X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/e3eaeb5f6e4963220fa7da4ba4afec74c5727833..15be2239bf8d8970cea0817752bbee94c1fbeccd:/hpcc.tex diff --git a/hpcc.tex b/hpcc.tex index 81062de..e0db0c2 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} + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @@ -627,8 +631,9 @@ 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 2 clusters of 50 hosts each; \item Processor unit power: \np[GFlops]{1.5}; \item Intracluster network bandwidth: \np[Gbit/s]{1.25} and latency: \np[$\mu$s]{0.05}; @@ -642,11 +647,11 @@ Note that the program was run with the following parameters: \begin{itemize} \item Description of the cluster architecture matching the format ; + clusters> ; \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}