24-04-2014b

diff --git a/hpcc.tex b/hpcc.tex
index d9fe798d69f5dee7fe53a5117059b4592d9150fe..29eca38179aabe085bd84945c74473f1a1c1a17c 100644 (file)
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -405,7 +405,7 @@ u^{k+1}(x,y,z)= & u^k(x,y,z) - \frac{1}{6}\times\\
 \end{equation} 
 where the iteration matrix $A$ of size $N_x\times N_y\times N_z$ of the discretized linear system is sparse, symmetric and positive definite. 
 
 \end{equation} 
 where the iteration matrix $A$ of size $N_x\times N_y\times N_z$ of the discretized linear system is sparse, symmetric and positive definite. 
 

-The parallel solving of the 3D Poisson problem with our multisplitting method requires a data partitioning of the problem between clusters and between processors within a cluster. We have choose the 3D partitioning instead of the row-by-row partitioning in order to reduce the data exchanges at sub-domain boundaries. Figure~\ref{fig:4.2} shows an example of the data partitioning of the 3D Poisson problem between two clusters of processors, where each sub-problem is assigned to a processor. In this context, a processor has at most six neighbors within a cluster of in distant clusters with which it shares data at sub-domain boundaries. 
+The parallel solving of the 3D Poisson problem with our multisplitting method requires a data partitioning of the problem between clusters and between processors within a cluster. We have chosen the 3D partitioning instead of the row-by-row partitioning in order to reduce the data exchanges at sub-domain boundaries. Figure~\ref{fig:4.2} shows an example of the data partitioning of the 3D Poisson problem between two clusters of processors, where each sub-problem is assigned to a processor. In this context, a processor has at most six neighbors within a cluster of in distant clusters with which it shares data at sub-domain boundaries. 

 
 \begin{figure}[!t]
 \centering
 
 \begin{figure}[!t]
 \centering