From 992c04fea16bc7e6f2f98c23e4047533dcd273ad Mon Sep 17 00:00:00 2001 From: RCE Date: Fri, 11 Apr 2014 02:15:05 +0200 Subject: [PATCH 1/1] INTRODUCTION 1) Ecriture de l'intro 2) Ajout des tableaux 1, 2 et 3 au format jpg --- hpcc.tex | 82 +++++++++++++++++++++++++++++++++++++++++++++++++------- 1 file changed, 73 insertions(+), 9 deletions(-) diff --git a/hpcc.tex b/hpcc.tex index fc79391..d44a7b4 100644 --- a/hpcc.tex +++ b/hpcc.tex @@ -325,7 +325,7 @@ \usepackage{algorithm} \usepackage{algpseudocode} %\usepackage{amsthm} -%\usepackage{graphicx} +\usepackage{graphicx} %\usepackage{xspace} \usepackage[american]{babel} % Extension pour les graphiques EPS @@ -354,7 +354,7 @@ % author names and affiliations % use a multiple column layout for up to three different % affiliations -\author{\IEEEauthorblockN{Raphaël Couturier and Arnaud Giersch and David Laiymani and Charles-Emile Ramamonjisoa} +\author{\IEEEauthorblockN{Raphaël Couturier and Arnaud Giersch and David Laiymani and Charles Emile Ramamonjisoa} \IEEEauthorblockA{Femto-ST Institute - DISC Department\\ Université de Franche-Comté\\ Belfort\\ @@ -408,8 +408,71 @@ The abstract goes here. \section{Introduction} -Présenter un bref état de l'art sur la simulation d'algos parallèles. Présenter rapidement les algos itératifs asynchrones et leurs avantages. Parler de leurs inconvénients en particulier la difficulté de déploiement à grande échelle donc il serait bien de simuler. Dire qu'à notre connaissance il n'existe pas de simulation de ce type d'algo. -Présenter les travaux et les résultats obtenus. Annoncer le plan. +Parallel computing and high performance computing (HPC) are becoming +more and more imperative for solving various problems raised by +researchers on various scientific disciplines but also by industrial in +the field. Indeed, the increasing complexity of these requested +applications combined with a continuous increase of their sizes lead to +write distributed and parallel algorithms requiring significant hardware +resources ( grid computing , clusters, broadband network ,etc... ) but +also a non- negligible CPU execution time. We consider in this paper a +class of highly efficient parallel algorithms called iterative executed +in a distributed environment. As their name suggests, these algorithm +solves a given problem that might be NP- complete complex by successive +iterations (X$_{n +1 }$= f (X$_{n}$) ) from an initial value X +$_{0}$ to find an approximate value X* of the solution with a very low +residual error. Several well-known methods demonstrate the convergence +of these algorithms. Generally, to reduce the complexity and the +execution time, the problem is divided into several "pieces" that will +be solved in parallel on multiple processing units. The latter will +communicate each intermediate results before a new iteration starts +until the approximate solution is reached. These distributed parallel +computations can be performed either in "synchronous" communication mode +where a new iteration begin only when all nodes communications are +completed, either "asynchronous" mode where processors can continue +independently without or few synchronization points. Despite the +effectiveness of iterative approach, a major drawback of the method is +the requirement of huge resources in terms of computing capacity, +storage and high speed communication network. Indeed, limited physical +resources are blocking factors for large-scale deployment of parallel +algorithms. + +In recent years, the use of a simulation environment to execute parallel +iterative algorithms found some interests in reducing the highly cost of +access to computing resources: (1) for the applications development life +cycle and in code debugging (2) and in production to get results in a +reasonable execution time with a simulated infrastructure not accessible +with physical resources. Indeed, the launch of distributed 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. According our knowledge, no testing of large-scale +simulation of the class of algorithm solving to achieve real results has +been undertaken to date. We had in the scope of this work implemented a +program for solving large non-symmetric linear system of equations by +numerical method GMRES (Generalized Minimal Residual ) in the simulation +environment Simgrid . The simulated platform 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. In addition, it has been +permitted to show the effectiveness of asynchronous mode algorithm by +comparing its performance with the synchronous mode time. 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 40 \% in asynchronous mode. + +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 will be presented with the +settings to create various distributed architectures. The algorithm of +the multi -splitting method used by GMRES written with MPI primitives +and its adaptation to Simgrid with SMPI (Simulation MPI ) will be in the +next section . At last, the experiments results carried out will be +presented before the conclusion which we will announce the opening of +our future work after the results. \section{The asynchronous iteration model} @@ -559,21 +622,22 @@ lat latency , ... ). \centering \caption{2 clusters X 50 nodes} \label{tab.cluster.2x50} - \includegraphics[width=209pt]{img-1.eps} + \includegraphics[width=209pt]{img1.jpg} \end{table} \begin{table} \centering - \caption{3 clusters X 33 n\oe{}uds} + \caption{3 clusters X 33 nodes} \label{tab.cluster.3x33} - \includegraphics[width=209pt]{img-1.eps} + \includegraphics[width=209pt]{img2.jpg} \end{table} \begin{table} \centering - \caption{3 clusters X 67 noeuds} + \caption{3 clusters X 67 nodes} \label{tab.cluster.3x67} - \includegraphics[width=128pt]{img-2.eps} +% \includegraphics[width=160pt]{img3.jpg} + \includegraphics[scale=0.5]{img3.jpg} \end{table} \paragraph*{Interpretations and comments} -- 2.39.5