renomme un fichier figure

[Krylov_multi.git] / krylov_multi_reviewed.tex

diff --git a/krylov_multi_reviewed.tex b/krylov_multi_reviewed.tex
index 02d1acaaa59d40a61b4227935bf1475445990db9..79345018f4a9c66e356086ab5e35794a212b5d93 100644 (file)
--- a/krylov_multi_reviewed.tex
+++ b/krylov_multi_reviewed.tex
@@ -84,16 +84,16 @@ thousands of cores are used.
 
 Traditional parallel iterative solvers are based on fine-grain computations that
 frequently  require  data exchanges  between  computing  nodes  and have  global
 
 Traditional parallel iterative solvers are based on fine-grain computations that
 frequently  require  data exchanges  between  computing  nodes  and have  global

-synchronizations  that penalize  the  scalability. Particularly,  they are  more
-penalized on large  scale architectures or on distributed  platforms composed of
-distant  clusters interconnected  by  a high-latency  network.  It is  therefore
-imperative to develop coarse-grain based algorithms to reduce the communications
-in the  parallel iterative  solvers. Two possible  solutions consists  either in
-using  asynchronous  iterative  methods~\cite{ref18}  or in  using  multisplitting
-algorithms.  In this  paper,  we will  reconsider  the use  of a  multisplitting
-method. In opposition to traditional multisplitting method that suffer from slow
-convergence, as  proposed in~\cite{huang1993krylov},  the use of  a minimization
-process can drastically improve the convergence.\\
+synchronizations that penalize the scalability~\cite{zkcgb+14:ij}. Particularly,
+they are more penalized on large scale architectures or on distributed platforms
+composed of  distant clusters interconnected  by a high-latency network.   It is
+therefore  imperative to  develop coarse-grain  based algorithms  to  reduce the
+communications  in  the  parallel  iterative  solvers.  Two  possible  solutions
+consists either in using asynchronous iterative methods~\cite{ref18} or in using
+multisplitting  algorithms.  In  this paper,  we will  reconsider the  use  of a
+multisplitting method.  In opposition to traditional  multisplitting method that
+suffer from slow convergence,  as proposed in~\cite{huang1993krylov}, the use of
+a minimization process can drastically improve the convergence.\\

 
 
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@@ -380,7 +380,7 @@ respectively. The size of the Krylov subspace basis $S$ is fixed to 10 vectors.
 \begin{figure}[htbp]
 \centering
 \begin{tabular}{c}
 \begin{figure}[htbp]
 \centering
 \begin{tabular}{c}

-\includegraphics[width=0.8\textwidth]{weak_scaling_280k} \\ \includegraphics[width=0.8\textwidth]{weak_scaling_280K}\\
+\includegraphics[width=0.8\textwidth]{weak_scaling_280k} \\ \includegraphics[width=0.8\textwidth]{weak_scaling_280K2}\\

 \end{tabular}
 \caption{Weak scaling with 3 blocks of 4 cores each to solve a 3D Poisson problem with approximately 280K components per core}
 \label{fig:002}
 \end{tabular}
 \caption{Weak scaling with 3 blocks of 4 cores each to solve a 3D Poisson problem with approximately 280K components per core}
 \label{fig:002}


@@ -476,7 +476,7 @@ to 115. So it is not different from GMRES.
 \begin{figure}[htbp]
 \centering
   \includegraphics[width=0.7\textwidth]{nb_iter_sec}
 \begin{figure}[htbp]
 \centering
   \includegraphics[width=0.7\textwidth]{nb_iter_sec}

-\caption{Number of iterations per second  with the same parameters as in Table~\ref{tab1} (weak scaling) with only 2 blocks of cores}
+\caption{Number of iterations per second  with the same parameters as in Table~\ref{tab1} (weak scaling) with only  blocks of cores}

 \label{fig:01}
 \end{figure}
 
 \label{fig:01}
 \end{figure}