X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy.git/blobdiff_plain/c00a8e3e79d7a39860d4c701f85adff1cc02788b..068765674898390f6a849e6426474b76e4ebf291:/paper.tex?ds=sidebyside

diff --git a/paper.tex b/paper.tex
index babc12b..bfa1bc1 100644
--- a/paper.tex
+++ b/paper.tex
@@ -14,8 +14,8 @@
 % \usepackage{secdot}
 %\usepackage[font={footnotesize,bt}]{caption}
 %\usepackage[font=scriptsize,labelfont=bf]{caption}
-\usepackage{todonotes}
-\newcommand{\AG}[2][inline]{\todo[color=green!50,#1]{\sffamily\small\textbf{AG:} #2}}
+\usepackage[textsize=footnotesize]{todonotes}
+\newcommand{\AG}[2][inline]{\todo[color=green!50,#1]{\sffamily\textbf{AG:} #2}}
 
 \begin{document}
 
@@ -36,18 +36,20 @@
 
 \maketitle
 
-\AG{``Optimal'' is a bit pretentious in the title}
+\AG{``Optimal'' is a bit pretentious in the title.\\
+  Complete affiliation, add an email address, etc.}
 
 \begin{abstract}
   \AG{complete the abstract\dots}
 \end{abstract}
 
 \section{Introduction}
+\label{sec.intro}
 
 The need for computing power is still increasing and it is not expected to slow
 down in the coming years. To satisfy this demand, researchers and supercomputers
 constructors have been regularly increasing the number of computing cores in
-supercomputers (for example in November 2013, according to the top 500
+supercomputers (for example in November 2013, according to the TOP500
 list~\cite{43}, the Tianhe-2 was the fastest supercomputer. It has more than 3
 millions of cores and delivers more than 33 Tflop/s while consuming 17808
 kW). This large increase in number of computing cores has led to large energy
@@ -82,10 +84,22 @@ this algorithm to seven MPI benchmarks. These MPI programs are the NAS parallel
 benchmarks (NPB v3.3) developed by NASA~\cite{44}. Our experiments are executed
 using the simulator SimGrid/SMPI v3.10~\cite{Casanova:2008:SGF:1397760.1398183}
 over an homogeneous distributed memory architecture. Furthermore, we compare the
-proposed algorithm with Rauber's methods. The comparison's results show that our
+proposed algorithm with Rauber's methods.
+\AG{Add citation for Rauber's methods.  Moreover, Rauber was not alone to to this work (use ``Rauber et al.'', or ``Rauber and Gudula'', or \dots)}
+The comparison's results show that our
 algorithm gives better energy-time trade off.
+%
+\AG{Correctly reword the following}%
+In Section~\ref{sec.relwork} we present works from other
+authors. Then, in Sections~\ref{sec.ptasks} and~\ref{sec.energy}, we
+introduce our model. [\dots] Finally, we conclude in
+Section~\ref{sec.concl}.
 
 \section{Related Works}
+\label{sec.relwork}
+
+\AG{Consider introducing the models (sec.~\ref{sec.ptasks},
+  maybe~\ref{sec.energy}) before related works}
 
 In the this section some heuristics, to compute the scaling factor, are
 presented and classified in two parts : offline and online methods.
@@ -142,6 +156,7 @@ paper.  However, the primary contributions of this paper are:
 \end{enumerate}
 
 \section{Parallel Tasks Execution on Homogeneous Platform}
+\label{sec.ptasks}
 
 A homogeneous cluster consists of identical nodes in terms of the hardware and
 the software. Each node has its own memory and at least one processor which can
@@ -172,6 +187,7 @@ of the program is the execution time of the slowest task as :
 where $T_i$ is the execution time of process $i$.
 
 \section{Energy Model for Homogeneous Platform}
+\label{sec.energy}
 
 The energy consumption by the processor consists of two powers metric: the
 dynamic and the static power. This general power formulation is used by many
@@ -256,6 +272,7 @@ scaling factor as in EQ~(\ref{eq:sopt}).
 \end{equation}
 
 \section{Performance Evaluation of MPI Programs}
+\label{sec.mpip}
 
 The performance (execution time) of the parallel MPI applications are depends on
 the time of the slowest task as in figure~(\ref{fig:homo}). Normally the
@@ -295,6 +312,7 @@ method as we will show in the coming sections. In the next section we make an
 investigation study for the EQ~(\ref{eq:tnew}).
 
 \section{Performance Prediction Verification}
+\label{sec.verif}
 
 In this section we evaluate the precision of our performance prediction methods
 on the NAS benchmark. We use the EQ~(\ref{eq:tnew}) that predicts the execution
@@ -325,6 +343,8 @@ example, we are present the execution times of the NAS benchmarks as in the
 figure~(\ref{fig:pred}).
 
 \section{Performance to Energy Competition}
+\label{sec.compet}
+
 This section demonstrates our approach for choosing the optimal scaling
 factor. This factor gives maximum energy reduction taking into account the
 execution time for both computation and communication times . The relation
@@ -382,10 +402,10 @@ performance as follows :
 \begin{figure*}
   \centering
   \subfloat[Converted Relation.]{%
-    \includegraphics[width=.4\textwidth]{file.eps}\label{fig:r1}}%
+    \includegraphics[width=.33\textwidth]{file.eps}\label{fig:r1}}%
   \qquad%
   \subfloat[Real Relation.]{%
-    \includegraphics[width=.4\textwidth]{file3.eps}\label{fig:r2}}
+    \includegraphics[width=.33\textwidth]{file3.eps}\label{fig:r2}}
   \label{fig:rel}
   \caption{The Energy and Performance Relation}
 \end{figure*}
@@ -409,6 +429,7 @@ objective of this paper and we choose Rauber's model as an example with two
 reasons that mentioned before.
 
 \section{Optimal Scaling Factor for Performance and Energy}
+\label{sec.optim}
 
 In the previous section we described the objective function that satisfy our
 goal in discovering optimal scaling factor for both performance and energy at
@@ -486,6 +507,7 @@ have imbalanced workloads. Then EQ~(\ref{eq:fi}) works in adaptive way to change
 the frequency according to the nodes workloads.
 
 \section{Experimental Results}
+\label{sec.expe}
 
 The proposed EPSA algorithm was applied to seven MPI programs of the NAS
 benchmarks (EP, CG, MG, FT, BT, LU and SP). We work on three classes (A, B and
@@ -500,7 +522,7 @@ detailed characteristics of our platform file are shown in the
 table~(\ref{table:platform}). Each node in the cluster has 18 frequency values
 from 2.5 GHz to 800 MHz with 100 MHz difference between each two successive
 frequencies.
-\begin{table}[ht]
+\begin{table}[htb]
   \caption{Platform File Parameters}
   % title of Table
   \centering
@@ -532,7 +554,7 @@ 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*}
+\begin{figure*}[t]
   \centering
   \includegraphics[width=.33\textwidth]{ep.eps}\hfill%
   \includegraphics[width=.33\textwidth]{cg.eps}\hfill%
@@ -543,7 +565,7 @@ same time over all available scales.
   \caption{Optimal scaling factors for The NAS MPI Programs}
   \label{fig:nas}
 \end{figure*}
-\begin{table}
+\begin{table}[htb]
   \caption{Optimal Scaling Factors Results}
   % title of Table
   \centering
@@ -575,6 +597,7 @@ cases. In EP there are no communications inside the iterations. This make our
 EPSA to selects smaller scaling factor values (inducing smaller energy savings).
 
 \section{Comparing Results}
+\label{sec.compare}
 
 In this section, we compare our EPSA algorithm results with Rauber's
 methods~\cite{3}. He had two scenarios, the first is to reduce energy to optimal
@@ -586,7 +609,7 @@ scenario as $Rauber_{E-P}$. The comparison is made in tables~(\ref{table:compare
   Class A},\ref{table:compare Class B},\ref{table:compare Class C}). These
 tables show the results of our EPSA and Rauber's two scenarios for all the NAS
 benchmarks programs for classes A,B and C.
-\begin{table}[ht]
+\begin{table*}[p]
   \caption{Comparing Results for  The NAS Class A}
   % title of Table
   \centering
@@ -626,8 +649,8 @@ benchmarks programs for classes A,B and C.
   \end{tabular}
   \label{table:compare Class A}
   % is used to refer this table in the text
-\end{table}
-\begin{table}[ht]
+\end{table*}
+\begin{table*}[p]
   \caption{Comparing Results for The NAS Class B}
   % title of Table
   \centering
@@ -667,9 +690,9 @@ benchmarks programs for classes A,B and C.
   \end{tabular}
   \label{table:compare Class B}
   % is used to refer this table in the text
-\end{table}
+\end{table*}
 
-\begin{table}[ht]
+\begin{table*}[p]
   \caption{Comparing Results for The NAS Class C}
   % title of Table
   \centering
@@ -709,7 +732,7 @@ benchmarks programs for classes A,B and C.
   \end{tabular}
 \label{table:compare Class C}
 % is used to refer this table in the text
-\end{table}
+\end{table*}
 As shown in these tables our scaling factor is not optimal for energy saving
 such as Rauber's scaling factor EQ~(\ref{eq:sopt}), but it is optimal for both
 the energy and the performance simultaneously. Our EPSA optimal scaling factors
@@ -731,22 +754,23 @@ 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*}
+\begin{figure}[t]
   \centering
-  \includegraphics[width=.33\textwidth]{compare_class_A.pdf}\hfill%
-  \includegraphics[width=.33\textwidth]{compare_class_B.pdf}\hfill%
+  \includegraphics[width=.33\textwidth]{compare_class_A.pdf}
+  \includegraphics[width=.33\textwidth]{compare_class_B.pdf}
   \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}
+\label{sec.concl}
 
 \AG{the conclusion needs to be written\dots{} one day}
 
 \section*{Acknowledgment}
 
+\AG{Right?}
 Computations have been performed on the supercomputer facilities of the
 MÃ©socentre de calcul de Franche-ComtÃ©.
 
