X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/Krylov_multi.git/blobdiff_plain/0d162d0a192a8e0e159e831ba451862a05260ee5..a5cc060a0ba3e9cee743f3c3c6e836a769deeaee:/krylov_multi.tex?ds=inline diff --git a/krylov_multi.tex b/krylov_multi.tex index ea6cfe9..dca87e4 100644 --- a/krylov_multi.tex +++ b/krylov_multi.tex @@ -5,6 +5,7 @@ \usepackage{graphicx} \usepackage{algorithm} \usepackage{algpseudocode} +\usepackage{multirow} \algnewcommand\algorithmicinput{\textbf{Input:}} \algnewcommand\Input{\item[\algorithmicinput]} @@ -12,8 +13,12 @@ \algnewcommand\algorithmicoutput{\textbf{Output:}} \algnewcommand\Output{\item[\algorithmicoutput]} +\newcommand{\Time}[1]{\mathit{Time}_\mathit{#1}} +\newcommand{\Prec}{\mathit{prec}} +\newcommand{\Ratio}{\mathit{Ratio}} \title{A scalable multisplitting algorithm for solving large sparse linear systems} +\date{} @@ -28,7 +33,7 @@ \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 @@ -61,8 +66,13 @@ 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. @@ -201,9 +211,9 @@ 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 @@ -229,7 +239,10 @@ 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 @@ -251,12 +264,12 @@ 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} @@ -296,16 +309,16 @@ 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 @@ -319,6 +332,101 @@ 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. + +Doing many experiments with many cores is not easy and require to access to a +supercomputer with several hours for developping 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. + +In the experiments we report the size of the 3D poisson considered + + +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 +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 +multisplitting code, the total number of inner iterations is represented in +parenthesis. + + We also give the other parameters: the restart for the GRMES method.... + +\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) & 433.1 & 55,494 & 4.92e-7 & 74.1 & 1,101(8,211) & 6.62e-08 & 5.84 \\ +\hline +$743^3$ & 8192 (2x4096) & 704.4 & 87,822 & 4.80e-07 & 151.2 & 3,061(14,914) & 5.87e-08 & 4.65 \\ +\hline +$743^3$ & 8192 (4x2048) & 704.4 & 87,822 & 4.80e-07 & 110.3 & 1,531(12,721) & 1.47e-07& 6.39 \\ +\hline + +\end{tabular} +\caption{Results without preconditioner} +\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} + +\section{Conclusion and perspectives} + +Other applications (=> other matrices)\\ +Larger experiments\\ +Async\\ +Overlapping %%%%%%%%%%%%%%%%%%%%%%%%