X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/5b2dbb0b083f40235d97c8e8ee2b5834dd2096cc..201b9e3feb0d4318510a67c834f9645f04c4e8d0:/hpcc.tex?ds=inline

diff --git a/hpcc.tex b/hpcc.tex
index 0dccef3..dabef52 100644
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -45,14 +45,14 @@
 
 \begin{document}
 
-\title{Simulation of Asynchronous Iterative Numerical Algorithms Using SimGrid}
+\title{Simulation of Asynchronous Iterative Algorithms Using SimGrid}
 
 \author{%
   \IEEEauthorblockN{%
     Charles Emile Ramamonjisoa\IEEEauthorrefmark{1},
+    Lilia Ziane Khodja\IEEEauthorrefmark{2},
     David Laiymani\IEEEauthorrefmark{1},
-    Arnaud Giersch\IEEEauthorrefmark{1},
-    Lilia Ziane Khodja\IEEEauthorrefmark{2} and
+    Arnaud Giersch\IEEEauthorrefmark{1} and
     RaphaÃ«l Couturier\IEEEauthorrefmark{1}
   }
   \IEEEauthorblockA{\IEEEauthorrefmark{1}%
@@ -71,34 +71,20 @@
 
 \maketitle
 
-\RC{Ordre des auteurs pas dÃ©finitif.}
 \begin{abstract}
-\AG{L'abstract est AMHA incomprÃ©hensible et ne donne pas envie de lire la suite.}
-In recent years, the scalability of large-scale implementation in a 
-distributed environment of algorithms becoming more and more complex has 
-always been hampered by the limits of physical computing resources 
-capacity. One solution is to run the program in a virtual environment 
-simulating a real interconnected computers architecture. The results are 
-convincing and useful solutions are obtained with far fewer resources 
-than in a real platform. However, challenges remain for the convergence 
-and efficiency of a class of algorithms that concern us here, namely 
-numerical parallel iterative algorithms executed in asynchronous mode, 
-especially in a large scale level. Actually, such algorithm requires a 
-balance and a compromise between computation and communication time 
-during the execution. Two important factors determine the success of the 
-experimentation: the convergence of the iterative algorithm on a large 
-scale and the execution time reduction in asynchronous mode. Once again, 
-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 \np[\%]{40} for the algorithm
-execution time in asynchronous mode compared to the synchronous one with 
-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. 
-
-\LZK{Long\ldots}
+
+Synchronous  iterative  algorithms  are  often less  scalable  than  asynchronous
+iterative  ones.  Performing  large  scale experiments  with  different kind  of
+network parameters is not easy  because with supercomputers such parameters are
+fixed. So one  solution consists in using simulations first  in order to analyze
+what parameters  could influence or not  the behaviors of an  algorithm. In this
+paper, we show  that it is interesting to use SimGrid  to simulate the behaviors
+of asynchronous  iterative algorithms. For that,  we compare the  behaviour of a
+synchronous  GMRES  algorithm  with  an  asynchronous  multisplitting  one  with
+simulations  in  which we  choose  some parameters.   Both  codes  are real  MPI
+codes. Experiments allow us to see when the multisplitting algorithm can be more
+efficient than the GMRES one to solve a 3D Poisson problem.
+
 
 % no keywords for IEEE conferences
 % Keywords: Algorithm distributed iterative asynchronous simulation SimGrid
