12-12-2014 v01

[Krylov_multi.git] / krylov_multi_reviewed.tex

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\title{A scalable multisplitting algorithm to solve large sparse linear systems} 
\date{}

\author[1]{Raphaël Couturier}
\author[2]{ Lilia Ziane Khodja}
\affil[1]{ Femto-ST Institute\\
    University of Franche Comte\\
    France\\
    email: raphael.couturier@univ-fcomte.fr}
\affil[2]{Inria Bordeaux Sud-Ouest\\
    France\\
    email: lilia.ziane@inria.fr}
\begin{document}


\maketitle


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

\begin{abstract}
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
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  with  up   to  8,192  cores.   They  show  the  obtained
improvements compared to a classical GMRES both in terms of number of iterations
and in terms of 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  different researchers.   For
example, the GMRES method and the  Conjugate Gradient method are very well known
and  used~\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.
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.

%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 in  using  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.\\


%%% AJOUTE************************
%%%*******************************
\noindent {\bf Contributions:}\\ 
In this work we develop a new parallel two-stage algorithm for large-scale clusters. Our objective is to mix between Krylov based iterative methods and the multisplitting method to improve the scalability. In fact Krylov subspace methods are well-known for their good convergence compared to other iterative methods. So our main contribution is to use the multisplitting method which splits the problem to solve into different blocks in order to reduce the large amount of communications and, to implement both inner and outer iterations as Krylov subspace iterations improving the convergence of the multisplitting algorithm.\\
%%%*******************************
%%%*******************************

The present paper is  organized as follows. First, Section~\ref{sec:02} presents
some  related  works and  the  principle  of  multisplitting methods.  Then,  in
Section~\ref{sec:03}  the algorithm  of our  Krylov multisplitting
method, based  on inner-outer  iterations, is presented. 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.


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

\section{Related works and presentation of the multisplitting method}
\label{sec:02}
A general framework  to study parallel multisplitting methods has  been presented in~\cite{o1985multi}
by O'Leary and White. Convergence conditions are given for the
most general cases.  Many authors have improved multisplitting algorithms by proposing,
for  example,  an  asynchronous  version~\cite{bru1995parallel} or  convergence
conditions~\cite{bai1999block,bahi2000asynchronous}     or  other
two-stage algorithms~\cite{frommer1992h,bru1995parallel}.

In~\cite{huang1993krylov},  the  authors  have 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 solutions to  the first task which  is in
charge of  combining 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 the
convergence of the global system. 

In~\cite{couturier2008gremlins}, the  authors have developed practical implementations
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  designed 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
an asynchronous  multisplitting algorithm  could be more  efficient  than traditional
solvers on an exascale architecture with hundreds of thousands of cores.

So, compared to these works, we propose in this paper a practical multisplitting method based on parallel iterative blocks which 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}
A = M_\ell - N_\ell,
\label{eq01}
\end{equation}
where for all $\ell\in\{1,\ldots,L\}$ $M_\ell$ are non-singular matrices. Then the linear system is solved by an iteration based on the obtained splittings as follows
\begin{equation}
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}
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}
\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}
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}
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}
x^k = \displaystyle\sum^L_{\ell=1} E_\ell v_\ell^k,
\label{eq05}
\end{equation}    
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}
\label{sec:03}

%%% MODIFIE ************************
%%%*********************************
Let $Ax=b$ be a given large and sparse linear system of $n$ equations where $A\in\mathbb{R}^{n\times n}$ is a sparse 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. We use a multisplitting method to solve the linear system on a large computing platform in order to reduce the communications. Let the computing platform be composed of $L$ clusters of processors physically adjacent or geographically distant. In this work we apply the block Jacobi multisplitting to the linear system as follows
%%%*********************************
%%%*********************************


\begin{equation}
\left\{
\begin{array}{lll}
A & = & [A_{1}, \ldots, A_{L}]\\
x & = & [X_{1}, \ldots, X_{L}]\\
b & = & [B_{1}, \ldots, B_{L}]
\end{array}
\right.
\label{sec03:eq01}
\end{equation}  
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 $\sum_\ell n_\ell=n$. 
%%% MODIFIE ***********************
%%%********************************
The splitting is performed row-by-row 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}
\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} 
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 to solve the linear system in such a way that each sub-system
\begin{equation}
\left\{
\begin{array}{l}
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}
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}). 
%%% MODIFIE ***********************
%%%********************************
GMRES is one of the most used Krylov iterative methods to solve sparse linear systems by minimizing the residuals over an orthonormal basis of a Krylov subspace. 
%%%********************************
%%%********************************
%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 algorithm.  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  splittings is, the larger the  spectral radius is.  In  this paper, our
work is based  on   the  work   presented  in~\cite{huang1993krylov}  to   increase  the
convergence and improve the scalability of the multisplitting methods.

%%% AJOUTE ************************
%%%********************************
Krylov subspace methods are well-known for their good convergence compared to other iterative methods. 
%%%********************************
%%%********************************
In order to accelerate the convergence, we implemented the outer iteration of our 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. 
%%% MODIFIE ***********************
%%%********************************
The advantage of such a Krylov subspace is that we neither need an orthonormal basis nor any synchronization between clusters is necessary to orthogonalize the generated basis. This avoids to perform other synchronizations between the blocks of processors.

The multisplitting method is periodically restarted every $s$ iterations with a new initial guess $\tilde{x}=S\alpha$ which minimizes an error function, in our case it minimizes the residuals $\|b-Ax\|_2$ 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}
\end{equation}
where $R=AS$ is a dense rectangular matrix of size $n\times s$ and $s\ll n$. This leads us to solve a system of normal equations
\begin{equation}
R^TR\alpha=R^Tb,
\label{sec03:eq06}
\end{equation}
which is associated with the least squares problem
\begin{equation}
\text{minimize}~\|b-R\alpha\|_2,
\label{sec03:eq07}
\end{equation}  
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]
\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 \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 
\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}{$x^0$, $j$}
\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

\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 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}

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: the GMRES method to solve each splitting~(\ref{sec03:eq03}) on a cluster of processors, and the CGNR method, executed periodically 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  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}
\label{sec:04}
%%% MODIFIE ***********************
%%%********************************
In order to illustrate the interest of our Krylov multisplitting algorithm, we have compared its performances with those of a classical block Jacobi multisplitting method and those of the GMRES method which is a commonly used method in many situations.
%%%********************************
%%%********************************
 We have chosen to focus on only one problem which is very simple to implement: a 3 dimension Poisson problem.

\begin{equation}
\left\{
                \begin{array}{ll}
                  \nabla u&=f \mbox{~in~} \omega\\
                  u &=0 \mbox{~on~}  \Gamma=\partial \omega
                \end{array}
              \right.
\end{equation}

After discretization, with a finite  difference scheme, a seven point stencil is
used. It  is well-known that the  spectral radius of  matrices representing such
problems are very close to 1.  Moreover, the larger the number of discretization
points is,  the closer to 1  the spectral radius  is.  Hence, to solve  a matrix
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.


%%% AJOUTE ************************
%%%********************************
We have performed some experiments on an infiniband cluster of three Intel Xeon quad-core E5620 CPUs of 2.40 GHz and 12 GB of memory. For the GMRES code (alone and in both multisplitting versions) the restart parameter is fixed to 16. The precision of the GMRES version is fixed to 1e-6. For the multisplitting versions, 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 used by our Krylov multisplitting algorithm are fixed to 1e-25 and 20 respectively. The size of the Krylov subspace basis $S$ is fixed to 10 vectors.

\begin{figure}[htbp]
\centering
  \includegraphics[width=0.8\textwidth]{strong_scaling_150x150x150}
\caption{Strong scaling with 3 clusters of cores to solve a 3D Poisson problem of size $150^3$ components}
\label{fig:001}
\end{figure}

\begin{figure}[htbp]
\centering
\begin{tabular}{c}
\includegraphics[width=0.8\textwidth]{weak_scaling_280k} \\ \includegraphics[width=0.8\textwidth]{weak_scaling_280K}\\
\end{tabular}
\caption{Weak scaling with 3 clusters of cores to solve a 3D Poisson problem with approximately 280K components per core}
\label{fig:002}
\end{figure}

The experiments are performed on 3 different clusters of cores interconnected by an infiniband network (each cluster is a quad-core CPU). Figures~\ref{fig:001} and~\ref{fig:002} show the scalability performances of GMRES, classical multisplitting and Krylov multisplitting methods: strong and weak scaling are presented respectively. We can remark from these figures that the performances of our Krylov multisplitting method are better than those of GMRES and classical multisplitting methods. In the experiments conducted in this work, our method is about twice faster than the GMRES method and about 9 times faster than the classical multisplitting method. Our multisplitting method uses a minimization step over a Krylov subspace which reduces the number of iterations and accelerates the convergence. We can also remark that the performances of the classical block Jacobi multisplitting method are the worst compared with those of the other two methods. This is why in the following experiments we compare the performances of our Krylov multisplitting method with only those of the GMRES method.
%%%********************************
%%%********************************


%%% MODIFIE ************************
%%%*********************************
%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 achieve 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. The different parameters used by the GMRES and the Krylov multisplitting codes are as those previously mentioned. 

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 brackets,  one can find the decomposition  used for the
Krylov multisplitting. The  third column and the sixth  column respectively show
the execution time for the GMRES  and the Krylov multisplitting codes. The fourth
and  the   seventh  column  describe   the  number  of  iterations.    For  the
multisplitting  code, the  total number  of inner  iterations is  represented in
brackets.
%%%********************************
%%%******************************** 

\begin{table}[htbp]
\begin{center}
\begin{changemargin}{-1.8cm}{0cm}
\begin{small}
\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
$468^3$ & 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
$743^3$ & 8,192 (2x4,096)        & 704.4     & 87,822    & 4.80e-07 &  151.2   & 3,061(14,914) & 5.87e-08 & 4.65    \\
\hline
$743^3$ & 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}
\caption{Results}
\label{tab1}
\end{small}
\end{changemargin}
\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 ranges between 4 and 6.  It can be noticed that the number of
iterations is drastically reduced with the multisplitting version even it is not
negligible. Moreover, with 8,192 cores, we  can see that using 4 clusters gives a
better performance than simply using 2 clusters. In fact, we can notice that the
precision with 2 clusters is slightly  better but in both cases the precision is
under the specified threshold.


%%% AJOUTE************************
%%%*******************************
In Figure~\ref{fig:01}, the number of iterations per second is reported for both
GMRES and the  multisplitting methods. It should be noted that  we took only the
inner number  of iterations (i.e.  the GMRES iterations) for  the multisplitting
method. Iterations of CGNR are not  taken into account. From this figure, it can
be seen that the  number of iterations per second is higher  with GMRES but it is
not  so different  with the  multisplitting method.  For the  case  with $8,192$
cores,  the number of  iterations per  second with  4 clusters  is approximately
equals to 115. So it is not different from GMRES.

\begin{figure}[htbp]
\centering
  \includegraphics[width=0.7\textwidth]{nb_iter_sec}
\caption{Number of iterations per second  with the same parameters as in Table~\ref{tab1} (weak scaling) with only 2 clusters}
\label{fig:01}
\end{figure}

\noindent {\bf Final remarks:}\\
It should  be noted, on  the one  hand, that the  development of a  complete new
method usable with any  kind of problem is a really long  and fastidious task if
one is working from  scratch. On the other hand, using an  existing tool for the
inner solver is also not easy because it requires to make link between the inner
solver  and the outer  one.  We  plan to  do that  later with  engineers working
specifically on  that point.  Moreover,  we think that  it is very  important to
analyze the convergence  of this method compared to other  methods. In this work,
we have  focused on the  description of this  method and its performance  with a
typical application. Many other investigations are required for this method as explained in the next section.
%%%*******************************
%%%*******************************

\section{Conclusion and perspectives}
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  multisplitting 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
up-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 the  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 numbers  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 iterations 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


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