X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/Krylov_multi.git/blobdiff_plain/d9105f3cc518086be070a250845823a6ec0f7b4a..09702354d347f9bf651fba24d04f262c757e2cc5:/krylov_multi.tex diff --git a/krylov_multi.tex b/krylov_multi.tex index 0ff9175..7a7e809 100644 --- a/krylov_multi.tex +++ b/krylov_multi.tex @@ -14,6 +14,7 @@ \title{A scalable multisplitting algorithm for solving large sparse linear systems} +\date{} @@ -28,7 +29,7 @@ \begin{abstract} -In this paper we revist the krylov multisplitting algorithm presented in +In this paper we revisit the krylov multisplitting algorithm presented in \cite{huang1993krylov} which uses a scalar method to minimize the krylov iterations computed by a multisplitting algorithm. Our new algorithm is based on a parallel multisplitting algorithm with few blocks of large size using a @@ -206,9 +207,9 @@ 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 parallel GMRES method~\cite{ref34} -as an inner iteration method for solving the +as an inner iteration method to solve the sub-systems~(\ref{sec03:eq03}). It is a well-known iterative method -which gives good performances for solving sparse linear systems in +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 @@ -234,7 +235,10 @@ 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 the different clusters to generate this basis. +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 @@ -256,12 +260,12 @@ which is associated with the least squares problem \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 for solving this system. We use the parallel conjugate -gradient method for the normal equations CGNR~\cite{S96,refCGNR}. +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} @@ -301,16 +305,16 @@ gradient method for the normal equations CGNR~\cite{S96,refCGNR}. \label{algo:01} \end{algorithm} -The main key points of the multisplitting method for solving large -sparse linear systems are given in Algorithm~\ref{algo:01}. This +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 for solving each splitting on a +iterative algorithms: the GMRES method to solve each splitting on a cluster of processors, and the CGNR method executed in parallel by all -clusters for minimizing the function error over the Krylov subspace +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 @@ -324,6 +328,36 @@ synchronizations by using the MPI collective communication subroutines. +\section{Experiments} + +In order to illustrate the interest of our algorithm. We have compared our +algorithm with the GMRES method which a very well 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. + +\section{Conclusion and perspectives} + +Other applications (=> other matrices)\\ +Larger experiments\\ +Async\\ +Overlapping %%%%%%%%%%%%%%%%%%%%%%%%