X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy.git/blobdiff_plain/5cbb41e9f46ba582a492088627d2e1533092f205..b2e13cc196d2e0b917988a193d737cdcb4d983ee:/paper.tex?ds=sidebyside

diff --git a/paper.tex b/paper.tex
index 332f1f1..d7ced09 100644
--- a/paper.tex
+++ b/paper.tex
@@ -9,11 +9,10 @@
 \usepackage{listings}
 \usepackage{colortbl}
 \usepackage{amsmath}
-% \usepackage{sectsty}
-% \usepackage{titlesec}
-% \usepackage{secdot}
-%\usepackage[font={footnotesize,bt}]{caption}
-%\usepackage[font=scriptsize,labelfont=bf]{caption}
+
+\usepackage[autolanguage,np]{numprint}
+\renewcommand*\npunitcommand[1]{\text{#1}}
+
 \usepackage{xspace}
 \usepackage[textsize=footnotesize]{todonotes}
 \newcommand{\AG}[2][inline]{\todo[color=green!50,#1]{\sffamily\textbf{AG:} #2}\xspace}
@@ -314,7 +313,7 @@ communication time consists of the beginning times which an MPI calls for
 sending or receiving till the message is synchronously sent or received. In this
 paper we predict the execution time of the program for any new scaling factor
 value. Depending on this prediction we can produce our energy-performance scaling
-method as we will show in the coming sections. In the next section we make an
+method as we will show in the coming sections. In the next section we make to finishan
 investigation study for the EQ~(\ref{eq:tnew}).
 
 \section{Performance Prediction Verification}
@@ -525,11 +524,11 @@ frequencies.
   \caption{Platform File Parameters}
   % title of Table
   \centering
-  \begin{tabular}{ | l | l | l |l | l |l |l |  p{2cm} |}
+  \begin{tabular}{|*{7}{l|}}
     \hline
     Max & Min & Backbone & Backbone&Link &Link& Sharing  \\
     Freq. & Freq. & Bandwidth & Latency & Bandwidth& Latency&Policy  \\ \hline
-    2.5 &800 & 2.25 GBps &$5\times 10^{-7} s$& 1 GBps & $5\times 10^{-5} s$ &Full  \\
+    \np{2.5} & \np{800} & \np[GBps]{2.25} &\np[$\mu$s]{0.5}& \np[GBps]{1} & \np[$\mu$s]{50} &Full  \\
     GHz& MHz&  & & &  &Duplex  \\\hline
   \end{tabular}
   \label{table:platform}
@@ -545,7 +544,7 @@ inversed performance curves, because there are different communication features
 for each MPI program.  When there are little or not communications, the inversed
 performance curve is very close to the energy curve. Then the distance between
 the two curves is very small. This lead to small energy savings. The opposite
-happens when there are a lot of communication, the distance between the two
+happens when there are a lot of communication, theto finish distance between the two
 curves is big.  This lead to more energy savings (e.g. CG and FT), see
 table~(\ref{table:factors results}). All discovered frequency scaling factors
 optimize both the energy and the performance simultaneously for all the NAS
@@ -565,13 +564,10 @@ same time over all available scales.
   \label{fig:nas}
 \end{figure*}
 \begin{table}[htb]
-  \caption{Optimal Scaling Factors Results}
+  \caption{The EPSA Scaling Factors Results}
   % title of Table
   \centering
-  \AG{Use the same number of decimals for all numbers in a column,
-    and vertically align the numbers along the decimal points.
-    The same for all the following tables.}
-  \begin{tabular}{ | l | l | l |l | r |}
+  \begin{tabular}{|l|*{4}{r|}}
     \hline
     Program & Optimal & Energy  & Performance&Energy-Perf.\\
     Name & Scaling Factor& Saving \%&Degradation \% &Distance  \\ \hline
@@ -612,7 +608,7 @@ benchmarks programs for classes A,B and C.
   \caption{Comparing Results for  The NAS Class A}
   % title of Table
   \centering
-  \begin{tabular}{ | l | l | l |l | l | r|  }
+  \begin{tabular}{|l|l|*{4}{r|}}
     \hline
     Method&Program&Factor& Energy& Performance &Energy-Perf.\\
     Name &Name&Value& Saving \%&Degradation \% &Distance
@@ -653,7 +649,7 @@ benchmarks programs for classes A,B and C.
   \caption{Comparing Results for The NAS Class B}
   % title of Table
   \centering
-  \begin{tabular}{ | l | l | l |l | l |r|  }
+  \begin{tabular}{|l|l|*{4}{r|}}
     \hline
     Method&Program&Factor& Energy& Performance &Energy-Perf.\\
     Name &Name&Value& Saving \%&Degradation \% &Distance
@@ -695,7 +691,7 @@ benchmarks programs for classes A,B and C.
   \caption{Comparing Results for The NAS Class C}
   % title of Table
   \centering
-  \begin{tabular}{ | l | l | l |l | l |r|  }
+  \begin{tabular}{|l|l|*{4}{r|}}
     \hline
     Method&Program&Factor& Energy& Performance &Energy-Perf.\\
     Name &Name&Value& Saving \%&Degradation \% &Distance
@@ -761,17 +757,17 @@ than the first.
   \caption{Comparing Our EPSA with Rauber and RÃ¼nger Methods}
   \label{fig:compare}
 \end{figure}
-
 \section{Conclusion}
 \label{sec.concl}
-
-\AG{the conclusion needs to be written\dots{} one day}
+In this paper we develop the simultaneous energy-performance algorithm. It is works based on the trade off relation between the energy and performance. The results showed that when the scaling factor is big value leads to more energy saving. Also, it show that when the the scaling factor is small value leads to the fact that the scaling factor has bigger impact on performance than energy. Then the algorithm optimize the energy saving and performance in the same time to have positive trade off. The optimal trade off refer to maximum distance between the energy and the inversed performance curves. Also, the results explained when setting the slowest task to maximum frequency usually not have a big improvement on performance. 
 
 \section*{Acknowledgment}
 
 \AG{Right?}
 Computations have been performed on the supercomputer facilities of the
-MÃ©socentre de calcul de Franche-ComtÃ©.
+MÃ©socentre de calcul de Franche-ComtÃ©. As a PhD student , M. Ahmed Fanfakh , would 
+likes to thank the University of Babylon /Iraq for supporting  my scholarship program that allows  me  
+working on this paper.    
 
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