X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/2367662d760a73d98ca68d37a3912c211972afe3..992c04fea16bc7e6f2f98c23e4047533dcd273ad:/hpcc.tex?ds=inline

diff --git a/hpcc.tex b/hpcc.tex
index 968b235..d44a7b4 100644
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -322,10 +322,10 @@
 \usepackage[utf8]{inputenc}
 \usepackage{amsfonts,amssymb}
 \usepackage{amsmath}
-%\usepackage{amsmath}
+\usepackage{algorithm}
+\usepackage{algpseudocode}
 %\usepackage{amsthm}
-%\usepackage{amsfonts}
-%\usepackage{graphicx}
+\usepackage{graphicx}
 %\usepackage{xspace}
 \usepackage[american]{babel}
 % Extension pour les graphiques EPS
@@ -335,6 +335,14 @@
 % et l'affichage correct des URL (commande \url{http://example.com})
 %\usepackage{hyperref}
 
+\algnewcommand\algorithmicinput{\textbf{Input:}}
+\algnewcommand\Input{\item[\algorithmicinput]}
+
+\algnewcommand\algorithmicoutput{\textbf{Output:}}
+\algnewcommand\Output{\item[\algorithmicoutput]}
+
+
+
 
 \begin{document}
 %
@@ -346,7 +354,7 @@
 % author names and affiliations
 % use a multiple column layout for up to three different
 % affiliations
-\author{\IEEEauthorblockN{RaphaÃ«l Couturier and Arnaud Giersch and David Laiymani and Charles-Emile Ramamonjisoa}
+\author{\IEEEauthorblockN{RaphaÃ«l Couturier and Arnaud Giersch and David Laiymani and Charles Emile Ramamonjisoa}
 \IEEEauthorblockA{Femto-ST Institute - DISC Department\\
 UniversitÃ© de Franche-ComtÃ©\\
 Belfort\\
@@ -400,8 +408,71 @@ The abstract goes here.
 
 \section{Introduction}
 
-PrÃ©senter un bref Ã©tat de l'art sur la simulation d'algos parallÃ¨les. PrÃ©senter rapidement les algos itÃ©ratifs asynchrones et leurs avantages. Parler de leurs inconvÃ©nients en particulier la difficultÃ© de dÃ©ploiement Ã  grande Ã©chelle donc il serait bien de simuler. Dire qu'Ã  notre connaissance il n'existe pas de simulation de ce type d'algo.
-PrÃ©senter les travaux et les rÃ©sultats 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}
 
@@ -417,7 +488,7 @@ DÃ©crire SimGrid (Arnaud)
 
 
 
-%%%%%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \section{Simulation of the multisplitting method}
 %DÃ©crire le problÃ¨me (algo) traitÃ© ainsi que le processus d'adaptation Ã  SimGrid.
 Let $Ax=b$ be a large sparse system of $n$ linear equations in $\mathbb{R}$, where $A$ is a sparse square and nonsingular matrix, $x$ is the solution vector and $y$ is the right-hand side vector. We use a multisplitting method based on the block Jacobi partitioning to solve this linear system on a large scale platform composed of $L$ clusters of processors. In this case, we apply a row-by-row splitting without overlapping  
@@ -452,7 +523,31 @@ Y_l = B_l - \displaystyle\sum_{i=1,i\neq l}^{L}A_{li}X_i,
 \label{eq:4.1}
 \end{equation}
 is solved independently by a cluster and communication are required to update the right-hand side sub-vectors $Y_l$, such that the sub-vectors $X_i$ represent the data dependencies between the clusters. As each sub-system (\ref{eq:4.1}) is solved in parallel by a cluster of processors, our multisplitting method uses an iterative method as an inner solver which is easier to parallelize and more scalable than a direct method. In this work, we use the parallel GMRES method~\cite{ref1} which is one of the most used iterative method by many researchers. 
-%%%%%
+
+\begin{algorithm}
+\caption{A multisplitting solver with inner iteration GMRES method}
+\begin{algorithmic}[1]
+\Input $A_l$ (local sparse matrix), $B_l$ (local right-hand side), $x^0$ (initial guess)
+\Output $X_l$ (local solution vector)\vspace{0.2cm}
+\State Load $A_l$, $B_l$, $x^0$
+\State Initialize the shared vector $\hat{x}=x^0$
+\For {$k=1,2,3,\ldots$ until the global convergence}
+\State $x^0=\hat{x}$
+\State Inner iteration solver: \Call{InnerSolver}{$x^0$, $k$}
+\State Exchange the local solution ${X}_l^k$ with the neighboring clusters and copy the shared vector elements in $\hat{x}$
+\EndFor
+
+\Statex
+
+\Function {InnerSolver}{$x^0$, $k$}
+\State Compute the local right-hand side: $Y_l = B_l - \sum^L_{i=1,i\neq l}A_{li}X_i^0$
+\State Solving the local splitting $A_{ll}X_l^k=Y_l$ using the parallel GMRES method, such that $X_l^0$ is the local initial guess
+\State \Return $X_l^k$
+\EndFunction
+\end{algorithmic}
+\label{algo:01}
+\end{algorithm}
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 
 
@@ -527,21 +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}
