From: lilia <lilia@amazigh.bordeaux.inria.fr>
Date: Thu, 21 Aug 2014 11:23:24 +0000 (+0200)
Subject: v1-21-08-2014
X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/GMRES2stage.git/commitdiff_plain/ccfba25388fe899463c9ec52777cfc6e9ad6ef36

v1-21-08-2014
---

diff --git a/paper.tex b/paper.tex
index f7590c0..fb32347 100644
--- a/paper.tex
+++ b/paper.tex
@@ -542,7 +542,11 @@ Iterative methods are become more attractive than direct ones to solve large spa
 
 The most successful iterative methods currently available are those based on Krylov subspaces which consist in forming a basis of a sequence of successive matrix powers times an initial vector for example the residual. These methods are based on orthogonality of vectors of the Krylov subspace basis to solve linear systems. The most well-known iterative Krylov subspace methods are Conjugate Gradient method and GMRES method (generalized minimal residual).
 
-However, the iterative methods suffer from scalability problems on parallel computing platforms with many processors due to their need for reduction operations and collective communications to perform matrix-vector multiplications. The communications on large clusters with thousands of cores and large sizes of messages can significantly affect the performances of iterative methods. In practice, Krylov subspace iteration methods are often used with preconditioners in order to increase their convergence and accelerate their performances. However, most of the good preconditioners are not scalable on large clusters.    
+However, the iterative methods suffer from scalability problems on parallel computing platforms with many processors due to their need for reduction operations and collective communications to perform matrix-vector multiplications. The communications on large clusters with thousands of cores and large sizes of messages can significantly affect the performances of iterative methods. In practice, Krylov subspace iteration methods are often used with preconditioners in order to increase their convergence and accelerate their performances. However, most of the good preconditioners are not scalable on large clusters. 
+
+In this paper we propose a two-stage algorithm, also called inner-outer iteration algorithm, based on two nested iterations.  
+
+This paper is organized as follows. In Section~\ref{sec:02} some related works are presented. Section~\ref{sec:03} presents our two-stage algorithm based on Krylov subspace iteration methods. Section~\ref{sec:04} shows some experimental results obtained on large clusters using routines of PETSC toolkit.    
 
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@@ -552,6 +556,7 @@ However, the iterative methods suffer from scalability problems on parallel comp
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 \section{Related works}
+\label{sec:02} 
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@@ -561,6 +566,7 @@ However, the iterative methods suffer from scalability problems on parallel comp
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 \section{A Krylov two-stage algorithm}
+\label{sec:03}
 We propose a two-stage algorithm to solve large sparse linear systems of the form $Ax=b$, where $A\in\mathbb{R}^{n\times n}$ is a sparse and square nonsingular matrix, $x\in\mathbb{R}^n$ is the solution vector and $b\in\mathbb{R}^n$ is the right-hand side. The algorithm is implemented as an inner-outer iteration solver based on iterative Krylov methods. The main key points of our solver are given in Algorithm~\ref{algo:01}. 
 
 In order to accelerate the convergence, the outer iteration is implemented as an iterative Krylov method which minimizes some error function over a Krylov subspace~\cite{saad96}. At every iteration, the sparse linear system $Ax=b$ is solved iteratively with an iterative method as GMRES method~\cite{saad86} and the Krylov subspace that we used is spanned by a basis $S$ composed of successive solutions issued from the inner iteration
@@ -605,6 +611,8 @@ such that $R=AS$ is a dense rectangular matrix in $\mathbb{R}^{n\times s}$, $s\l
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 \section{Experiments using petsc}
+\label{sec:04}
+
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@@ -613,6 +621,7 @@ such that $R=AS$ is a dense rectangular matrix in $\mathbb{R}^{n\times s}$, $s\l
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 \section{Conclusion}
+\label{sec:05}
 %The conclusion goes here. this is more of the conclusion
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