X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/7845b6502c9948b08bc33b38b0ac5293125e565e..108b6ffda00e3d8bf697f0f6ee88c0c6e9d53e3f:/hpcc.tex diff --git a/hpcc.tex b/hpcc.tex index b2509c1..6a39362 100644 --- a/hpcc.tex +++ b/hpcc.tex @@ -232,9 +232,9 @@ asynchronous iterative algorithms comes from the fact it is necessary to run the with real data. In fact, from an execution to another the order of messages will change and the number of iterations to reach the convergence will also change. According to all the parameters of the platform (number of nodes, power of -nodes, inter and intra clusrters bandwith and latency, ....) and of the -algorithm (number of splitting with the multisplitting algorithm), the -multisplitting code will obtain the solution more or less quickly. Or course, +nodes, inter and intra clusrters bandwith and latency, etc.) and of the +algorithm (number of splittings with the multisplitting algorithm), the +multisplitting code will obtain the solution more or less quickly. Of course, the GMRES method also depends of the same parameters. As it is difficult to have access to many clusters, grids or supercomputers with many different network parameters, it is interesting to be able to simulate the behaviors of @@ -251,8 +251,8 @@ SimGrid~\cite{SimGrid,casanova+legrand+quinson.2008.simgrid} is a simulation framework to study the behavior of large-scale distributed systems. As its name says, it emanates from the grid computing community, but is nowadays used to study grids, clouds, HPC or peer-to-peer systems. The early versions of SimGrid -date from 1999, but it's still actively developed and distributed as an open -source software. Today, it's one of the major generic tools in the field of +date from 1999, but it is still actively developed and distributed as an open +source software. Today, it is one of the major generic tools in the field of simulation for large-scale distributed systems. SimGrid provides several programming interfaces: MSG to simulate Concurrent @@ -383,8 +383,8 @@ exchanged by message passing using MPI non-blocking communication routines. \begin{figure}[!t] \centering - \includegraphics[width=60mm,keepaspectratio]{clustering2} -\caption{Example of two distant clusters of processors.} + \includegraphics[width=60mm,keepaspectratio]{clustering} +\caption{Example of three distant clusters of processors.} \label{fig:4.1} \end{figure} @@ -422,7 +422,7 @@ u =0 \text{~on~} \Gamma =\partial\Omega \right. \label{eq:02} \end{equation} -where $\nabla^2$ is the Laplace operator, $f$ and $u$ are real-valued functions, and $\Omega=[0,1]^3$. The spatial discretization with a finite difference scheme reduces problem~(\ref{eq:02}) to a system of sparse linear equations. Our multisplitting method solves the 3D Poisson problem using a seven point stencil whose the general expression could be written as +where $\nabla^2$ is the Laplace operator, $f$ and $u$ are real-valued functions, and $\Omega=[0,1]^3$. The spatial discretization with a finite differences scheme reduces problem~(\ref{eq:02}) to a system of sparse linear equations. Our multisplitting method solves the 3D Poisson problem using a seven point stencil whose the general expression could be written as \begin{equation} \begin{array}{l} u(x-1,y,z) + u(x,y-1,z) + u(x,y,z-1)\\+u(x+1,y,z)+u(x,y+1,z)+u(x,y,z+1) \\ -6u(x,y,z)=h^2f(x,y,z),