X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/e3eaeb5f6e4963220fa7da4ba4afec74c5727833..7509f7bc4a76338951fc145f7c10a932fe8144cc:/hpcc.tex?ds=inline

diff --git a/hpcc.tex b/hpcc.tex
index 81062de..98a55ff 100644
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -288,6 +288,8 @@ These two techniques can help to run simulations at a very large scale.
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \section{Simulation of the multisplitting method}
+
+\subsection{The multisplitting method}
 %DÃ©crire le problÃ¨me (algo) traitÃ© ainsi que le processus d'adaptation Ã  SimGrid.
 Let $Ax=b$ be a large sparse system of $n$ linear equations in $\mathbb{R}$, where $A$ is a sparse square and nonsingular matrix, $x$ is the solution vector and $b$ is the right-hand side vector. We use a multisplitting method based on the block Jacobi splitting to solve this linear system on a large scale platform composed of $L$ clusters of processors~\cite{o1985multi}. In this case, we apply a row-by-row splitting without overlapping  
 \begin{equation*}
@@ -442,6 +444,8 @@ The parallel solving of the 3D Poisson problem with our multisplitting method re
 \end{figure}
 
 
+\subsection{Simulation of the multisplitting method using SimGrid and SMPI}
+
 
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
