\author{%
\IEEEauthorblockN{%
Charles Emile Ramamonjisoa\IEEEauthorrefmark{1},
+ Lilia Ziane Khodja\IEEEauthorrefmark{2},
David Laiymani\IEEEauthorrefmark{1},
- Arnaud Giersch\IEEEauthorrefmark{1},
- Lilia Ziane Khodja\IEEEauthorrefmark{2} and
+ Arnaud Giersch\IEEEauthorrefmark{1} and
Raphaël Couturier\IEEEauthorrefmark{1}
}
\IEEEauthorblockA{\IEEEauthorrefmark{1}%
\maketitle
-\RC{Ordre des auteurs pas définitif.}
\begin{abstract}
-\AG{L'abstract est AMHA incompréhensible et ne donne pas envie de lire la suite.}
-In recent years, the scalability of large-scale implementation in a
-distributed environment of algorithms becoming more and more complex has
-always been hampered by the limits of physical computing resources
-capacity. One solution is to run the program in a virtual environment
-simulating a real interconnected computers architecture. The results are
-convincing and useful solutions are obtained with far fewer resources
-than in a real platform. However, challenges remain for the convergence
-and efficiency of a class of algorithms that concern us here, namely
-numerical parallel iterative algorithms executed in asynchronous mode,
-especially in a large scale level. Actually, such algorithm requires a
-balance and a compromise between computation and communication time
-during the execution. Two important factors determine the success of the
-experimentation: the convergence of the iterative algorithm on a large
-scale and the execution time reduction in asynchronous mode. Once again,
-from the current work, a simulated environment like SimGrid provides
-accurate results which are difficult or even impossible to obtain in a
-physical platform by exploiting the flexibility of the simulator on the
-computing units clusters and the network structure design. Our
-experimental outputs showed a saving of up to \np[\%]{40} for the algorithm
-execution time in asynchronous mode compared to the synchronous one with
-a residual precision up to \np{E-11}. Such successful results open
-perspectives on experimentations for running the algorithm on a
-simulated large scale growing environment and with larger problem size.
-
-\LZK{Long\ldots}
+
+Synchronous iterative algorithms is often less scalable than asynchronous
+iterative ones. Performing large scale experiments with different kind of
+networks parameters is not easy because with supercomputers such parameters are
+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
+synchronous GMRES algorithm with an asynchronous multisplitting one with
+simulations in which we choose some parameters. Both codes are real MPI
+codes. Experiments allow us to see when the multisplitting algorithm can be more
+efficience than the GMRES one to solve a 3D Poisson problem.
+
% no keywords for IEEE conferences
% Keywords: Algorithm distributed iterative asynchronous simulation SimGrid
