v2

[Krylov_multi.git] / krylov_multi.tex

diff --git a/krylov_multi.tex b/krylov_multi.tex
index e51e232207ed9c08fd320d4e94d90f4c5ab2c917..87ee2279999d06ae61bfa857b6c2ae4aab60c1bd 100644 (file)
--- a/krylov_multi.tex
+++ b/krylov_multi.tex
@@ -17,10 +17,10 @@
 \newcommand{\Prec}{\mathit{prec}}
 \newcommand{\Ratio}{\mathit{Ratio}}
 
 \newcommand{\Prec}{\mathit{prec}}
 \newcommand{\Ratio}{\mathit{Ratio}}
 

-\usepackage{xspace}
-\usepackage[textsize=footnotesize]{todonotes}
-\newcommand{\LZK}[2][inline]{%
-\todo[color=green!40,#1]{\sffamily\textbf{LZK:} #2}\xspace}
+%\usepackage{xspace}
+%\usepackage[textsize=footnotesize]{todonotes}
+%\newcommand{\LZK}[2][inline]{%
+%\todo[color=green!40,#1]{\sffamily\textbf{LZK:} #2}\xspace}

 
 \title{A scalable multisplitting algorithm for solving large sparse linear systems} 
 \date{}
 
 \title{A scalable multisplitting algorithm for solving large sparse linear systems} 
 \date{}


@@ -37,14 +37,15 @@
 %%%%%%%%%%%%%%%%%%%%%%%%
 
 \begin{abstract}
 %%%%%%%%%%%%%%%%%%%%%%%%
 
 \begin{abstract}

-In  this paper  we  revisit  the krylov  multisplitting  algorithm presented  in
-\cite{huang1993krylov}  which  uses  a  scalar  method to  minimize  the  krylov
+In  this paper  we  revisit  the Krylov  multisplitting  algorithm presented  in
+\cite{huang1993krylov}  which  uses  a  sequential  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
 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
+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
 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.
+problem  are  presented  with  up   to  8,192  cores.   They  show  the  obtained
+improvements compared to a classical GMRES both in terms of number of iterations
+and execution times.

 \end{abstract}
 
 %%%%%%%%%%%%%%%%%%%%%%%%
 \end{abstract}
 
 %%%%%%%%%%%%%%%%%%%%%%%%


@@ -55,31 +56,35 @@ 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.   For
 example, the GMRES method and the  Conjugate Gradient method are very well known
 the form  $Ax=b$ because they are  easier to parallelize than  direct ones. Many
 iterative  methods have  been proposed  and  adapted by  many researchers.   For
 example, the GMRES method and the  Conjugate Gradient method are very well known

-and  used by  many researchers~\cite{S96}. Both  the method  are based  on the
-Krylov subspace which consists in forming  a basis of the sequence of successive
+and  used by  many researchers~\cite{S96}. Both methods  are based  on the
+Krylov subspace which consists in forming  a basis of a sequence of successive

 matrix powers times the initial residual.
 
 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.
 matrix powers times the initial residual.
 
 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
+Preconditioners can be  used in order to increase  the convergence of iterative
+solvers.   However, most  of the  good preconditioners  are not  scalable when

 thousands of cores are used.
 
 thousands of cores are used.
 

-Traditional iterative  solvers have  global synchronizations that  penalize the
-scalability.   Two  possible solutions  consists  either  in using  asynchronous
-iterative  methods~\cite{ref18} or  to  use multisplitting  algorithms. In  this
-paper, we will  reconsider the use of a multisplitting  method. In opposition to
-traditionnal  multisplitting  method  that  suffer  from  slow  convergence,  as
-proposed  in~\cite{huang1993krylov},  the  use  of a  minimization  process  can
-drastically improve the convergence.
+%Traditional iterative  solvers have  global synchronizations that  penalize the
+%scalability.   Two  possible solutions  consists  either  in using  asynchronous
+%iterative  methods~\cite{ref18} or  to  use multisplitting  algorithms. In  this
+%paper, we will  reconsider the use of a multisplitting  method. In opposition to
+%traditional  multisplitting  method  that  suffer  from  slow  convergence,  as
+%proposed  in~\cite{huang1993krylov},  the  use  of a  minimization  process  can
+%drastically improve the convergence.
+
+Traditional parallel iterative solvers are based on fine-grain computations that frequently require data exchanges between computing nodes and have global synchronizations that penalize the scalability. Particularly, they are more penalized on large scale architectures or on distributed platforms composed of distant clusters interconnected by a high-latency network. It is therefore imperative to develop coarse-grain based algorithms to reduce the communications in the parallel iterative solvers. Two  possible solutions consists either in using asynchronous iterative methods~\cite{ref18} or to use multisplitting algorithms. In this paper, we will reconsider the use of a multisplitting method. In opposition to traditional multisplitting method that suffer from slow convergence, as proposed in~\cite{huang1993krylov}, the use of a minimization process can drastically improve the convergence.
+
+The present paper is organized as follows. First in Section~\ref{sec:02} is given some related works and the main principle of multisplitting methods. Then, in Section~\ref{sec:03} is presented the algorithm of our Krylov multisplitting method based on inner-outer iterations. Finally, in Section~\ref{sec:04}, the parallel experiments on Hector architecture show the performances of the Krylov multisplitting algorithm compared to the classical GMRES algorithm to solve a 3D Poisson problem.

 
 

-\LZK[]{Suite\dots}

 
 %%%%%%%%%%%%%%%%%%%%%%%%
 %%%%%%%%%%%%%%%%%%%%%%%%
 
 
 %%%%%%%%%%%%%%%%%%%%%%%%
 %%%%%%%%%%%%%%%%%%%%%%%%
 

-\section{Related works}
+\section{Related works and presentation of the multisplitting method}
+\label{sec:02}

 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
 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


@@ -89,59 +94,61 @@ 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
 
 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
+the splitting  and to send their  local solutions 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 task minimizes  the error function over  the basis to
 increase the convergence, then the other tasks receive the updated solution until
 convergence of the global system. 
 
 In~\cite{couturier2008gremlins}, the  authors proposed practical implementations
 charge to  combine the vectors at  each iteration.  These vectors  form a Krylov
 basis for  which the first task minimizes  the error function over  the basis to
 increase the convergence, then the other tasks receive the updated solution until
 convergence of the global system. 
 
 In~\cite{couturier2008gremlins}, the  authors proposed practical implementations

-of multisplitting algorithms that take benefit from multisplitting algorithms\LZK[]{répétition ???} 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.\LZK[]{lu et gmres par exemple}
+of multisplitting algorithms to solve  large scale linear systems. Inner solvers
+could be  based on sequential direct method  with the LU method  or sequential iterative
+one with GMRES.

 
 In~\cite{prace-multi},  the  authors have  proposed a  parallel  multisplitting
 algorithm in which large blocks are solved using a GMRES solver. The authors have
 performed large scale experiments up-to  32,768 cores and they conclude that
 asynchronous  multisplitting algorithm  could be more  efficient  than traditional
 
 In~\cite{prace-multi},  the  authors have  proposed a  parallel  multisplitting
 algorithm in which large blocks are solved using a GMRES solver. The authors have
 performed large scale experiments up-to  32,768 cores and they conclude that
 asynchronous  multisplitting algorithm  could be more  efficient  than traditional

-solvers on exascale architecture with hundreds of thousands of cores.
+solvers on an exascale architecture with hundreds of thousands of cores.

 
 

-\LZK[]{Peut-être autres related works\ldots}\\
+So compared to these works, we propose in this paper a practical multisplitting method based on parallel iterative blocks and gives better results than classical GMRES method for the 3D Poisson problem we considered.
+\\

 
 The key idea of a multisplitting method to solve a large system of linear equations $Ax=b$ is defined as follows. The first step consists in partitioning the matrix $A$ in $L$ several ways 
 \begin{equation}
 
 The key idea of a multisplitting method to solve a large system of linear equations $Ax=b$ is defined as follows. The first step consists in partitioning the matrix $A$ in $L$ several ways 
 \begin{equation}

-A = M_l - N_l,
+A = M_\ell - N_\ell,

 \label{eq01}
 \end{equation}
 \label{eq01}
 \end{equation}

-where for all $l\in\{1,\ldots,L\}$ $M_l$ are non-singular matrices. Then the linear system is solved by iteration based on the obtained splittings as follows
+where for all $\ell\in\{1,\ldots,L\}$ $M_\ell$ are non-singular matrices. Then the linear system is solved by iteration based on the obtained splittings as follows

 \begin{equation}
 \begin{equation}

-x^{k+1}=\displaystyle\sum^L_{l=1} E_l M^{-1}_l (N_l x^k + b),~k=1,2,3,\ldots
+x^{k+1}=\displaystyle\sum^L_{\ell=1} E_\ell M^{-1}_\ell (N_\ell x^k + b),~k=1,2,3,\ldots

 \label{eq02}
 \end{equation}
 \label{eq02}
 \end{equation}

-where $E_l$ are non-negative and diagonal weighting matrices and their sum is an identity matrix $I$. The convergence of such a method is dependent on the condition
+where $E_\ell$ are non-negative and diagonal weighting matrices and their sum is an identity matrix $I$. The convergence of such a method is dependent on the condition

 \begin{equation}
 \begin{equation}

-\rho(\displaystyle\sum^L_{l=1}E_l M^{-1}_l N_l)<1.
+\rho(\displaystyle\sum^L_{\ell=1}E_\ell M^{-1}_\ell N_\ell)<1.

 \label{eq03}
 \end{equation}
 where $\rho$ is the spectral radius of the square matrix.
 
 The advantage of the multisplitting method is that at each iteration $k$ there are $L$ different linear sub-systems
 \begin{equation}
 \label{eq03}
 \end{equation}
 where $\rho$ is the spectral radius of the square matrix.
 
 The advantage of the multisplitting method is that at each iteration $k$ there are $L$ different linear sub-systems
 \begin{equation}

-v_l^k=M^{-1}_l N_l x_l^{k-1} + M^{-1}_l b,~l\in\{1,\ldots,L\},
+v_\ell^k=M^{-1}_\ell N_\ell x_\ell^{k-1} + M^{-1}_\ell b,~\ell\in\{1,\ldots,L\},

 \label{eq04}
 \end{equation}
 \label{eq04}
 \end{equation}

-to be solved independently by a direct or an iterative method, where $v_l^k$ is the solution of the local sub-system. Thus the computations of $\{v_l\}_{1\leq l\leq L}$ may be performed in parallel by a set of processors. A multisplitting method using an iterative method as an inner solver is called an inner-outer iterative method or a two-stage method. The results $v_l$ obtained from the different splittings~(\ref{eq04}) are combined to compute solution $x$ of the linear system by using the diagonal weighting matrices
+to be solved independently by a direct or an iterative method, where $v_\ell$ is the solution of the local sub-system. Thus the computations of $\{v_\ell\}_{1\leq \ell\leq L}$ may be performed in parallel by a set of processors. A multisplitting method using an iterative method as an inner solver is called an inner-outer iterative method or a two-stage method. The results $v_\ell$ obtained from the different splittings~(\ref{eq04}) are combined to compute solution $x$ of the linear system by using the diagonal weighting matrices

 \begin{equation}
 \begin{equation}

-x^k = \displaystyle\sum^L_{l=1} E_l v_l^k,
+x^k = \displaystyle\sum^L_{\ell=1} E_\ell v_\ell^k,

 \label{eq05}
 \end{equation}    
 \label{eq05}
 \end{equation}    

-In the case where the diagonal weighting matrices $E_l$ have only zero and one factors (i.e. $v_l$ are disjoint vectors), the multisplitting method is non-overlapping and corresponds to the block Jacobi method.
+In the case where the diagonal weighting matrices $E_\ell$ have only zero and one factors (i.e. $v_\ell$ are disjoint vectors), the multisplitting method is non-overlapping and corresponds to the block Jacobi method.

 
 %%%%%%%%%%%%%%%%%%%%%%%%
 %%%%%%%%%%%%%%%%%%%%%%%%
 
 \section{A two-stage method with a minimization}
 
 %%%%%%%%%%%%%%%%%%%%%%%%
 %%%%%%%%%%%%%%%%%%%%%%%%
 
 \section{A two-stage method with a minimization}

-Let $Ax=b$ be a given large and sparse linear system of $n$ equations to solve in parallel on $L$ clusters of processors, physically adjacent or geographically distant, where $A\in\mathbb{R}^{n\times n}$ is a square and  non-singular matrix, $x\in\mathbb{R}^{n}$ is the solution vector and $b\in\mathbb{R}^{n}$ is the right-hand side vector. The multisplitting of this linear system is defined as follows
+\label{sec:03}
+Let $Ax=b$ be a given large and sparse linear system of $n$ equations to solve in parallel on $L$ clusters of processors, physically adjacent or geographically distant, where $A\in\mathbb{R}^{n\times n}$ is a square and non-singular matrix, $x\in\mathbb{R}^{n}$ is the solution vector and $b\in\mathbb{R}^{n}$ is the right-hand side vector. The multisplitting of this linear system is defined as follows

 \begin{equation}
 \left\{
 \begin{array}{lll}
 \begin{equation}
 \left\{
 \begin{array}{lll}


@@ -152,36 +159,44 @@ b & = & [B_{1}, \ldots, B_{L}]
 \right.
 \label{sec03:eq01}
 \end{equation}  
 \right.
 \label{sec03:eq01}
 \end{equation}  

-where for $l\in\{1,\ldots,L\}$, $A_l$ is a rectangular block of size $n_l\times n$ and $X_l$ and $B_l$ are sub-vectors of size $n_l$ each, such that $\sum_ln_l=n$. In this work, we use a row-by-row splitting without overlapping in such a way that successive rows of sparse matrix $A$ and both vectors $x$ and $b$ are assigned to one cluster. So, the multisplitting format of the linear system is defined as follows
+where for $\ell\in\{1,\ldots,L\}$, $A_\ell$ is a rectangular block of size $n_\ell\times n$ and $X_\ell$ and $B_\ell$ are sub-vectors of size $n_\ell$ each, such that $\sum_\ell n_\ell=n$. In this work, we use a row-by-row splitting without overlapping in such a way that successive rows of sparse matrix $A$ and both vectors $x$ and $b$ are assigned to one cluster. So, the multisplitting format of the linear system is defined as follows

 \begin{equation}
 \begin{equation}

-\forall l\in\{1,\ldots,L\} \mbox{,~} \displaystyle\sum_{m=1}^{l-1}A_{lm}X_m + A_{ll}X_l + \displaystyle\sum_{m=l+1}^{L}A_{lm}X_m = B_l, 
+\forall \ell\in\{1,\ldots,L\} \mbox{,~} A_{\ell \ell}X_\ell + \displaystyle\sum_{\substack{m=1\\m\neq\ell}}^L A_{\ell m}X_m = B_\ell, 

 \label{sec03:eq02}
 \end{equation} 
 \label{sec03:eq02}
 \end{equation} 

-where $A_{lm}$ is a sub-block of size $n_l\times  n_m$ of the rectangular matrix $A_l$, $X_m\neq  X_l$ is a sub-vector of size $n_m$ of the solution vector $x$ and $\sum_{m\neq l}n_m+n_l=n$, for all $m\in\{1,\ldots,L\}$.
+where $A_{\ell m}$ is a sub-block of size $n_\ell\times  n_m$ of the rectangular matrix $A_\ell$, $X_m\neq  X_\ell$ is a sub-vector of size $n_m$ of the solution vector $x$ and $\sum_{m\neq \ell}n_m+n_\ell=n$, for all $m\in\{1,\ldots,L\}$.

 
 

-Our multisplitting method proceeds by iteration for solving the linear system in such a way each sub-system
+Our multisplitting method proceeds by iteration to solve the linear system in such a way that each sub-system

 \begin{equation}
 \left\{
 \begin{array}{l}
 \begin{equation}
 \left\{
 \begin{array}{l}

-A_{ll}X_l = Y_l \mbox{,~such that}\\
-Y_l = B_l - \displaystyle\sum_{\substack{m=1\\m\neq l}}^{L}A_{lm}X_m,
+A_{\ell \ell}X_\ell = Y_\ell \mbox{,~such that}\\
+Y_\ell = B_\ell - \displaystyle\sum_{\substack{m=1\\m\neq \ell}}^{L}A_{\ell m}X_m,

 \end{array}
 \right.
 \label{sec03:eq03}
 \end{equation}
 \end{array}
 \right.
 \label{sec03:eq03}
 \end{equation}

-is solved independently by a {\it cluster of processors} and communication are required to update the right-hand side vectors $Y_l$, such that the vectors $X_m$ represent the data dependencies between the clusters. In this work, we use the parallel GMRES method~\cite{ref34} as an inner iteration method to solve sub-systems~(\ref{sec03:eq03}). GMRES is one of the most used Krylov iterative methods to solve sparse linear systems in parallel on clusters of processors. In practice, GMRES is used with a preconditioner to improve its convergence. In this work, we used a preconditioning matrix equivalent to the main diagonal of sparse sub-matrix $A_{ll}$. This preconditioner is straightforward to implement in parallel and gives good performances in many situations.  
-
-It should be noted that the convergence of the inner iterative solver for the different sub-systems~(\ref{sec03:eq03}) does not necessarily involve the convergence of the multisplitting method. It strongly depends on the properties of the global sparse linear system to be solved and the computing environment~\cite{o1985multi,ref18}. Furthermore, the multisplitting
-of the linear system among several clusters of processors increases the spectral radius of the iteration matrix, thereby slowing the convergence. In this work, we based on the work presented in~\cite{huang1993krylov} to increase the convergence and improve the scalability of the multisplitting methods.
-
-In order to accelerate the convergence, we implemented the outer iteration of the multisplitting solver as a Krylov subspace method which minimizes some error function over a Krylov subspace~\cite{S96}. The Krylov subspace that we used is spanned by a basis composed of successive solutions issued from solving the $L$ splittings~(\ref{sec03:eq03})
+is solved independently by a {\it cluster of processors} and communications are required to update the right-hand side vectors $Y_\ell$, such that the vectors $X_m$ represent the data dependencies between the clusters. In this work, we use the parallel restarted GMRES method~\cite{ref34} as an inner iteration method to solve sub-systems~(\ref{sec03:eq03}). GMRES is one of the most used Krylov iterative methods to solve sparse linear systems. %In practice, GMRES is used with a preconditioner to improve its convergence. In this work, we used a preconditioning matrix equivalent to the main diagonal of sparse sub-matrix $A_{ll}$. This preconditioner is straightforward to implement in parallel and gives good performances in many situations.  
+
+It should  be noted that the convergence  of the inner iterative  solver for the
+different  sub-systems~(\ref{sec03:eq03})  does   not  necessarily  involve  the
+convergence of the multisplitting method.  It strongly depends on the properties
+of       the       global      sparse       linear       system      to       be
+solved~\cite{o1985multi,ref18}. Furthermore, the  splitting of the linear system
+among  several clusters  of  processors  increases the  spectral  radius of  the
+iteration  matrix, thereby  slowing the  convergence.  In  fact, the  larger the
+number of  splitting is, the larger the  spectral radius is.  In  this paper, we
+based  on   the  work   presented  in~\cite{huang1993krylov}  to   increase  the
+convergence and improve the scalability of the multisplitting methods.
+
+In order to accelerate the convergence, we implemented the outer iteration of the multisplitting solver as a Krylov iterative method which minimizes some error function over a Krylov subspace~\cite{S96}. The Krylov subspace that we used is spanned by a basis composed of successive solutions issued from solving the $L$ splittings~(\ref{sec03:eq03})

 \begin{equation}
 S=\{x^1,x^2,\ldots,x^s\},~s\leq n,
 \label{sec03:eq04}
 \end{equation}
 where for $j\in\{1,\ldots,s\}$, $x^j=[X_1^j,\ldots,X_L^j]$ is a solution of the global linear system. The advantage of such a Krylov subspace is that we need neither an orthogonal basis nor synchronizations between clusters to generate this basis.
 
 \begin{equation}
 S=\{x^1,x^2,\ldots,x^s\},~s\leq n,
 \label{sec03:eq04}
 \end{equation}
 where for $j\in\{1,\ldots,s\}$, $x^j=[X_1^j,\ldots,X_L^j]$ is a solution of the global linear system. The advantage of such a Krylov subspace is that we need neither an orthogonal basis nor synchronizations between clusters to generate this basis.
 

-The multisplitting method is periodically restarted every $s$ iterations with a new initial guess $\tilde{x}=S\alpha$ which minimizes the error function $\|b-Ax\|_2$ over the Krylov subspace spanned by vectors of  $S$. So $\alpha$ is defined as the solution of the large overdetermined linear system
+The multisplitting method is periodically restarted every $s$ iterations with a new initial guess $\tilde{x}=S\alpha$ which minimizes the error function $\|b-Ax\|_2$ over the Krylov subspace spanned by vectors of $S$. So $\alpha$ is defined as the solution of the large overdetermined linear system

 \begin{equation}
 R\alpha=b,
 \label{sec03:eq05}
 \begin{equation}
 R\alpha=b,
 \label{sec03:eq05}


@@ -196,54 +211,55 @@ which is associated with the least squares problem
 \text{minimize}~\|b-R\alpha\|_2,
 \label{sec03:eq07}
 \end{equation}  
 \text{minimize}~\|b-R\alpha\|_2,
 \label{sec03:eq07}
 \end{equation}  

-where $R^T$ denotes the transpose of the matrix $R$. Since $R$ (i.e. $AS$) and $b$ are split among $L$ clusters, the symmetric positive definite system~(\ref{sec03:eq06}) is solved in  parallel. Thus an iterative method would be more  appropriate than a direct one to solve this system. We use the parallel conjugate gradient method for the normal equations CGNR~\cite{S96,refCGNR}.
+where $R^T$ denotes the transpose of matrix $R$. Since $R$ (i.e. $AS$) and $b$ are split among $L$ clusters, the symmetric positive definite system~(\ref{sec03:eq06}) is solved in parallel. Thus an iterative method would be more appropriate than a direct one to solve this system. We use the parallel Conjugate Gradient method for the normal equations CGNR~\cite{S96,refCGNR}.

 
 \begin{algorithm}[!t]
 \caption{A two-stage linear solver with inner iteration GMRES method}
 \begin{algorithmic}[1]
 
 \begin{algorithm}[!t]
 \caption{A two-stage linear solver with inner iteration GMRES method}
 \begin{algorithmic}[1]

-\Input $A_l$ (sparse sub-matrix), $B_l$ (right-hand side sub-vector)
-\Output $X_l$ (solution sub-vector)\vspace{0.2cm}
-\State Load $A_l$, $B_l$
-\State Initialize the initial guess $x^0$
+\Input $A_\ell$ (sparse sub-matrix), $B_\ell$ (right-hand side sub-vector)
+\Output $X_\ell$ (solution sub-vector)\vspace{0.2cm}
+\State Load $A_\ell$, $B_\ell$
+\State Set the initial guess $x^0$

 \State Set the minimizer $\tilde{x}^0=x^0$
 \For {$k=1,2,3,\ldots$ until the global convergence}
 \State Restart with $x^0=\tilde{x}^{k-1}$:
 \For {$j=1,2,\ldots,s$}
 \State Set the minimizer $\tilde{x}^0=x^0$
 \For {$k=1,2,3,\ldots$ until the global convergence}
 \State Restart with $x^0=\tilde{x}^{k-1}$:
 \For {$j=1,2,\ldots,s$}

-\State Inner iteration solver: \Call{InnerSolver}{$x^0$, $j$}
-\State Construct basis $S$: add column vector $X_l^j$ to the matrix $S_l^k$
-\State Exchange local values of $X_l^j$ with the neighboring clusters
-\State Compute dense matrix $R$: $R_l^{k,j}=\sum^L_{i=1}A_{li}X_i^j$ 
+\State \label{line7}Inner iteration solver: \Call{InnerSolver}{$x^0$, $j$}
+\State Construct basis $S$: add column vector $X_\ell^j$ to the matrix $S_\ell^k$
+\State Exchange local values of $X_\ell^j$ with the neighboring clusters
+\State Compute dense matrix $R$: $R_\ell^{k,j}=\sum^L_{i=1}A_{\ell i}X_i^j$ 

 \EndFor 
 \EndFor 

-\State Minimization $\|b-R\alpha\|_2$: \Call{UpdateMinimizer}{$S_l$, $R$, $b$, $k$}
-\State Local solution of the linear system $Ax=b$: $X_l^k=\tilde{X}_l^k$
-\State Exchange the local minimizer $\tilde{X}_l^k$ with the neighboring clusters
+\State \label{line12}Minimization $\|b-R\alpha\|_2$: \Call{UpdateMinimizer}{$S_\ell$, $R$, $b$, $k$}
+\State Local solution of linear system $Ax=b$: $X_\ell^k=\tilde{X}_\ell^k$
+\State Exchange the local minimizer $\tilde{X}_\ell^k$ with the neighboring clusters

 \EndFor
 
 \Statex
 
 \Function {InnerSolver}{$x^0$, $j$}
 \EndFor
 
 \Statex
 
 \Function {InnerSolver}{$x^0$, $j$}

-\State Compute local right-hand side $Y_l = B_l - \sum^L_{\substack{m=1\\m\neq l}}A_{lm}X_m^0$
-\State Solving local splitting $A_{ll}X_l^j=Y_l$ using parallel GMRES method, such that $X_l^0$ is the initial guess
-\State \Return $X_l^j$
+\State Compute local right-hand side $Y_\ell = B_\ell - \sum^L_{\substack{m=1\\m\neq \ell}}A_{\ell m}X_m^0$
+\State Solving local splitting $A_{\ell \ell}X_\ell^j=Y_\ell$ using parallel GMRES method, such that $X_\ell^0$ is the initial guess
+\State \Return $X_\ell^j$

 \EndFunction
 
 \Statex
 
 \EndFunction
 
 \Statex
 

-\Function {UpdateMinimizer}{$S_l$, $R$, $b$, $k$}
+\Function {UpdateMinimizer}{$S_\ell$, $R$, $b$, $k$}

 \State Solving normal equations $(R^k)^TR^k\alpha^k=(R^k)^Tb$ in parallel by $L$ clusters using parallel CGNR method
 \State Solving normal equations $(R^k)^TR^k\alpha^k=(R^k)^Tb$ in parallel by $L$ clusters using parallel CGNR method

-\State Compute local minimizer $\tilde{X}_l^k=S_l^k\alpha^k$
-\State \Return $\tilde{X}_l^k$
+\State Compute local minimizer $\tilde{X}_\ell^k=S_\ell^k\alpha^k$
+\State \Return $\tilde{X}_\ell^k$

 \EndFunction
 \end{algorithmic}
 \label{algo:01}
 \end{algorithm}
 
 \EndFunction
 \end{algorithmic}
 \label{algo:01}
 \end{algorithm}
 

-The main key points of our multisplitting method to solve a large sparse linear system are given in Algorithm~\ref{algo:01}. This algorithm is based on a two-stage method with a minimization using GMRES iterative method as an inner solver. It is executed in parallel by each cluster of processors. Matrices and vectors with the subscript $l$ represent the local data for cluster  $l$, where $l\in\{1,\ldots,L\}$. The two-stage solver uses two different parallel iterative algorithms: GMRES method to solve each splitting~(\ref{sec03:eq03}) on a cluster of processors, and CGNR method executed in parallel by all clusters to minimize the function error~(\ref{sec03:eq07}) over the Krylov subspace spanned by $S$. The algorithm requires two global synchronizations between $L$ clusters. The first one is performed at line~$12$ in Algorithm~\ref{algo:01} to exchange local values of vector solution $x$ (i.e. the minimizer $\tilde{x}$) required to restart the multisplitting solver. The second one is needed to construct the matrix $R$ of the Krylov subspace. We chose to perform this latter synchronization $s$ times in every outer iteration $k$ (line~$7$ in Algorithm~\ref{algo:01}). This is a straightforward way to compute the sparse matrix-dense matrix multiplication $R=AS$. We implemented all synchronizations by using message passing collective communications of MPI library.
+The main key points of our Krylov multisplitting method to solve a large sparse linear system are given in Algorithm~\ref{algo:01}. This algorithm is based on a two-stage method with a minimization using restarted GMRES iterative method as an inner solver. It is executed in parallel by each cluster of processors. Matrices and vectors with the subscript $\ell$ represent the local data for cluster $\ell$, where $\ell\in\{1,\ldots,L\}$. The two-stage solver uses two different parallel iterative algorithms: GMRES method to solve each splitting~(\ref{sec03:eq03}) on a cluster of processors, and CGNR method executed in parallel by all clusters to minimize the function error~(\ref{sec03:eq07}) over the Krylov subspace spanned by $S$. The algorithm requires two global synchronizations between $L$ clusters. The first one is performed at line~\ref{line12} in Algorithm~\ref{algo:01} to exchange local values of vector solution $x$ (i.e. the minimizer $\tilde{x}$) required to restart the multisplitting solver. The second one is needed to construct the matrix $R$. We chose to perform this latter synchronization $s$ times in every outer iteration $k$ (line~\ref{line7} in Algorithm~\ref{algo:01}). This is a straightforward way to compute the sparse matrix-dense matrix multiplication $R=AS$. We implemented all synchronizations by using message passing collective communications of MPI library.

 
 %%%%%%%%%%%%%%%%%%%%%%%%
 %%%%%%%%%%%%%%%%%%%%%%%%
 
 \section{Experiments}
 
 %%%%%%%%%%%%%%%%%%%%%%%%
 %%%%%%%%%%%%%%%%%%%%%%%%
 
 \section{Experiments}

+\label{sec:04}

 In order to illustrate  the interest  of our algorithm. We have  compared our
 algorithm  with  the  GMRES  method  which  is a very  well  used  method  in  many
 situations.  We have chosen to focus on only one problem which is very simple to
 In order to illustrate  the interest  of our algorithm. We have  compared our
 algorithm  with  the  GMRES  method  which  is a very  well  used  method  in  many
 situations.  We have chosen to focus on only one problem which is very simple to


@@ -266,29 +282,32 @@ obtained for  a 3D Poisson  problem, the number  of iterations is high.  Using a
 preconditioner  it  is   possible  to  reduce  the  number   of  iterations  but
 preconditioners are not scalable when using many cores.
 
 preconditioner  it  is   possible  to  reduce  the  number   of  iterations  but
 preconditioners are not scalable when using many cores.
 

-Doing many experiments  with many cores is  not easy and requires to  access to a
-supercomputer  with several  hours for  developing  a code  and then  improving
-it. In the following we presented  some experiments we could achieved out on the
-Hector architecture,  the previous UK's  high-end computing resource,  funded by
-the UK Research Councils, which has been stopped in the early 2014.
+%Doing many experiments  with many cores is  not easy and requires to  access to a supercomputer  with several  hours for  developing  a code  and then  improving it. 
+In the following we present some experiments we could achieved out on the Hector
+architecture,  a UK's  high-end computing  resource, funded  by the  UK Research
+Councils~\cite{hector}.  This is  a Cray  XE6 supercomputer,  equipped  with two
+16-core AMD  Opteron 2.3 Ghz  and 32 GB  of memory. Machines  are interconnected
+with a 3D torus.

 
 

-In the experiments  we report the size of the 3D  poisson considered\LZK[]{Suite\dots ?}
-
-
-The first column  shows the size of the  problem The size is chosen  in order to
-have approximately 50,000 components per core.  The second column represents the
+Table~\ref{tab1} shows  the result of  the experiments.  The first  column shows
+the  size of  the  3D Poisson  problem.  The size  is chosen  in  order to  have
+approximately  50,000 components  per core.   The second  column  represents the

 number of  cores used. In parenthesis,  there is the decomposition  used for the
 Krylov multisplitting. The  third column and the sixth  column respectively show
 number of  cores used. In parenthesis,  there is the decomposition  used for the
 Krylov multisplitting. The  third column and the sixth  column respectively show

-the execution time for the GMRES  and the Kyrlow multisplitting code. The fourth
-and  the   seventh  column   describes  the  number   of  iterations.   For  the
+the execution time for the GMRES  and the Krylov multisplitting codes. The fourth
+and  the   seventh  column  describes   the  number  of  iterations.    For  the

 multisplitting  code, the  total number  of inner  iterations is  represented in
 multisplitting  code, the  total number  of inner  iterations is  represented in

-parenthesis.
-
- We  also give  the other parameters:  the restart  for the GRMES method....\\
-
-\LZK{La seule remarque que j'ai pu tirée des deux tableaux c'est le fait qu'il y a plus de procs dans un cluster pour 2x4096 et c'est pour cette configuration qu'on a un bon speedup avec préconditionnement!!! Mais je ne sais pas toujours pourquoi?}
-
-\begin{table}[p]
+parenthesis. For  the GMRES code (alone  and in the  multisplitting version) the
+restart parameter is fixed to 16. The precision of the GMRES version is fixed to
+1e-6. For  the multisplitting,  there are two  precisions, one for  the external
+solver which is fixed to 1e-6 and another one for the inner solver (GMRES) which
+is fixed to 1e-10. It should be noted  that a high precision is used but we also
+fixed  a maximum number of  iterations for each  internal step. In  practice, we
+limit the  number of iterations in the internal step to  10. So an internal  iteration is finished
+when the precision is reached or  when the maximum internal number of iterations
+is reached. The precision and the maximum number of iterations of CGNR method are fixed to 1e-25 and 20 respectively. The size of the Krylov subspace basis $S$ is fixed to 10 vectors.
+
+\begin{table}[htbp]

 \begin{center}
 \begin{tabular}{|c|c||c|c|c||c|c|c||c|} 
 \hline
 \begin{center}
 \begin{tabular}{|c|c||c|c|c||c|c|c||c|} 
 \hline


@@ -296,49 +315,72 @@ parenthesis.
  \cline{3-8}
            &                   &  Time (s) & nb Iter. & $\Delta$  &   Time (s)& nb Iter. & $\Delta$ & \\
 \hline
  \cline{3-8}
            &                   &  Time (s) & nb Iter. & $\Delta$  &   Time (s)& nb Iter. & $\Delta$ & \\
 \hline

-
-$590^3$ & 4096 (2x2048)        &  433.1    & 55,494    & 4.92e-7  &  74.1    & 1,101(8,211) & 6.62e-08  & 5.84   \\
+$468^3$ & 2,048 (2x1,024)        &  299.7    & 41,028    & 5.02e-8  &  48.4    & 691(6,146) & 8.24e-08  & 6.19   \\
+\hline
+$590^3$ & 4,096 (2x2,048)        &  433.1    & 55,494    & 4.92e-7  &  74.1    & 1,101(8,211) & 6.62e-08  & 5.84   \\

 \hline
 \hline

-$743^3$ & 8192 (2x4096)        & 704.4     & 87,822    & 4.80e-07 &  151.2   & 3,061(14,914) & 5.87e-08 & 4.65    \\
+$743^3$ & 8,192 (2x4,096)        & 704.4     & 87,822    & 4.80e-07 &  151.2   & 3,061(14,914) & 5.87e-08 & 4.65    \\

 \hline
 \hline

-$743^3$ & 8192 (4x2048)        & 704.4     & 87,822    & 4.80e-07 &  110.3   & 1,531(12,721) & 1.47e-07& 6.39  \\
+$743^3$ & 8,192 (4x2,048)        & 704.4     & 87,822    & 4.80e-07 &  110.3   & 1,531(12,721) & 1.47e-07& 6.39  \\

 \hline
 
 \end{tabular}
 \hline
 
 \end{tabular}

-\caption{Results without preconditioner}
+\caption{Results}

 \label{tab1}
 \end{center}
 \end{table}
 
 
 \label{tab1}
 \end{center}
 \end{table}
 
 

-\begin{table}[p]
-\begin{center}
-\begin{tabular}{|c|c||c|c|c||c|c|c||c|} 
-\hline
-\multirow{2}{*}{Pb size}&\multirow{2}{*}{Nb. cores} &  \multicolumn{3}{c||}{GMRES} &  \multicolumn{3}{c||}{Krylov Multisplitting} & \multirow{2}{*}{Ratio}\\
- \cline{3-8}
-           &                   &  Time (s) & nb Iter. & $\Delta$  &   Time (s)& nb Iter. & $\Delta$ & \\
-\hline
-
-$590^3$ & 4096 (2x2048)        &  433.0    & 55,494    & 4.92e-7  &  80.4    & 1,091(9,545) & 7.64e-08  & 5.39   \\
-\hline
-$743^3$ & 8192 (2x4096)        & 704.4     & 87,822    & 4.80e-07 &  110.2   & 1,401(12,379) & 1.11e-07 & 6.39    \\
-\hline
-$743^3$ & 8192 (4x2048)        & 704.4     & 87,822    & 4.80e-07 &  139.8   & 1,891(15,960) & 1.60e-07& 5.03  \\
-\hline
-
-\end{tabular}
-\caption{Results with preconditioner}
-\label{tab2}
-\end{center}
-\end{table}
+From these  experiments, it can be  observed that the  multisplitting version is
+always  faster   than  the  GMRES   version.   The  acceleration  gain   of  the
+multisplitting version is between 4 and 6.  It can be noticed that the number of
+iterations is drastically reduced with the multisplitting version even it is not
+neglectable. Moreover, with 8,192 cores, we  can see that using 4 clusters gives
+better performance than simply using 2 clusters. In fact, we can remark that the
+precision with 2 clusters is slightly  better but in both cases the precision is
+under the specified threshold.

 
 \section{Conclusion and perspectives}
 
 \section{Conclusion and perspectives}

-
-Other applications (=> other matrices)\\
-Larger experiments\\
-Async\\
-Overlapping
+We  have implemented  a  Krylov  multisplitting method  to  solve sparse  linear
+systems  on large-scale computing  platforms.  We  have developed  a synchronous
+two-stage  method based  on the  block Jacobi  multisaplitting which  uses GMRES
+iterative  method as  an inner  iteration.  Our  contribution in  this  paper is
+twofold. First we provide a multi cluster decomposition that allows us to choose
+the  appropriate size  of  the clusters  according  to the  architecures of  the
+supercomputer.  Second,   we  have  implemented  the  outer   iteration  of  the
+multisplitting method  as a  Krylov subspace method  which minimizes  some error
+function.  This  increases the convergence  and improves the scalability  of the
+multisplitting method.
+
+We  have tested  our multisplitting  method to  solve the  sparse  linear system
+issued from  the discretization of  a 3D Poisson  problem. We have  compared its
+performances to the  classical GMRES method on a  supercomputer composed of 2,048
+to 8,192 cores. The experimental results showed that the multisplitting method is
+about 4  to 6  times faster  than the GMRES  method for  different sizes  of the
+problem split into  2 or 4 blocks when using  multisplitting method. Indeed, the
+GMRES  method  has  difficulties to  scale  with  many  cores while  the  Krylov
+multisplitting  method  allows to  hide  latency  and  reduce the  inter-cluster
+communications.
+
+In future  works, we plan to conduct  experiments on larger number  of cores and
+test  the  scalability  of  our   Krylov  multisplitting  method.  It  would  be
+interesting  to validate its  performances to  solve other  linear/nonlinear and
+symmetric/nonsymmetric problems.  Moreover, we intend  to develop multisplitting
+methods based  on asynchronous iteration in which  communications are overlapped
+by computations.  These methods would  be interesting for platforms  composed of
+distant  clusters interconnected  by  a high-latency  network.  In addition,  we
+intend  to investigate  the  convergence  improvements of  our  method by  using
+preconditioning  techniques  for  Krylov  iterative methods  and  multisplitting
+methods with overlapping blocks.
+
+\section{Acknowledgement}
+The authors would like to thank Mark Bull of the EPCC his fruitful remarks and the facilities of HECToR.
+
+%Other applications (=> other matrices)\\
+%Larger experiments\\
+%Async\\
+%Overlapping\\
+%preconditioning

 
 
 %%%%%%%%%%%%%%%%%%%%%%%%
 
 
 %%%%%%%%%%%%%%%%%%%%%%%%