11-01-2014 V1

[Krylov_multi.git] / krylov_multi.tex

diff --git a/krylov_multi.tex b/krylov_multi.tex
index 82cf45e9e563b28c4fb2afd8c82ef26f22957294..1b8b43e88a023a93d971a4113ee38a6d75c81452 100644 (file)
--- a/krylov_multi.tex
+++ b/krylov_multi.tex
@@ -38,9 +38,14 @@ classical GMRES both in terms of number of iterations and execution times.
 
 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 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
+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
+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
 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


@@ -48,7 +53,60 @@ thousands of cores are used.
 
 
 A completer...
 
 
 A completer...

-On ne peut pas parler de tout...
+On ne peut pas parler de tout...\\
+
+
+
+
+%%%%%%%%%%%%%%%%%%%%%%%
+%% BEGIN
+%%%%%%%%%%%%%%%%%%%%%%%
+The key idea  of the multisplitting method for  solving a large system
+of linear equations $Ax=b$ consists  in partitioning the matrix $A$ in
+$L$ several ways
+\begin{equation}
+A = M_l - N_l,~l\in\{1,\ldots,L\},
+\label{eq01}
+\end{equation}
+where $M_l$ are nonsingular matrices. Then the linear system is solved
+by iteration based on the multisplittings as follows
+\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
+\label{eq02}
+\end{equation}
+where $E_l$ are non-negative and diagonal weighting matrices such that
+$\sum^L_{l=1}E_l=I$ ($I$ is an identity matrix).  Thus the convergence
+of such a method is dependent on the condition
+\begin{equation}
+\rho(\displaystyle\sum^L_{l=1}E_l M^{-1}_l N_l)<1.
+\label{eq03}
+\end{equation}
+
+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\},
+\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
+calculations  of $v_l^k$  may be  performed in  parallel by  a  set of
+processors.   A multisplitting  method using  an iterative  method for
+solving the $L$ linear  sub-systems is called an inner-outer iterative
+method or a  two-stage method.  The results $v_l^k$  obtained from the
+different splittings~(\ref{eq04}) are combined to compute the solution
+$x^k$ of the linear system by using the diagonal weighting matrices
+\begin{equation}
+x^k = \displaystyle\sum^L_{l=1} E_l v_l^k,
+\label{eq05}
+\end{equation}    
+In the case where the diagonal weighting matrices $E_l$ have only zero
+and   one   factors  (i.e.   $v_l^k$   are   disjoint  vectors),   the
+multisplitting method is non-overlapping  and corresponds to the block
+Jacobi method.
+%%%%%%%%%%%%%%%%%%%%%%%
+%% END
+%%%%%%%%%%%%%%%%%%%%%%%

 
 \section{Related works}
 
 
 \section{Related works}
 


@@ -56,15 +114,15 @@ On ne peut pas parler de tout...
 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

-for  example  a  asynchronous  version  \cite{bru1995parallel}  and  convergence
-condition  \cite{bai1999block,bahi2000asynchronous}   in  this  case   or  other
-two-stage algorithms~\cite{frommer1992h,bru1995parallel}
+for  example  an  asynchronous  version  \cite{bru1995parallel}  and  convergence
+conditions  \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
 
 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
+basis for  which the first task minimizes  the error function over  the basis to

 increase the convergence, then the other tasks receive the update solution until
 convergence of the global system. 
 
 increase the convergence, then the other tasks receive the update solution until
 convergence of the global system. 
 


@@ -75,7 +133,11 @@ 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.
 
 solve large scale linear systems. Inner  solvers could be based on scalar direct
 method with the LU method or scalar iterative one with GMRES.
 

-
+In~\cite{prace-multi},  the  authors  have  proposed a  parallel  multisplitting
+algorithm in which large block are solved using a GMRES solver. The authors have
+performed large scale experimentations upto  32.768 cores and they conclude that
+asynchronous  multisplitting algorithm  could more  efficient  than traditionnal
+solvers on exascale architecture with hunders of thousands of cores.

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


@@ -83,12 +145,12 @@ method with the LU method or scalar iterative one with GMRES.
 
 
 \section{A two-stage method with a minimization}
 
 
 \section{A two-stage method with a minimization}

-Let $Ax=b$ be a given sparse and large linear system of $n$ equations
-to solve in parallel on $L$ clusters, physically adjacent or geographically
-distant, where $A\in\mathbb{R}^{n\times n}$ is a square and nonsingular
-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:
+Let $Ax=b$ be a given sparse  and large linear system of $n$ equations
+to  solve  in  parallel   on  $L$  clusters,  physically  adjacent  or
+geographically distant, where $A\in\mathbb{R}^{n\times n}$ is a square
+and  nonsingular 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}


@@ -99,19 +161,24 @@ b & = & [B_{1}, \ldots, B_{L}]
 \right.
 \label{sec03:eq01}
 \end{equation}  
 \right.
 \label{sec03:eq01}
 \end{equation}  

-where for all $l\in\{1,\ldots,L\}$ $A_l$ is a rectangular block of size $n_l\times n$
-and $X_l$ and $B_l$ are sub-vectors of size $n_l$, such that $\sum_ln_l=n$. In this
-case, we use a row-by-row splitting without overlapping in such a way that successive
-rows of the sparse matrix $A$ and both vectors $x$ and $b$ are assigned to a cluster.
-So, the multisplitting format of the linear system is defined as follows:
+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 $n_l$, such
+that  $\sum_ln_l=n$.  In this  case,  we  use  a row-by-row  splitting
+without overlapping in  such a way that successive  rows of the 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 l\in\{1,\ldots,L\} \mbox{,~} \displaystyle\sum_{i=1}^{l-1}A_{li}X_i + A_{ll}X_l + \displaystyle\sum_{i=l+1}^{L}A_{li}X_i = B_l, 
 \label{sec03:eq02}
 \end{equation} 
 \begin{equation}
 \forall l\in\{1,\ldots,L\} \mbox{,~} \displaystyle\sum_{i=1}^{l-1}A_{li}X_i + A_{ll}X_l + \displaystyle\sum_{i=l+1}^{L}A_{li}X_i = B_l, 
 \label{sec03:eq02}
 \end{equation} 

-where $A_{li}$ is a block of size $n_l\times n_i$ of the rectangular matrix $A_l$, $X_i\neq X_l$
-is a sub-vector of size $n_i$ of the solution vector $x$ and $\sum_{i<l}n_i+\sum_{i>l}n_i+n_l=n$,
-for all $i\in\{1,\ldots,l-1,l+1,\ldots,L\}$. Therefore, each cluster $l$ is in charge of solving
-the following spare sub-linear system: 
+where $A_{li}$ is  a block of size $n_l\times  n_i$ of the rectangular
+matrix  $A_l$, $X_i\neq  X_l$ is  a sub-vector  of size  $n_i$  of the
+solution vector  $x$ and $\sum_{i<l}n_i+\sum_{i>l}n_i+n_l=n$,  for all
+$i\in\{1,\ldots,l-1,l+1,\ldots,L\}$.
+
+The multisplitting method proceeds by iteration for solving the linear
+system in such a way each sub-system

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


@@ -121,7 +188,70 @@ Y_l = B_l - \displaystyle\sum_{i=1,i\neq l}^{L}A_{li}X_i,
 \right.
 \label{sec03:eq03}
 \end{equation}
 \right.
 \label{sec03:eq03}
 \end{equation}

-where the sub-vectors $X_i$ define the data dependencies between the cluster $l$ and other clusters.
+is solved  independently by a cluster of  processors and communication
+are required  to update the  right-hand side vectors $Y_l$,  such that
+the  vectors  $X_i$  represent   the  data  dependencies  between  the
+clusters. In this work, we use  the GMRES method as an inner iteration
+method  for  solving   the  sub-systems~(\ref{sec03:eq03}).  It  is  a
+well-known iterative method which  gives good performances for solving
+sparse linear systems in parallel on a cluster of processors.
+
+It should be noted that  the convergence of the inner iterative solver
+for  the  different  linear  sub-systems~(\ref{sec03:eq03})  does  not
+necessarily involve  the convergence of the  multisplitting method. It
+strongly depends on  the properties of the 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  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  implement  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  space of  the method 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  $k\in\{1,\ldots,s\}$,  $x^k=[X_1^k,\ldots,X_L^k]$   is  a
+solution of the  global linear system.%The advantage such a method is that the Krylov subspace does not need to be spanned by an orthogonal basis.
+The advantage  of such a  Krylov subspace is  that we need  neither an
+orthogonal basis  nor synchronizations between  the different 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 the vectors of $S$.
+So, $\alpha$ is defined as the solution of the large overdetermined linear system
+\begin{equation}
+B\alpha=b,
+\label{sec03:eq05}
+\end{equation}
+where $B=AS$ is a dense rectangular matrix of size $n\times s$ and $s\ll n$. This leads
+us to solve the system of normal equations 
+\begin{equation}
+B^TB\alpha=B^Tb,
+\label{sec03:eq06}
+\end{equation}
+which is associated with the least squares problem
+\begin{equation}
+\text{minimize}~\|b-B\alpha\|_2,
+\label{sec03:eq07}
+\end{equation}  
+where $B^T$ denotes the transpose of the matrix $B$. Since $B$ (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 for solving this system. We use the parallel conjugate gradient 
+method for the normal equations CGNR~\cite{S96,refCGNR}.
+
+%%% Ecrire l'algorithme(s)
+%%% Parler des synchronisations entre proc et clusters 
+