X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/Krylov_multi.git/blobdiff_plain/71d655a4cb613d91906ca8c723f61a78cae26b0e..8dec794b01b4893a1ccc93250b6108a852feb2f7:/krylov_multi.tex diff --git a/krylov_multi.tex b/krylov_multi.tex index 1b8b43e..61c1e28 100644 --- a/krylov_multi.tex +++ b/krylov_multi.tex @@ -3,8 +3,18 @@ \usepackage{amsfonts,amssymb} \usepackage{amsmath} \usepackage{graphicx} +\usepackage{algorithm} +\usepackage{algpseudocode} + +\algnewcommand\algorithmicinput{\textbf{Input:}} +\algnewcommand\Input{\item[\algorithmicinput]} + +\algnewcommand\algorithmicoutput{\textbf{Output:}} +\algnewcommand\Output{\item[\algorithmicoutput]} + \title{A scalable multisplitting algorithm for solving large sparse linear systems} +\date{} @@ -52,8 +62,13 @@ 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...\\ +Traditionnal 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. @@ -191,10 +206,11 @@ Y_l = B_l - \displaystyle\sum_{i=1,i\neq l}^{L}A_{li}X_i, 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. +clusters. In this work, we use the parallel GMRES method~\cite{ref34} +as an inner iteration method to solve the +sub-systems~(\ref{sec03:eq03}). It is a well-known iterative method +which gives good performances to solve 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 @@ -218,39 +234,98 @@ splittings~(\ref{sec03:eq03}) 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 +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 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, +R\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 +where $R=AS$ 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, +R^TR\alpha=R^Tb, \label{sec03:eq06} \end{equation} which is associated with the least squares problem \begin{equation} -\text{minimize}~\|b-B\alpha\|_2, +\text{minimize}~\|b-R\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 +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}. + +\begin{algorithm}[!t] +\caption{A two-stage linear solver with inner iteration GMRES method} +\begin{algorithmic}[1] +\Input $A_l$ (local sparse matrix), $B_l$ (local right-hand side), $x^0$ (initial guess) +\Output $X_l$ (local solution vector)\vspace{0.2cm} +\State Load $A_l$, $B_l$, $x^0$ +\State Initialize the minimizer $\tilde{x}^0=x^0$ +\For {$k=1,2,3,\ldots$ until the global convergence} +\State Restart with $x^0=\tilde{x}^{k-1}$: \textbf{for} $j=1,2,\ldots,s$ \textbf{do} +\State\hspace{0.5cm} Inner iteration solver: \Call{InnerSolver}{$x^0$, $j$} +\State\hspace{0.5cm} Construct the basis $S$: add the column vector $X_l^j$ to the matrix $S_l^k$ +\State\hspace{0.5cm} Exchange the local solution vector $X_l^j$ with the neighboring clusters +\State\hspace{0.5cm} Compute the dense matrix $R$: $R_l^{k,j}=\sum^L_{i=1}A_{li}X_i^j$ +\State\textbf{end for} +\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 +\EndFor + +\Statex + +\Function {InnerSolver}{$x^0$, $j$} +\State Compute the local right-hand side: $Y_l = B_l - \sum^L_{i=1,i\neq l}A_{li}X_i^0$ +\State Solving the local splitting $A_{ll}X_l^j=Y_l$ using the parallel GMRES method, such that $X_l^0$ is the initial guess +\State \Return $X_l^j$ +\EndFunction + +\Statex + +\Function {UpdateMinimizer}{$S_l$, $R$, $b$, $k$} +\State Solving the normal equations $(R^k)^TR^k\alpha^k=(R^k)^Tb$ in parallel by $L$ clusters using the parallel CGNR method +\State Compute the local minimizer: $\tilde{X}_l^k=S_l^k\alpha^k$ +\State \Return $\tilde{X}_l^k$ +\EndFunction +\end{algorithmic} +\label{algo:01} +\end{algorithm} + +The main key points of the 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 the +GMRES iterative method as an inner solver. It is executed in parallel +by each cluster of processors. The matrices and vectors with the +subscript $l$ represent the local data for the cluster $l$, where +$l\in\{1,\ldots,L\}$. The two-stage solver uses two different parallel +iterative algorithms: the GMRES method to solve each splitting on a +cluster of processors, and the CGNR method executed in parallel by all +clusters to minimize the function error over the Krylov subspace +spanned by $S$. The algorithm requires two global synchronizations +between the $L$ clusters. The first one is performed at line~$12$ in +Algorithm~\ref{algo:01} to exchange the local values of the 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 choose 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 +matrix-matrix multiplication $R=AS$. We implement all +synchronizations by using the MPI collective communication +subroutines.