fixed. So one solution consists in using simulations first in order to analyze
what parameters could influence or not the behaviors of an algorithm. In this
paper, we show that it is interesting to use SimGrid to simulate the behaviors
-of asynchronous iterative algorithms. For that, we compare the behaviour of a
+of asynchronous iterative algorithms. For that, we compare the behavior of a
synchronous GMRES algorithm with an asynchronous multisplitting one with
simulations which let us easily choose some parameters. Both codes are real MPI
codes and simulations allow us to see when the asynchronous multisplitting algorithm can be more
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, etc.) and of the
+nodes, inter and intra clusters bandwidth 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
-asynchronous iterative algoritms before being able to runs real experiments.
+asynchronous iterative algorithms before being able to run real experiments.
real execution of MPI applications on the one hand, and their simulation with
SMPI on the other hand, are presented in~\cite{guermouche+renard.2010.first,
clauss+stillwell+genaud+al.2011.single,
- bedaride+degomme+genaud+al.2013.toward}.
+ bedaride+degomme+genaud+al.2013.toward}. All these works conclude that
+SimGrid is able to simulate pretty accurately the real behavior of the
+applications.
+
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Simulation of the multisplitting method}
\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 whith the following characteristics: %two clusters (cluster1 and cluster2) with the following characteristics :
+\item PLATFORM: XML file description of the platform architecture with 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} or \np[GFlops]{1.5};
% LocalWords: Ouest Vieille Talence cedex scalability experimentations HPC MPI
% LocalWords: Parallelization AIAC GMRES multi SMPI SISC SIAC SimDAG DAGs Lua
% LocalWords: Fortran GFlops priori Mbit de du fcomte multisplitting scalable
-% LocalWords: SimGrid Belfort parallelize Labex ANR LABX IEEEabrv hpccBib
+% LocalWords: SimGrid Belfort parallelize Labex ANR LABX IEEEabrv hpccBib Gbit
% LocalWords: intra durations nonsingular Waitall discretization discretized
-% LocalWords: InnerSolver Isend Irecv
+% LocalWords: InnerSolver Isend Irecv parallelization
