X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/e8f6905d359fdb53a9e1446305d3f5267573c703..51c03eb8a38bbfba54b1e2fadca7ab7b1db166b3:/hpcc.tex?ds=sidebyside

diff --git a/hpcc.tex b/hpcc.tex
index 1aae53a..7d96f2b 100644
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -82,8 +82,8 @@ 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. Simulations allow us to see when the multisplitting algorithm can be more
+simulations  which let us easily choose  some parameters.   Both  codes  are real  MPI
+codes ans simulations allow us to see when the asynchronous multisplitting algorithm can be more
 efficient than the GMRES one to solve a 3D Poisson problem.
 
 
@@ -691,13 +691,15 @@ elements.
 %\CER{Définitivement, les paramètres réseaux variables ici se rapportent au réseau INTER cluster.}
 \section{Conclusion}
 The simulation of the execution of parallel asynchronous iterative algorithms on large scale  clusters has been presented. 
-Our work has demonstrated that using such a simulation tool allow us to 
+In this work, we show that SIMGRID is an efficient simulation tool that allows us to 
 reach the following three objectives: 
 
 \begin{enumerate}
-\item To have a flexible configurable execution platform resolving the 
-hard exercise to access to very limited but so solicited physical 
-resources;
+\item  To have  a flexible  configurable execution  platform that  allows  us to
+  simulate asynchronous iterative algorithm for  which execution of all parts of
+  the  code is  necessary. Using  simulations before  real execution  is  a nice
+  solution to detect the scalability problems.
+
 \item to ensure the algorithm convergence with a reasonable time and
 iteration number ;
 \item and finally and more importantly, to find the correct combination 
@@ -705,17 +707,18 @@ of the cluster and network specifications permitting to save time in
 executing the algorithm in asynchronous mode.
 \end{enumerate}
 Our results have shown that in certain conditions, asynchronous mode is 
-speeder up to \np[\%]{40} than executing the algorithm in synchronous mode
+speeder up to \np[\%]{40} comparing to the synchronous GMRES method
 which is not negligible for solving complex practical problems with more 
 and more increasing size.
 
- Several studies have already addressed the performance execution time of 
+Several studies have already addressed the performance execution time of 
 this class of algorithm. The work presented in this paper has 
 demonstrated an original solution to optimize the use of a simulation 
 tool to run efficiently an iterative parallel algorithm in asynchronous 
 mode in a grid architecture. 
 
-\LZK{Perspectives???}
+In future works, we plan to extend our experimentations to larger scale platforms by increasing the number of computing cores and the number of clusters. 
+We will also have to increase the size of the input problem which will require the use of a more powerful simulation platform. At last, we expect to compare our simulation results to real execution results on real architectures in order to experimentally validate our study.
 
 \section*{Acknowledgment}