From: raphael couturier Date: Sun, 27 Apr 2014 14:19:16 +0000 (+0200) Subject: modif intro X-Git-Tag: hpcc2014_submission~57^2~2 X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/commitdiff_plain/d08d0574c6782fe3a320861ccd6ec60a2ab6025e?ds=sidebyside;hp=-c modif intro --- d08d0574c6782fe3a320861ccd6ec60a2ab6025e diff --git a/hpcc.tex b/hpcc.tex index abcf399..29d00a1 100644 --- a/hpcc.tex +++ b/hpcc.tex @@ -82,7 +82,7 @@ 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 +codes. Simulations allow us to see when the multisplitting algorithm can be more efficient than the GMRES one to solve a 3D Poisson problem. @@ -135,35 +135,38 @@ iterative asynchronous algorithms to solve a given problem on a large-scale simulated environment challenges to find optimal configurations giving the best results with a lowest residual error and in the best of execution time. -To our knowledge, there is no existing work on the large-scale simulation of a -real AIAC application. There are {\bf two contributions} in this paper. First we give a first -approach of the simulation of AIAC algorithms using a simulation tool (i.e. the -SimGrid toolkit~\cite{SimGrid}). Second, we confirm the effectiveness of the -asynchronous multisplitting algorithm by comparing its performance with the synchronous -GMRES. More precisely, we had implemented a program for solving large -linear system of equations by numerical method GMRES (Generalized -Minimal Residual) \cite{ref1}. We show, that with minor modifications of the -initial MPI code, the SimGrid toolkit allows us to perform a test campaign of a -real AIAC application on different computing architectures. The simulated -results we obtained are in line with real results exposed in ??\AG[]{ref?}. -SimGrid had allowed us to launch the application from a modest computing -infrastructure by simulating different distributed architectures composed by -clusters nodes interconnected by variable speed networks. With selected -parameters on the network platforms (bandwidth, latency of inter cluster -network) and on the clusters architecture (number, capacity calculation power) -in the simulated environment, the experimental results have demonstrated not -only the algorithm convergence within a reasonable time compared with the -physical environment performance, but also a time saving of up to \np[\%]{40} in -asynchronous mode. -\AG{Il faudrait revoir la phrase précédente (couper en deux?). Là, on peut - avoir l'impression que le gain de \np[\%]{40} est entre une exécution réelle - et une exécution simulée!} - -This article is structured as follows: after this introduction, the next section will give a brief description of -iterative asynchronous model. Then, the simulation framework SimGrid is presented with the settings to create various -distributed architectures. The algorithm of the multisplitting method used by GMRES \LZK{??? GMRES n'utilise pas la méthode de multisplitting! Sinon ne doit on pas expliquer le choix d'une méthode de multisplitting?} written with MPI primitives and -its adaptation to SimGrid with SMPI (Simulated MPI) is detailed in the next section. At last, the experiments results -carried out will be presented before some concluding remarks and future works. +To our knowledge, there is no existing work on the large-scale simulation of a +real AIAC application. {\bf The contribution of the present paper can be + summarised in two main points}. First we give a first approach of the +simulation of AIAC algorithms using a simulation tool (i.e. the SimGrid +toolkit~\cite{SimGrid}). Second, we confirm the effectiveness of the +asynchronous multisplitting algorithm by comparing its performance with the +synchronous GMRES (Generalized Minimal Residual) \cite{ref1}. Both these codes +can be used to solve large linear systems. In this paper, we focus on a 3D +Poisson problem. We show, that with minor modifications of the initial MPI +code, the SimGrid toolkit allows us to perform a test campaign of a real AIAC +application on different computing architectures. +% The simulated results we +%obtained are in line with real results exposed in ??\AG[]{ref?}. +SimGrid had allowed us to launch the application from a modest computing +infrastructure by simulating different distributed architectures composed by +clusters nodes interconnected by variable speed networks. Parameters of the +network platforms are the bandwidth and the latency of inter cluster +network. Parameters on the cluster's architecture are the number of machines and +the computation power of a machine. Simulations show that the asynchronous +multisplitting algorithm can solve the 3D Poisson problem approximately twice +faster than GMRES with two distant clusters. + + + +This article is structured as follows: after this introduction, the next section +will give a brief description of iterative asynchronous model. Then, the +simulation framework SimGrid is presented with the settings to create various +distributed architectures. Then, the multisplitting method is presented, it is +based on GMRES to solve each block obtained of the splitting. This code is +written with MPI primitives and its adaptation to SimGrid with SMPI (Simulated +MPI) is detailed in the next section. At last, the simulation results carried +out will be presented before some concluding remarks and future works. \section{Motivations and scientific context}