From: David Laiymani <david.laiymani@univ-fcomte.fr>
Date: Tue, 28 Apr 2015 12:13:38 +0000 (+0200)
Subject: Premier draft de l'intro
X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/rce2015.git/commitdiff_plain/f721cbed7d2388830aaa885dc59b8a9bab852a32?ds=inline

Premier draft de l'intro
---

diff --git a/paper.tex b/paper.tex
index 73c98e4..87949b4 100644
--- a/paper.tex
+++ b/paper.tex
@@ -113,6 +113,47 @@ simulation tool before.
 \maketitle
 
 \section{Introduction} 
+The use of multi-core architectures for solving large scientific problems seems to  become imperative  in  a  lot  of  cases. 
+Whatever the scale of these architectures (distributed clusters, computational grids, embedded multi-core \ldots) they  are generally 
+well adapted to execute complexe parallel applications operating on a large amount of data.  Unfortunately,  users (industrials or scientists), 
+who need such computational resources may not have an easy access to such efficient architectures. The cost of using the platform and/or the cost of 
+testing and deploying an application are often very important.  So, in this context it is difficult to optimize a given application for a given 
+architecture. In this way and in order to reduce the access cost to these computing resources it seems very interesting to use a simulation environment. 
+The advantages are numerous: development life cycle, code debugging, ability to obtain results quickly \ldots
+
+In this paper we focus on a class of highly efficient parallel algorithms called \emph{iterative algorithms}. The
+parallel scheme of iterative methods is quite simple. It generally involves the division of the problem
+into  several  \emph{blocks}  that  will  be  solved  in  parallel  on  multiple
+processing units.  Then each processing unit has to
+compute an iteration, to send/receive some data dependencies to/from
+its neighbors and to iterate this process until the convergence of
+the method. Several well-known methods demonstrate the convergence of these algorithms~\cite{BT89,Bahi07}.
+In this processing mode a task cannot begin a new iteration while it
+has not received data dependencies from its neighbors. We say that the iteration computation follows a synchronous scheme.
+In the asynchronous scheme a task can compute a new iteration without having to
+wait for the data dependencies coming from its neighbors. Both
+communication and computations are asynchronous inducing that there is
+no more idle times, due to synchronizations, between two
+iterations~\cite{bcvc06:ij}. This model presents some advantages and drawbacks that we detail in section 2 but even if the number of iterations required to converge is 
+generally  greater  than for the synchronous  case, it appears that the asynchronous  iterative scheme  can significantly  reduce  overall execution
+times by  suppressing idle  times due to  synchronizations~\cite{Bahi07} for more details.
+
+Nevertheless, in both cases (synchronous or asynchronous) it is very time consuming to find optimal configuration and deployment requirements 
+for a given application on a given multi-core architecture. Finding good resource allocations policies under varying CPU power, network speeds and 
+loads is very challenging and labor intensive.~\cite{Calheiros:2011:CTM:1951445.1951450}.   This problematic is even more difficult for the asynchronous scheme 
+where variations of the parameters of the execution platform can lead to very different number of iterations required to converge and so to very different execution times.
+In this challenging context we think that the use of a simulation tool can leverage the possibility of testing various platform scenarios.
+
+The main contribution of this paper is to show that the use of a simulation tool (i.e. the SimGrid   toolkit~\cite{SimGrid}) in the context of real 
+parallel applications (i.e. large linear system solver) can help developers to better tune their application for a given multi-core architecture.
+To show the validity of this approach we first compare the simulated execution of the multisplitting algorithm  with  the  GMRES   (Generalized   Minimal  Residual) solver
+\cite{ref1} both in synchronous mode. The obtained results on different simulated multi-core architectures confirm the results previously obtained on non simulated architecture. 
+We also confirm  the efficiency  of the  asynchronous  multisplitting algorithm  comparing to the synchronous  GMRES. In this way and with a simple computing architecture (a laptop) 
+SimGrid allows us (with small modifications of the MPI code) to run a test campaign  of  a  real parallel iterative  applications on  different simulated multi-core architectures. 
+To our knowledge, there is no related work on the large-scale multi-core simulation of a real synchronous and asynchronous iterative application.  
+
+This paper is organized as follows:
+
 
 \section{The asynchronous iteration model}