle
 
 \AG{``Optimal'' is a bit pretentious in the title}
@@ -138,13 +149,13 @@ be a multi-core. The nodes are connected via a high bandwidth network. Tasks
 executed on this model can be either synchronous or asynchronous. In this paper
 we consider execution of the synchronous tasks on distributed homogeneous
 platform. These tasks can exchange the data via synchronous memory passing.
-\begin{figure}[h]
+\begin{figure*}[t]
   \centering
   \subfloat[Synch. Imbalanced Communications]{\includegraphics[scale=0.67]{synch_tasks}\label{fig:h1}}
   \subfloat[Synch. Imbalanced Computations]{\includegraphics[scale=0.67]{compt}\label{fig:h2}}
   \caption{Parallel Tasks on Homogeneous Platform}
   \label{fig:homo}
-\end{figure}
+\end{figure*}
 Therefore, the execution time of a task consists of the computation time and the
 communication time. Moreover, the synchronous communications between tasks can
 lead to idle time while tasks wait at the synchronous point for others tasks to
@@ -293,15 +304,15 @@ with all available scaling factors on 8 or 9 nodes to produce real execution
 time values. These scaling factors are computed by dividing the maximum
 frequency by the new one see EQ~(\ref{eq:s}). In all tests, we use the simulator
 SimGrid/SMPI v3.10 to run the NAS programs.
-\begin{figure}[width=\textwidth,height=\textheight,keepaspectratio]
+\begin{figure*}[t]
   \centering
-  \includegraphics[scale=0.60]{cg_per.eps}
-  \includegraphics[scale=0.60]{mg_pre.eps}
-  \includegraphics[scale=0.60]{bt_pre.eps}
-  \includegraphics[scale=0.60]{lu_pre.eps}
+  \includegraphics[width=.4\textwidth]{cg_per.eps}\qquad%
+  \includegraphics[width=.4\textwidth]{mg_pre.eps}
+  \includegraphics[width=.4\textwidth]{bt_pre.eps}\qquad%
+  \includegraphics[width=.4\textwidth]{lu_pre.eps}
   \caption{Fitting Predicted to Real Execution Time}
   \label{fig:pred}
-\end{figure}
+\end{figure*}
 %see Figure~\ref{fig:pred}
 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
@@ -324,15 +335,15 @@ 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 :
-\begin{equation}
+\begin{multline}
   \label{eq:enorm}
-  E_\textit{Norm} = \frac{E_{Reduced}}{E_{Original}}
-          = \frac{ P_{dyn} \cdot S_i^{-2} \cdot
+  E_\textit{Norm} = \frac{E_{Reduced}}{E_{Original}}\\
+  {} = \frac{ P_{dyn} \cdot S_i^{-2} \cdot
                \left( T_1 + \sum_{i=2}^{N}\frac{T_i^3}{T_1^2}\right) +
                P_{static} \cdot T_1 \cdot S_i \cdot N  }{
               P_{dyn} \cdot \left(T_1+\sum_{i=2}^{N}\frac{T_i^3}{T_1^2}\right) +
               P_{static} \cdot T_1 \cdot N }
-\end{equation}
+\end{multline}
 \AG{Use \texttt{\textbackslash{}text\{xxx\}} or
   \texttt{\textbackslash{}textit\{xxx\}} for all subscripted words in equations
   (e.g. \mbox{\texttt{E\_\{\textbackslash{}text\{Norm\}\}}}).
@@ -368,13 +379,16 @@ performance as follows :
                = \frac{T_{Old}}{T_{\textit{Max Comp Old}} \cdot S +
                  T_{\textit{Max Comm Old}}}
 \end{equation}
-\begin{figure}
+\begin{figure*}
   \centering
-  \subfloat[Converted Relation.]{\includegraphics[scale=0.70]{file.eps}\label{fig:r1}}
-  \subfloat[Real Relation.]{\includegraphics[scale=0.70]{file3.eps}\label{fig:r2}}
+  \subfloat[Converted Relation.]{%
+    \includegraphics[width=.4\textwidth]{file.eps}\label{fig:r1}}%
+  \qquad%
+  \subfloat[Real Relation.]{%
+    \includegraphics[width=.4\textwidth]{file3.eps}\label{fig:r2}}
   \label{fig:rel}
   \caption{The Energy and Performance Relation}
-\end{figure}
+\end{figure*}
 Then, we can modelize our objective function as finding the maximum distance
 between the energy curve EQ~(\ref{eq:enorm}) and the inverse of performance
 curve EQ~(\ref{eq:pnorm_en}) over all available scaling factors. This represent
@@ -518,18 +532,18 @@ programs. In table~(\ref{table:factors results}), we record all optimal scaling
 factors results for each program on class C. These factors give the maximum
 energy saving percent and the minimum performance degradation percent in the
 same time over all available scales.
-\begin{figure}[width=\textwidth,height=\textheight,keepaspectratio]
+\begin{figure*}
   \centering
-  \includegraphics[scale=0.47]{ep.eps}
-  \includegraphics[scale=0.47]{cg.eps}
-  \includegraphics[scale=0.47]{sp.eps}
-  \includegraphics[scale=0.47]{lu.eps}
-  \includegraphics[scale=0.47]{bt.eps}
-  \includegraphics[scale=0.47]{ft.eps}
+  \includegraphics[width=.33\textwidth]{ep.eps}\hfill%
+  \includegraphics[width=.33\textwidth]{cg.eps}\hfill%
+  \includegraphics[width=.33\textwidth]{sp.eps}
+  \includegraphics[width=.33\textwidth]{lu.eps}\hfill%
+  \includegraphics[width=.33\textwidth]{bt.eps}\hfill%
+  \includegraphics[width=.33\textwidth]{ft.eps}
   \caption{Optimal scaling factors for The NAS MPI Programs}
   \label{fig:nas}
-\end{figure}
-\begin{table}[width=\textwidth,height=\textheight,keepaspectratio]
+\end{figure*}
+\begin{table}
   \caption{Optimal Scaling Factors Results}
   % title of Table
   \centering
@@ -717,15 +731,14 @@ concatenating with less performance degradation and this the objective of this
 paper. While the negative trade offs refers to improving energy saving (or may
 be the performance) while degrading the performance (or may be the energy) more
 than the first.
-\begin{figure}[width=\textwidth,height=\textheight,keepaspectratio]
+\begin{figure*}
   \centering
-  \includegraphics[scale=0.60]{compare_class_A.pdf}
-  \includegraphics[scale=0.60]{compare_class_B.pdf}
-  \includegraphics[scale=0.60]{compare_class_c.pdf}
-  % use scale 35 for all to be in the same line
+  \includegraphics[width=.33\textwidth]{compare_class_A.pdf}\hfill%
+  \includegraphics[width=.33\textwidth]{compare_class_B.pdf}\hfill%
+  \includegraphics[width=.33\textwidth]{compare_class_c.pdf}
   \caption{Comparing Our EPSA with Rauber's Methods}
   \label{fig:compare}
-\end{figure}
+\end{figure*}
 
 \section{Conclusion}
 \label{sec.conc}
@@ -737,8 +750,8 @@ than the first.
 Computations have been performed on the supercomputer facilities of the
 MÃ©socentre de calcul de Franche-ComtÃ©.
 
-\bibliographystyle{plain}
-\bibliography{my_reference}
+\bibliographystyle{IEEEtran}
+\bibliography{IEEEabrv,my_reference}
 \end{document}
 
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