X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/8b34ea2b1bad6b4287c588f79dab03ee382b66a8..cde7fbb5fc62299aef64b60c08055ed400b93bc4:/hpcc.tex

diff --git a/hpcc.tex b/hpcc.tex
index 207c560..f470143 100644
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -45,24 +45,29 @@
 
 \author{%
   \IEEEauthorblockN{%
-    Charles Emile Ramamonjisoa and
-    David Laiymani and
-    Arnaud Giersch and
-    Lilia Ziane Khodja and
-    RaphaÃ«l Couturier
+    Charles Emile Ramamonjisoa\IEEEauthorrefmark{1},
+    David Laiymani\IEEEauthorrefmark{1},
+    Arnaud Giersch\IEEEauthorrefmark{1},
+    Lilia Ziane Khodja\IEEEauthorrefmark{2} and
+    RaphaÃ«l Couturier\IEEEauthorrefmark{1}
   }
-  \IEEEauthorblockA{%
-    Femto-ST Institute - DISC Department\\
-    UniversitÃ© de Franche-ComtÃ©\\
-    Belfort\\
-    Email: \email{{raphael.couturier,arnaud.giersch,david.laiymani,charles.ramamonjisoa}@univ-fcomte.fr}
+  \IEEEauthorblockA{\IEEEauthorrefmark{1}%
+    Femto-ST Institute -- DISC Department\\
+    UniversitÃ© de Franche-ComtÃ©,
+    IUT de Belfort-MontbÃ©liard\\
+    19 avenue du MarÃ©chal Juin, BP 527, 90016 Belfort cedex, France\\
+    Email: \email{{charles.ramamonjisoa,david.laiymani,arnaud.giersch,raphael.couturier}@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 dÃ©finitif.}
-\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
 
@@ -84,9 +89,9 @@ 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 
-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 
-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. 
 
@@ -199,7 +204,7 @@ B_1 \\
 \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}
@@ -373,7 +378,7 @@ 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
-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.
 
@@ -403,7 +408,7 @@ executing the algorithm in asynchronous mode.
 \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.
 
