DL : modif partie asynchrone : ajout des motivations

diff --git a/paper.tex b/paper.tex
index 34f7ec75f8636c2ae3278b1e157868fb86a8ffaa..31fd190115a09a3a7023c94c92c173d6d4ef0ac7 100644 (file)
--- a/paper.tex
+++ b/paper.tex
@@ -192,7 +192,7 @@ experimental results are presented in section~\ref{sec:expe} followed by some
 concluding remarks and perspectives.
 
 
 concluding remarks and perspectives.
 
 

-\section{The asynchronous iteration model}
+\section{The asynchronous iteration model and the motivations of our work}

 \label{sec:asynchro}
 
 Asynchronous iterative methods have been  studied for many years theoritecally and
 \label{sec:asynchro}
 
 Asynchronous iterative methods have been  studied for many years theoritecally and


@@ -216,6 +216,21 @@ point. In the  asynchronous model, the convergence detection is  more tricky as
 it   must  not   synchronize  all   the  processors.   Interested  readers   can
 consult~\cite{myBCCV05c,bahi07,ccl09:ij}.
 
 it   must  not   synchronize  all   the  processors.   Interested  readers   can
 consult~\cite{myBCCV05c,bahi07,ccl09:ij}.
 

+The number of iterations required to reach the convergence is generally greater
+for the asynchronous scheme (this number depends depends on  the delay of the
+messages). Note that, it is not the case in the synchronous mode where the
+number of iterations is the same than in the sequential mode. In this way, the
+set of the parameters  of the  platform (number  of nodes,  power of nodes,
+inter and  intra clusters  bandwidth  and  latency \ldots) and  of  the
+application can drastically change the number of iterations required to get the
+convergence. It follows that asynchronous iterative algorithms are difficult to
+optimize since the financial and deployment costs on large scale multi-core
+architecture are often very important. So, prior to delpoyment and tests it
+seems very promising to be able to simulate the behavior of asynchronous
+iterative algorithms. The problematic is then to show that the results produce
+by simulation are in accordance with reality i.e. of the same order of
+magnitude. To our knowledge, there is no study on this problematic.
+

 \section{SimGrid}
  \label{sec:simgrid}
 
 \section{SimGrid}
  \label{sec:simgrid}
 


@@ -529,7 +544,7 @@ and  4x8). We  can  observ  the low  sensitivity  of  the Krylov multisplitting
 in the  grid: in  average, the GMRES  (resp. Multisplitting)  algorithm performs
 $40\%$ better (resp. $48\%$) when running from 2x16=32 to 8x8=64 processors.
 
 in the  grid: in  average, the GMRES  (resp. Multisplitting)  algorithm performs
 $40\%$ better (resp. $48\%$) when running from 2x16=32 to 8x8=64 processors.
 

-\subsubsection{Running on two different inter-clusters network speeds \\} 
+\subsubsection{Running on two different inter-clusters network speeds \\}

 
 \begin{table} [ht!]
 \begin{center}
 
 \begin{table} [ht!]
 \begin{center}


@@ -550,7 +565,7 @@ speed inter-cluster  network (N1) and  also on  a less performant  network (N2).
 Figure~\ref{fig:02} shows that end users will  gain to reduce the execution time
 for  both  algorithms  in using  a  grid  architecture  like  4x16 or  8x8:  the
 performance was increased  by a factor of  $2$. The results depict  also that when
 Figure~\ref{fig:02} shows that end users will  gain to reduce the execution time
 for  both  algorithms  in using  a  grid  architecture  like  4x16 or  8x8:  the
 performance was increased  by a factor of  $2$. The results depict  also that when

-the  network speed  drops down (variation of 12.5\%), the  difference between  the two Multisplitting algorithms execution times can reach more than 25\%. 
+the  network speed  drops down (variation of 12.5\%), the  difference between  the two Multisplitting algorithms execution times can reach more than 25\%.

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