X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/Krylov_multi.git/blobdiff_plain/37ae33a711e93b7d43faf6a64064a0576e7f90b5..70c44690f5342df3f1f809bf2bf5565a1e50f6ff:/krylov_multi.tex?ds=sidebyside

diff --git a/krylov_multi.tex b/krylov_multi.tex
index c3f9e1c..0fd3b79 100644
--- a/krylov_multi.tex
+++ b/krylov_multi.tex
@@ -1,7 +1,72 @@
 \documentclass{article}
+\usepackage[utf8]{inputenc}
+\usepackage{amsfonts,amssymb}
+\usepackage{amsmath}
+\usepackage{graphicx}
+
+\title{A scalable multisplitting algorithm for solving large sparse linear systems} 
+
+
 
 \begin{document}
+\author{Raphaël Couturier \and Lilia Ziane Khodja}
+
+\maketitle
+
+
+\begin{abstract}
+In  this  paper we  revist  the  krylov  multisplitting algorithm  presented  in
+\cite{huang1993krylov}  which  uses  a  scalar  method to  minimize  the  krylov
+iterations computed by a multisplitting algorithm. Our new algorithm is based on
+a  parallel multisplitting  algorithm  with few  blocks  of large  size using  a
+parallel GMRES method inside each block and on a parallel krylov minimization in
+order to improve the convergence. Some large scale experiments with a 3D Poisson
+problem  are presented.   They  show  the obtained  improvements  compared to  a
+classical GMRES both in terms of number of iterations and execution times.
+\end{abstract}
+
+\section{Introduction}
+
+Iterative methods are used to solve  large sparse linear systems of equations of
+the form  $Ax=b$ because they are  easier to parallelize than  direct ones. Many
+iterative  methods have  been proposed  and  adapted by  many researchers.  When
+solving large  linear systems  with many cores,  iterative methods  often suffer
+from  scalability  problems.    This  is  due  to  their   need  for  collective
+communications  to  perform  matrix-vector  products and  reduction  operations.
+Preconditionners can be  used in order to increase  the convergence of iterative
+solvers.   However, most  of the  good preconditionners  are not  sclalable when
+thousands of cores are used.
+
+
+A completer...
+On ne peut pas parler de tout...
+
+\section{Related works}
+
+
+A general framework  for studying parallel multisplitting has  been presented in
+\cite{o1985multi} by O'Leary and White. Convergence conditions are given for the
+most general case.  Many authors improved multisplitting algorithms by proposing
+for  example  a  asynchronous  version  \cite{bru1995parallel}  and  convergence
+condition  \cite{bai1999block,bahi2000asynchronous}   in  this  case   or  other
+two-stage algorithms~\cite{frommer1992h,bru1995parallel}
+
+In  \cite{huang1993krylov},  the  authors  proposed  a  parallel  multisplitting
+algorithm in which all the tasks except  one are devoted to solve a sub-block of
+the splitting  and to send their  local solution to  the first task which  is in
+charge to  combine the vectors at  each iteration.  These vectors  form a Krylov
+basis for  which the first tasks minimize  the error function over  the basis to
+increase the convergence, then the other tasks receive the update solution until
+convergence of the global system. 
+
+
+
+In \cite{couturier2008gremlins}, the  authors proposed practical implementations
+of multisplitting algorithms that take benefit from multisplitting algorithms to
+solve large scale linear systems. Inner  solvers could be based on scalar direct
+method with the LU method or scalar iterative one with GMRES.
 
-This paper presents ....
+\bibliographystyle{plain}
+\bibliography{biblio}
 
 \end{document}