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 \begin{abstract}
 In  this  paper we  revist  the  krylov  multisplitting algorithm  presented  in
 \cite{huang1993krylov}  which  uses  a  scalar  method to  minimize  the  krylov
 \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 simply a
-parallel multisplitting algorithm  with few blocks of large  size and a parallel
-krylov  minimization  is used  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.
+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
 \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 adpated  by many  researchers.  When
+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
 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.
+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.

 
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