modif intro

[hpcc2014.git] / hpcc.tex

diff --git a/hpcc.tex b/hpcc.tex
index d60fd6ae91906910775825f2aadf108178e7aadb..29d00a12b6988e8e5291c2f29a9b6d550054f356 100644 (file)
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -45,7 +45,7 @@
 
 \begin{document}
 
 
 \begin{document}
 

-\title{Simulation of Asynchronous Iterative Numerical Algorithms Using SimGrid}
+\title{Simulation of Asynchronous Iterative Algorithms Using SimGrid}

 
 \author{%
   \IEEEauthorblockN{%
 
 \author{%
   \IEEEauthorblockN{%


@@ -73,17 +73,17 @@
 
 \begin{abstract}
 
 
 \begin{abstract}
 

-Synchronous  iterative  algorithms  is  often less  scalable  than  asynchronous
+Synchronous  iterative  algorithms  are  often less  scalable  than  asynchronous

 iterative  ones.  Performing  large  scale experiments  with  different kind  of
 iterative  ones.  Performing  large  scale experiments  with  different kind  of

-networks parameters is not easy  because with supercomputers such parameters are
+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
 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
-efficience than the GMRES one to solve a 3D Poisson problem.
+codes. Simulations 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
 
 
 % no keywords for IEEE conferences


@@ -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}.


@@ -113,57 +113,60 @@ 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.
 
 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
-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
-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
-real AIAC application on different computing architectures. The simulated
-results we obtained are in line with real results exposed in ??\AG[]{ref?}.
-SimGrid had allowed us to launch the application from a modest computing
-infrastructure by simulating different distributed architectures composed by
-clusters nodes interconnected by variable speed networks.  With selected
-parameters on the network platforms (bandwidth, latency of inter cluster
-network) and on the clusters architecture (number, capacity calculation power)
-in the simulated environment, the experimental results have demonstrated not
-only the algorithm convergence within a reasonable time compared with the
-physical environment performance, but also a time saving of up to \np[\%]{40} in
-asynchronous mode.
-\AG{Il faudrait revoir la phrase précédente (couper en deux?).  Là, on peut
-  avoir l'impression que le gain de \np[\%]{40} est entre une exécution réelle
-  et une exécution simulée!}
-
-This article is structured as follows: after this introduction, the next  section will give a brief description of
-iterative asynchronous model.  Then, the simulation framework SimGrid is presented with the settings to create various
-distributed architectures. The algorithm of  the multisplitting method used by GMRES \LZK{??? GMRES n'utilise pas la méthode de multisplitting! Sinon ne doit on pas expliquer le choix d'une méthode de multisplitting?} written with MPI primitives and
-its adaptation to SimGrid with SMPI (Simulated MPI) is detailed in the next section. At last, the experiments results
-carried out will be presented before some concluding remarks and future works.
+To our knowledge,  there is no existing work on the  large-scale simulation of a
+real  AIAC application.   {\bf  The contribution  of  the present  paper can  be
+  summarised  in two  main  points}.  First  we  give a  first  approach of  the
+simulation  of  AIAC algorithms  using  a  simulation  tool (i.e.   the  SimGrid
+toolkit~\cite{SimGrid}).    Second,  we   confirm  the   effectiveness   of  the
+asynchronous  multisplitting algorithm  by  comparing its  performance with  the
+synchronous GMRES (Generalized Minimal  Residual) \cite{ref1}.  Both these codes
+can be  used to  solve large linear  systems. In  this paper, we  focus on  a 3D
+Poisson  problem.  We show,  that with  minor modifications  of the  initial MPI
+code, the SimGrid  toolkit allows us to  perform a test campaign of  a real AIAC
+application on different computing architectures.
+% The  simulated results  we
+%obtained are  in line with real  results exposed in  ??\AG[]{ref?}. 
+SimGrid  had  allowed us  to  launch the  application  from  a modest  computing
+infrastructure  by simulating  different distributed  architectures  composed by
+clusters  nodes interconnected by  variable speed  networks.  Parameters  of the
+network  platforms  are   the  bandwidth  and  the  latency   of  inter  cluster
+network. Parameters on the cluster's architecture are the number of machines and
+the  computation power  of a  machine.  Simulations show  that the  asynchronous
+multisplitting algorithm  can solve the  3D Poisson problem  approximately twice
+faster than GMRES with two distant clusters.
+
+
+
+This article is structured as follows: after this introduction, the next section
+will  give a  brief  description  of iterative  asynchronous  model.  Then,  the
+simulation framework  SimGrid is presented  with the settings to  create various
+distributed architectures.  Then, the  multisplitting method is presented, it is
+based  on GMRES to  solve each  block obtained  of the  splitting. This  code is
+written with MPI  primitives and its adaptation to  SimGrid with SMPI (Simulated
+MPI) is  detailed in the next  section. At last, the  simulation results carried
+out will be presented before some concluding remarks and future works.

  
 \section{Motivations and scientific context}
 
  
 \section{Motivations and scientific context}