Update remarks.

[hpcc2014.git] / hpcc.tex

diff --git a/hpcc.tex b/hpcc.tex
index 207c56083c6e498dadd6fd3b166a6662938615b0..94e9ee71ea351c901c920d16ab0d5dec4fa37d44 100644 (file)
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -25,10 +25,12 @@
 \usepackage[textsize=footnotesize]{todonotes}
 \newcommand{\AG}[2][inline]{%
   \todo[color=green!50,#1]{\sffamily\textbf{AG:} #2}\xspace}
 \usepackage[textsize=footnotesize]{todonotes}
 \newcommand{\AG}[2][inline]{%
   \todo[color=green!50,#1]{\sffamily\textbf{AG:} #2}\xspace}

-\newcommand{\RC}[2][inline]{%
-  \todo[color=red!10,#1]{\sffamily\textbf{RC:} #2}\xspace}
+\newcommand{\DL}[2][inline]{%
+  \todo[color=yellow!50,#1]{\sffamily\textbf{DL:} #2}\xspace}

 \newcommand{\LZK}[2][inline]{%
   \todo[color=blue!10,#1]{\sffamily\textbf{LZK:} #2}\xspace}
 \newcommand{\LZK}[2][inline]{%
   \todo[color=blue!10,#1]{\sffamily\textbf{LZK:} #2}\xspace}

+\newcommand{\RC}[2][inline]{%
+  \todo[color=red!10,#1]{\sffamily\textbf{RC:} #2}\xspace}

 
 \algnewcommand\algorithmicinput{\textbf{Input:}}
 \algnewcommand\Input{\item[\algorithmicinput]}
 
 \algnewcommand\algorithmicinput{\textbf{Input:}}
 \algnewcommand\Input{\item[\algorithmicinput]}


@@ -45,24 +47,29 @@
 
 \author{%
   \IEEEauthorblockN{%
 
 \author{%
   \IEEEauthorblockN{%

-    Charles Emile Ramamonjisoa and
-    David Laiymani and
-    Arnaud Giersch and
-    Lilia Ziane Khodja and
-    Raphaël Couturier
+    Charles Emile Ramamonjisoa\IEEEauthorrefmark{1},
+    David Laiymani\IEEEauthorrefmark{1},
+    Arnaud Giersch\IEEEauthorrefmark{1},
+    Lilia Ziane Khodja\IEEEauthorrefmark{2} and
+    Raphaël Couturier\IEEEauthorrefmark{1}
+  }
+  \IEEEauthorblockA{\IEEEauthorrefmark{1}%
+    Femto-ST Institute -- DISC Department\\
+    Université de Franche-Comté,
+    IUT de Belfort-Montbéliard\\
+    19 avenue du Maréchal Juin, BP 527, 90016 Belfort cedex, France\\
+    Email: \email{{charles.ramamonjisoa,david.laiymani,arnaud.giersch,raphael.couturier}@univ-fcomte.fr}

   }
   }

-  \IEEEauthorblockA{%
-    Femto-ST Institute - DISC Department\\
-    Université de Franche-Comté\\
-    Belfort\\
-    Email: \email{{raphael.couturier,arnaud.giersch,david.laiymani,charles.ramamonjisoa}@univ-fcomte.fr}
+  \IEEEauthorblockA{\IEEEauthorrefmark{2}%
+    Inria Bordeaux Sud-Ouest\\
+    200 avenue de la Vieille Tour, 33405 Talence cedex, France \\
+    Email: \email{lilia.ziane@inria.fr}

   }
 }
 
 \maketitle
 
 \RC{Ordre des autheurs pas définitif.}
   }
 }
 
 \maketitle
 
 \RC{Ordre des autheurs pas définitif.}

-\LZK{Adresse de Lilia: Inria Bordeaux Sud-Ouest, 200 Avenue de la Vieille Tour, 33405 Talence Cedex, France \\ Email: lilia.ziane@inria.fr}

 \begin{abstract}
 ABSTRACT
 
 \begin{abstract}
 ABSTRACT
 


@@ -84,15 +91,14 @@ from the current work, a simulated environment like Simgrid provides
 accurate results which are difficult or even impossible to obtain in a 
 physical platform by exploiting the flexibility of the simulator on the 
 computing units clusters and the network structure design. Our 
 accurate results which are difficult or even impossible to obtain in a 
 physical platform by exploiting the flexibility of the simulator on the 
 computing units clusters and the network structure design. Our 

-experimental outputs showed a saving of up to 40 \% for the algorithm 
+experimental outputs showed a saving of up to \np[\%]{40} for the algorithm

 execution time in asynchronous mode compared to the synchronous one with 
 execution time in asynchronous mode compared to the synchronous one with 

-a residual precision up to E-11. Such successful results open 
+a residual precision up to \np{E-11}. Such successful results open

 perspectives on experimentations for running the algorithm on a 
 simulated large scale growing environment and with larger problem size. 
 
 perspectives on experimentations for running the algorithm on a 
 simulated large scale growing environment and with larger problem size. 
 

-Keywords : Algorithm distributed iterative asynchronous simulation 
-simgrid
-
+% no keywords for IEEE conferences
+% Keywords: Algorithm distributed iterative asynchronous simulation simgrid

 \end{abstract}
 
 \section{Introduction}
 \end{abstract}
 
 \section{Introduction}


@@ -165,16 +171,11 @@ our future work after the results.
  
 \section{The asynchronous iteration model}
 
  
 \section{The asynchronous iteration model}
 

-Décrire le modèle asynchrone. Je m'en charge (DL)
+\DL{Décrire le modèle asynchrone. Je m'en charge}

 
 \section{SimGrid}
 
 
 \section{SimGrid}
 

-Décrire SimGrid~\cite{casanova+legrand+quinson.2008.simgrid} (Arnaud)
-
-
-
-
-
+\AG{Décrire SimGrid~\cite{casanova+legrand+quinson.2008.simgrid} (Arnaud)}

 
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


@@ -199,7 +200,7 @@ B_1 \\
 \vdots\\
 B_L
 \end{array} \right)\] 
 \vdots\\
 B_L
 \end{array} \right)\] 

-in such a way that successive rows of matrix $A$ and both vectors $x$ and $b$ are assigned to one cluster, where for all $l,m\in\{1,\ldots,L\}$ $A_{lm}$ is a rectangular block of $A$ of size $n_l\times n_m$, $X_l$ and $B_l$ are sub-vectors of $x$ and $b$, respectively, each of size $n_l$ and $\sum_{l} n_l=\sum_{m} n_m=n$.
+in such a way that successive rows of matrix $A$ and both vectors $x$ and $b$ are assigned to one cluster, where for all $l,m\in\{1,\ldots,L\}$ $A_{lm}$ is a rectangular block of $A$ of size $n_l\times n_m$, $X_l$ and $B_l$ are sub-vectors of $x$ and $b$, respectively, of size $n_l$ each and $\sum_{l} n_l=\sum_{m} n_m=n$.

 
 The multisplitting method proceeds by iteration to solve in parallel the linear system on $L$ clusters of processors, in such a way each sub-system
 \begin{equation}
 
 The multisplitting method proceeds by iteration to solve in parallel the linear system on $L$ clusters of processors, in such a way each sub-system
 \begin{equation}


@@ -327,7 +328,10 @@ lat latency, \dots{}).
   \centering
   \caption{2 clusters X 50 nodes}
   \label{tab.cluster.2x50}
   \centering
   \caption{2 clusters X 50 nodes}
   \label{tab.cluster.2x50}

-  \AG{Les images manquent dans le dépôt Git. Si ce sont vraiment des tableaux, utiliser un format vectoriel (eps ou pdf), et surtout pas de jpeg!}
+  \AG{Ces tableaux (\ref{tab.cluster.2x50}, \ref{tab.cluster.3x33} et
+    \ref{tab.cluster.3x67}) sont affreux. Utiliser un format vectoriel (eps ou
+    pdf) ou, mieux, les réécrire en \LaTeX{}. Réécrire les légendes proprement
+    également (\texttt{\textbackslash{}times} au lieu de \texttt{X} par ex.)}

   \includegraphics[width=209pt]{img1.jpg}
 \end{table}
 
   \includegraphics[width=209pt]{img1.jpg}
 \end{table}
 


@@ -335,7 +339,7 @@ lat latency, \dots{}).
   \centering
   \caption{3 clusters X 33 nodes}
   \label{tab.cluster.3x33}
   \centering
   \caption{3 clusters X 33 nodes}
   \label{tab.cluster.3x33}

-  \AG{Le fichier manque.}
+  \AG{Refaire le tableau.}

   \includegraphics[width=209pt]{img2.jpg}
 \end{table}
 
   \includegraphics[width=209pt]{img2.jpg}
 \end{table}
 


@@ -343,7 +347,7 @@ lat latency, \dots{}).
   \centering
   \caption{3 clusters X 67 nodes}
   \label{tab.cluster.3x67}
   \centering
   \caption{3 clusters X 67 nodes}
   \label{tab.cluster.3x67}

-  \AG{Le fichier manque.}
+  \AG{Refaire le tableau.}

 %  \includegraphics[width=160pt]{img3.jpg}
   \includegraphics[scale=0.5]{img3.jpg}
 \end{table}
 %  \includegraphics[width=160pt]{img3.jpg}
   \includegraphics[scale=0.5]{img3.jpg}
 \end{table}


@@ -373,7 +377,7 @@ For the 3 clusters architecture including a total of 100 hosts, Table~\ref{tab.c
 that it was difficult to have a combination which gives an efficiency of
 asynchronous below \np[\%]{80}. Indeed, for a matrix size of 62 elements, equality
 between the performance of the two modes (synchronous and asynchronous) is
 that it was difficult to have a combination which gives an efficiency of
 asynchronous below \np[\%]{80}. Indeed, for a matrix size of 62 elements, equality
 between the performance of the two modes (synchronous and asynchronous) is

-achieved with an inter cluster of \np[Mbits/s]{10} and a latency of \np{E-1} ms. To
+achieved with an inter cluster of \np[Mbits/s]{10} and a latency of \np[ms]{E-1}. To

 challenge an efficiency by \np[\%]{78} with a matrix size of 100 points, it was
 necessary to degrade the inter cluster network bandwidth from 5 to 2 Mbit/s.
 
 challenge an efficiency by \np[\%]{78} with a matrix size of 100 points, it was
 necessary to degrade the inter cluster network bandwidth from 5 to 2 Mbit/s.
 


@@ -403,7 +407,7 @@ executing the algorithm in asynchronous mode.
 \setcounter{numberedCntD}{\theenumi}
 \end{enumerate}
 Our results have shown that in certain conditions, asynchronous mode is 
 \setcounter{numberedCntD}{\theenumi}
 \end{enumerate}
 Our results have shown that in certain conditions, asynchronous mode is 

-speeder up to 40 \% than executing the algorithm in synchronous mode 
+speeder up to \np[\%]{40} than executing the algorithm in synchronous mode

 which is not negligible for solving complex practical problems with more 
 and more increasing size.
 
 which is not negligible for solving complex practical problems with more 
 and more increasing size.