petites modifs

diff --git a/hpcc.tex b/hpcc.tex
index dabef52b9a0d2946e4ade41b098ecc7339c06d2d..abcf399755d8c18c50ffc6a2164fa02ca32aa2c0 100644 (file)
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -97,7 +97,7 @@ problems raised by  researchers on various scientific disciplines but also by in
 increasing complexity of these requested  applications combined with a continuous increase of their sizes lead to  write
 distributed and parallel algorithms requiring significant hardware  resources (grid computing, clusters, broadband
 network, etc.) but also a non-negligible CPU execution time. We consider in this paper a class of highly efficient
 increasing complexity of these requested  applications combined with a continuous increase of their sizes lead to  write
 distributed and parallel algorithms requiring significant hardware  resources (grid computing, clusters, broadband
 network, etc.) but also a non-negligible CPU execution time. We consider in this paper a class of highly efficient

-parallel algorithms called \emph{numerical iterative algorithms} executed in a distributed environment. As their name
+parallel algorithms called \emph{iterative algorithms} executed in a distributed environment. As their name

 suggests, these algorithms solve a given problem by successive iterations ($X_{n +1} = f(X_{n})$) from an initial value
 $X_{0}$ to find an approximate value $X^*$ of the solution with a very low residual error. Several well-known methods
 demonstrate the convergence of these algorithms~\cite{BT89,Bahi07}.
 suggests, these algorithms solve a given problem by successive iterations ($X_{n +1} = f(X_{n})$) from an initial value
 $X_{0}$ to find an approximate value $X^*$ of the solution with a very low residual error. Several well-known methods
 demonstrate the convergence of these algorithms~\cite{BT89,Bahi07}.


@@ -113,34 +113,34 @@ at that time. Even if the number of iterations required before the convergence i
 synchronous case, AIAC algorithms can significantly reduce overall execution times by suppressing idle times due to
 synchronizations especially in a grid computing context (see~\cite{Bahi07} for more details).
 
 synchronous case, AIAC algorithms can significantly reduce overall execution times by suppressing idle times due to
 synchronizations especially in a grid computing context (see~\cite{Bahi07} for more details).
 

-Parallel numerical applications (synchronous or asynchronous) may have different
-configuration and deployment requirements.  Quantifying their resource
-allocation policies and application scheduling algorithms in grid computing
-environments under varying load, CPU power and network speeds is very costly,
-very labor intensive and very time
-consuming~\cite{Calheiros:2011:CTM:1951445.1951450}.  The case of AIAC
-algorithms is even more problematic since they are very sensible to the
+Parallel   (synchronous  or  asynchronous)   applications  may   have  different
+configuration   and  deployment   requirements.    Quantifying  their   resource
+allocation  policies and  application  scheduling algorithms  in grid  computing
+environments under  varying load, CPU power  and network speeds  is very costly,
+very          labor           intensive          and          very          time
+consuming~\cite{Calheiros:2011:CTM:1951445.1951450}.     The   case    of   AIAC
+algorithms  is  even  more problematic  since  they  are  very sensible  to  the

 execution environment context. For instance, variations in the network bandwidth
 execution environment context. For instance, variations in the network bandwidth

-(intra and inter-clusters), in the number and the power of nodes, in the number
-of clusters\dots{} can lead to very different number of iterations and so to
-very different execution times. Then, it appears that the use of simulation
-tools to explore various platform scenarios and to run large numbers of
-experiments quickly can be very promising. In this way, the use of a simulation
-environment to execute parallel iterative algorithms found some interests in
-reducing the highly cost of access to computing resources: (1) for the
-applications development life cycle and in code debugging (2) and in production
-to get results in a reasonable execution time with a simulated infrastructure
-not accessible with physical resources. Indeed, the launch of distributed
-iterative asynchronous algorithms to solve a given problem on a large-scale
-simulated environment challenges to find optimal configurations giving the best
+(intra and inter-clusters), in the number  and the power of nodes, in the number
+of clusters\dots{}  can lead to  very different number  of iterations and  so to
+very  different execution times.  Then, it  appears that  the use  of simulation
+tools  to  explore  various platform  scenarios  and  to  run large  numbers  of
+experiments quickly can be very promising.  In this way, the use of a simulation
+environment  to execute parallel  iterative algorithms  found some  interests in
+reducing  the  highly  cost  of  access  to computing  resources:  (1)  for  the
+applications development life cycle and  in code debugging (2) and in production
+to get  results in a reasonable  execution time with  a simulated infrastructure
+not  accessible  with physical  resources.  Indeed,  the  launch of  distributed
+iterative  asynchronous algorithms  to solve  a given  problem on  a large-scale
+simulated environment challenges to  find optimal configurations giving the best

 results with a lowest residual error and in the best of execution time.
 
 To our knowledge, there is no existing work on the large-scale simulation of a
 results with a lowest residual error and in the best of execution time.
 
 To our knowledge, there is no existing work on the large-scale simulation of a

-real AIAC application. The aim of this paper is twofold. First we give a first
+real AIAC application. There are {\bf two contributions} in this paper. First we give a first

 approach of the simulation of AIAC algorithms using a simulation tool (i.e. the
 approach of the simulation of AIAC algorithms using a simulation tool (i.e. the

-SimGrid toolkit~\cite{SimGrid}). Second, we confirm the effectiveness of
-asynchronous mode algorithms by comparing their performance with the synchronous
-mode. More precisely, we had implemented a program for solving large
+SimGrid toolkit~\cite{SimGrid}). Second, we confirm the effectiveness of the
+asynchronous multisplitting algorithm by comparing its performance with the synchronous
+GMRES. More precisely, we had implemented a program for solving large

 linear system of equations by numerical method GMRES (Generalized
 Minimal Residual) \cite{ref1}. We show, that with minor modifications of the
 initial MPI code, the SimGrid toolkit allows us to perform a test campaign of a
 linear system of equations by numerical method GMRES (Generalized
 Minimal Residual) \cite{ref1}. We show, that with minor modifications of the
 initial MPI code, the SimGrid toolkit allows us to perform a test campaign of a