X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/e3eaeb5f6e4963220fa7da4ba4afec74c5727833..7fa502af50f815451a303c3ba53ae8f7a152525c:/hpcc.tex

diff --git a/hpcc.tex b/hpcc.tex
index 81062de..6140ad3 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}
 %Décrire le problème (algo) traité ainsi que le processus d'adaptation à 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}
+
 
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -627,13 +631,12 @@ Note that the program was run with the following parameters:
 
 \begin{itemize}
 \item HOSTFILE: Text file containing the list of the processors units name. Here 100 hosts;
-\item PLATFORM: XML file description of the platform architecture : two clusters (cluster1 and cluster2) with the following characteristics :
+\item PLATFORM: XML file description of the platform architecture whith the following characteristics: %two clusters (cluster1 and cluster2) with the following characteristics :
   \begin{itemize}
-  \item Processor unit power: \np[GFlops]{1.5};
-  \item Intracluster network bandwidth: \np[Gbit/s]{1.25} and latency:
-    \np[$\mu$s]{0.05};
-  \item Intercluster network bandwidth: \np[Mbit/s]{5} and latency:
-    \np[$\mu$s]{5};
+  \item 2 clusters of 50 hosts each;
+  \item Processor unit power: \np[GFlops]{1} or \np[GFlops]{1.5};
+  \item Intra-cluster network bandwidth: \np[Gbit/s]{1.25} and latency: \np[$\mu$s]{0.05};
+  \item Inter-cluster network bandwidth: \np[Mbit/s]{5} or \np[Mbit/s]{50} and latency: \np[$\mu$s]{20};
   \end{itemize}
 \end{itemize}
 
@@ -642,11 +645,11 @@ Note that the program was run with the following parameters:
 
 \begin{itemize}
 \item Description of the cluster architecture matching the format <Number of
-  cluster> <Number of hosts in cluster1> <Number of hosts in cluster2>;
+  clusters> <Number of hosts in cluster1> <Number of hosts in cluster2>;
 \item Maximum number of iterations;
 \item Precisions on the residual error;
 \item Matrix size $N_x$, $N_y$ and $N_z$;
-\item Matrix diagonal value: $6$ (See~(\ref{eq:03}));
+\item Matrix diagonal value: $6$ (See Equation~(\ref{eq:03}));
 \item Matrix off-diagonal value: $-1$;
 \item Communication mode: asynchronous.
 \end{itemize}