X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/GMRES2stage.git/blobdiff_plain/299e2e52d19b38d91f622d1eb8b7af2bb44c7685..cce18db44812b9a67dd4d30ca37ea74fd46f16b1:/paper.tex diff --git a/paper.tex b/paper.tex index 4757973..cf61a9e 100644 --- a/paper.tex +++ b/paper.tex @@ -369,7 +369,7 @@ % % paper title % can use linebreaks \\ within to get better formatting as desired -\title{TSIRM: A Two-Stage Iteration with least-square Residual Minimization algorithm to solve large sparse linear systems} +\title{TSIRM: A Two-Stage Iteration with least-squares Residual Minimization algorithm to solve large sparse linear systems} @@ -380,8 +380,8 @@ % use a multiple column layout for up to two different % affiliations -\author{\IEEEauthorblockN{Rapha\"el Couturier\IEEEauthorrefmark{1}, Lilia Ziane Khodja \IEEEauthorrefmark{2}, and Christophe Guyeux\IEEEauthorrefmark{1}} -\IEEEauthorblockA{\IEEEauthorrefmark{1} Femto-ST Institute, University of Franche Comte, France\\ +\author{\IEEEauthorblockN{Rapha\"el Couturier\IEEEauthorrefmark{1}, Lilia Ziane Khodja\IEEEauthorrefmark{2}, and Christophe Guyeux\IEEEauthorrefmark{1}} +\IEEEauthorblockA{\IEEEauthorrefmark{1} Femto-ST Institute, University of Franche-Comt\'e, France\\ Email: \{raphael.couturier,christophe.guyeux\}@univ-fcomte.fr} \IEEEauthorblockA{\IEEEauthorrefmark{2} INRIA Bordeaux Sud-Ouest, France\\ Email: lilia.ziane@inria.fr} @@ -425,20 +425,21 @@ Email: lilia.ziane@inria.fr} \begin{abstract} -In this article, a two-stage iterative algorithm is proposed to improve the -convergence of Krylov based iterative methods, typically those of GMRES variants. The -principle of the proposed approach is to build an external iteration over the Krylov -method, and to frequently store its current residual (at each -GMRES restart for instance). After a given number of outer iterations, a minimization -step is applied on the matrix composed by the saved residuals, in order to -compute a better solution and to make new iterations if required. It is proven that -the proposal has the same convergence properties than the inner embedded method itself. -Experiments using up to 16,394 cores also show that the proposed algorithm -runs around 5 or 7 times faster than GMRES. +In this article, a two-stage iterative algorithm is proposed to improve the +convergence of Krylov based iterative methods, typically those of GMRES +variants. The principle of the proposed approach is to build an external +iteration over the Krylov method, and to frequently store its current residual +(at each GMRES restart for instance). After a given number of outer iterations, +a least-squares minimization step is applied on the matrix composed by the saved +residuals, in order to compute a better solution and to make new iterations if +required. It is proven that the proposal has the same convergence properties +than the inner embedded method itself. Experiments using up to 16,394 cores +also show that the proposed algorithm runs around 5 or 7 times faster than +GMRES. \end{abstract} \begin{IEEEkeywords} -Iterative Krylov methods; sparse linear systems; residual minimization; PETSc; %à voir... +Iterative Krylov methods; sparse linear systems; two stage iteration; least-squares residual minimization; PETSc \end{IEEEkeywords} @@ -546,42 +547,46 @@ Iterative Krylov methods; sparse linear systems; residual minimization; PETSc; % % You must have at least 2 lines in the paragraph with the drop letter % (should never be an issue) -Iterative methods have recently become more attractive than direct ones to solve very large -sparse linear systems. They are more efficient in a parallel -context, supporting thousands of cores, and they require less memory and arithmetic -operations than direct methods. This is why new iterative methods are frequently -proposed or adapted by researchers, and the increasing need to solve very large sparse -linear systems has triggered the development of such efficient iterative techniques -suitable for parallel processing. - -Most of the successful iterative methods currently available are based on so-called ``Krylov -subspaces''. They consist in forming a basis of successive matrix -powers multiplied by an initial vector, which can be for instance the residual. These methods use vectors orthogonality of the Krylov subspace basis in order to solve linear -systems. The most known iterative Krylov subspace methods are conjugate -gradient and GMRES ones (Generalized Minimal RESidual). - - -However, iterative methods suffer from scalability problems on parallel -computing platforms with many processors, due to their need of reduction -operations, and to collective communications to achive matrix-vector +Iterative methods have recently become more attractive than direct ones to solve +very large sparse linear systems\cite{Saad2003}. They are more efficient in a +parallel context, supporting thousands of cores, and they require less memory +and arithmetic operations than direct methods~\cite{bahicontascoutu}. This is +why new iterative methods are frequently proposed or adapted by researchers, and +the increasing need to solve very large sparse linear systems has triggered the +development of such efficient iterative techniques suitable for parallel +processing. + +Most of the successful iterative methods currently available are based on +so-called ``Krylov subspaces''. They consist in forming a basis of successive +matrix powers multiplied by an initial vector, which can be for instance the +residual. These methods use vectors orthogonality of the Krylov subspace basis +in order to solve linear systems. The most known iterative Krylov subspace +methods are conjugate gradient and GMRES ones (Generalized Minimal RESidual). + + +However, iterative methods suffer from scalability problems on parallel +computing platforms with many processors, due to their need of reduction +operations, and to collective communications to achieve matrix-vector multiplications. The communications on large clusters with thousands of cores -and large sizes of messages can significantly affect the performances of these -iterative methods. As a consequence, Krylov subspace iteration methods are often used -with preconditioners in practice, to increase their convergence and accelerate their -performances. However, most of the good preconditioners are not scalable on -large clusters. - -In this research work, a two-stage algorithm based on two nested iterations -called inner-outer iterations is proposed. This algorithm consists in solving the sparse -linear system iteratively with a small number of inner iterations, and restarting -the outer step with a new solution minimizing some error functions over some -previous residuals. This algorithm is iterative and easy to parallelize on large -clusters. Furthermore, the minimization technique improves its convergence and -performances. +and large sizes of messages can significantly affect the performances of these +iterative methods. As a consequence, Krylov subspace iteration methods are often +used with preconditioners in practice, to increase their convergence and +accelerate their performances. However, most of the good preconditioners are +not scalable on large clusters. + +In this research work, a two-stage algorithm based on two nested iterations +called inner-outer iterations is proposed. This algorithm consists in solving +the sparse linear system iteratively with a small number of inner iterations, +and restarting the outer step with a new solution minimizing some error +functions over some previous residuals. For further information on two-stage +iteration methods, interested readers are invited to +consult~\cite{Nichols:1973:CTS}. Two-stage algorithms are easy to parallelize on +large clusters. Furthermore, the least-squares minimization technique improves +its convergence and performances. The present article is organized as follows. Related works are presented in Section~\ref{sec:02}. Section~\ref{sec:03} details the two-stage algorithm using -a least-square residual minimization, while Section~\ref{sec:04} provides +a least-squares residual minimization, while Section~\ref{sec:04} provides convergence results regarding this method. Section~\ref{sec:05} shows some experimental results obtained on large clusters using routines of PETSc toolkit. This research work ends by a conclusion section, in which the proposal @@ -596,7 +601,13 @@ is summarized while intended perspectives are provided. %%%********************************************************* \section{Related works} \label{sec:02} -%Wherever Times is specified, Times Roman or Times New Roman may be used. If neither is available on your system, please use the font closest in appearance to Times. Avoid using bit-mapped fonts if possible. True-Type 1 or Open Type fonts are preferred. Please embed symbol fonts, as well, for math, etc. +%GMRES method is one of the most widely used iterative solvers chosen to deal with the sparsity and the large order of linear systems. It was initially developed by Saad \& al.~\cite{Saad86} to deal with non-symmetric and non-Hermitian problems, and indefinite symmetric problems too. The convergence of the restarted GMRES with preconditioning is faster and more stable than those of some other iterative solvers. + +%The next two chapters explore a few methods which are considered currently to be among the most important iterative techniques available for solving large linear systems. These techniques are based on projection processes, both orthogonal and oblique, onto Krylov subspaces, which are subspaces spanned by vectors of the form p(A)v where p is a polynomial. In short, these techniques approximate A −1 b by p(A)b, where p is a “good” polynomial. This chapter covers methods derived from, or related to, the Arnoldi orthogonalization. The next chapter covers methods based on Lanczos biorthogonalization. + +%Krylov subspace techniques have inceasingly been viewed as general purpose iterative methods, especially since the popularization of the preconditioning techniqes. + +%Preconditioned Krylov-subspace iterations are a key ingredient in many modern linear solvers, including in solvers that employ support preconditioners. %%%********************************************************* %%%********************************************************* @@ -604,7 +615,7 @@ is summarized while intended perspectives are provided. %%%********************************************************* %%%********************************************************* -\section{Two-stage iteration with least-square residuals minimization algorithm} +\section{Two-stage iteration with least-squares residuals minimization algorithm} \label{sec:03} A two-stage algorithm is proposed to solve large sparse linear systems of the form $Ax=b$, where $A\in\mathbb{R}^{n\times n}$ is a sparse and square @@ -615,28 +626,29 @@ inner-outer iteration solver based on iterative Krylov methods. The main key-points of the proposed solver are given in Algorithm~\ref{algo:01}. It can be summarized as follows: the inner solver is a Krylov based one. In order to accelerate its convergence, the -outer solver periodically applies a least-square minimization on the residuals computed by the inner one. %Tsolver which does not required to be changed. - -At each outer iteration, the sparse linear system $Ax=b$ is partially -solved using only $m$ -iterations of an iterative method, this latter being initialized with the -best known approximation previously obtained. -GMRES method~\cite{Saad86}, or any of its variants, can be used for instance as an -inner solver. The current approximation of the Krylov method is then stored inside a matrix -$S$ composed by the successive solutions that are computed during inner iterations. - -At each $s$ iterations, the minimization step is applied in order to -compute a new solution $x$. For that, the previous residuals are computed with -$(b-AS)$. The minimization of the residuals is obtained by +outer solver periodically applies a least-squares minimization on the residuals computed by the inner one. %Tsolver which does not required to be changed. + +At each outer iteration, the sparse linear system $Ax=b$ is partially solved +using only $m$ iterations of an iterative method, this latter being initialized +with the last obtained approximation. GMRES method~\cite{Saad86}, or any of its +variants, can potentially be used as inner solver. The current approximation of +the Krylov method is then stored inside a $n \times s$ matrix $S$, which is +composed by the $s$ last solutions that have been computed during the inner +iterations phase. In the remainder, the $i$-th column vector of $S$ will be +denoted by $S_i$. + +At each $s$ iterations, another kind of minimization step is applied in order to +compute a new solution $x$. For that, the previous residuals of $Ax=b$ are computed by +the inner iterations with $(b-AS)$. The minimization of the residuals is obtained by \begin{equation} \underset{\alpha\in\mathbb{R}^{s}}{min}\|b-R\alpha\|_2 \label{eq:01} \end{equation} -with $R=AS$. Then the new solution $x$ is computed with $x=S\alpha$. +with $R=AS$. The new solution $x$ is then computed with $x=S\alpha$. In practice, $R$ is a dense rectangular matrix belonging in $\mathbb{R}^{n\times s}$, -with $s\ll n$. In order to minimize~\eqref{eq:01}, a least-square method such as +with $s\ll n$. In order to minimize~\eqref{eq:01}, a least-squares method such as CGLS ~\cite{Hestenes52} or LSQR~\cite{Paige82} is used. Remark that these methods are more appropriate than a single direct method in a parallel context. @@ -647,52 +659,55 @@ appropriate than a single direct method in a parallel context. \begin{algorithmic}[1] \Input $A$ (sparse matrix), $b$ (right-hand side) \Output $x$ (solution vector)\vspace{0.2cm} - \State Set the initial guess $x^0$ - \For {$k=1,2,3,\ldots$ until convergence (error$<\epsilon_{tsirm}$)} \label{algo:conv} - \State $x^k=Solve(A,b,x^{k-1},max\_iter_{kryl})$ \label{algo:solve} - \State retrieve error - \State $S_{k \mod s}=x^k$ \label{algo:store} - \If {$k \mod s=0$ {\bf and} error$>\epsilon_{kryl}$} + \State Set the initial guess $x_0$ + \For {$k=1,2,3,\ldots$ until convergence ($error<\epsilon_{tsirm}$)} \label{algo:conv} + \State $[x_k,error]=Solve(A,b,x_{k-1},max\_iter_{kryl})$ \label{algo:solve} + \State $S_{k \mod s}=x_k$ \label{algo:store} \Comment{update column ($k \mod s$) of $S$} + \If {$k \mod s=0$ {\bf and} $error>\epsilon_{kryl}$} \State $R=AS$ \Comment{compute dense matrix} \label{algo:matrix_mul} - \State Solve least-square problem $\underset{\alpha\in\mathbb{R}^{s}}{min}\|b-R\alpha\|_2$ \label{algo:} - \State $x^k=S\alpha$ \Comment{compute new solution} + \State $\alpha=Least\_Squares(R,b,max\_iter_{ls})$ \label{algo:} + \State $x_k=S\alpha$ \Comment{compute new solution} \EndIf \EndFor \end{algorithmic} \label{algo:01} \end{algorithm} -Algorithm~\ref{algo:01} summarizes the principle of our method. The outer -iteration is inside the for loop. Line~\ref{algo:solve}, the Krylov method is -called for a maximum of $max\_iter_{kryl}$ iterations. In practice, we suggest to set this parameter -equals to the restart number of the GMRES-like method. Moreover, a tolerance -threshold must be specified for the solver. In practice, this threshold must be -much smaller than the convergence threshold of the TSIRM algorithm (\emph{i.e.} -$\epsilon_{tsirm}$). Line~\ref{algo:store}, $S_{k~ mod~ s}=x^k$ consists in copying the -solution $x_k$ into the column $k~ mod~ s$ of the matrix $S$. After the -minimization, the matrix $S$ is reused with the new values of the residuals. To -solve the minimization problem, an iterative method is used. Two parameters are -required for that: the maximum number of iterations and the threshold to stop the -method. +Algorithm~\ref{algo:01} summarizes the principle of the proposed method. The +outer iteration is inside the \emph{for} loop. Line~\ref{algo:solve}, the Krylov +method is called for a maximum of $max\_iter_{kryl}$ iterations. In practice, +we suggest to set this parameter equal to the restart number in the GMRES-like +method. Moreover, a tolerance threshold must be specified for the solver. In +practice, this threshold must be much smaller than the convergence threshold of +the TSIRM algorithm (\emph{i.e.}, $\epsilon_{tsirm}$). We also consider that +after the call of the $Solve$ function, we obtain the vector $x_k$ and the error +which is defined by $||Ax_k-b||_2$. + + Line~\ref{algo:store}, +$S_{k \mod s}=x_k$ consists in copying the solution $x_k$ into the column $k +\mod s$ of $S$. After the minimization, the matrix $S$ is reused with the new +values of the residuals. To solve the minimization problem, an iterative method +is used. Two parameters are required for that: the maximum number of iterations +and the threshold to stop the method. Let us summarize the most important parameters of TSIRM: \begin{itemize} \item $\epsilon_{tsirm}$: the threshold to stop the TSIRM method; \item $max\_iter_{kryl}$: the maximum number of iterations for the Krylov method; \item $s$: the number of outer iterations before applying the minimization step; -\item $max\_iter_{ls}$: the maximum number of iterations for the iterative least-square method; -\item $\epsilon_{ls}$: the threshold used to stop the least-square method. +\item $max\_iter_{ls}$: the maximum number of iterations for the iterative least-squares method; +\item $\epsilon_{ls}$: the threshold used to stop the least-squares method. \end{itemize} -The parallelisation of TSIRM relies on the parallelization of all its -parts. More precisely, except the least-square step, all the other parts are +The parallelization of TSIRM relies on the parallelization of all its +parts. More precisely, except the least-squares step, all the other parts are obvious to achieve out in parallel. In order to develop a parallel version of our code, we have chosen to use PETSc~\cite{petsc-web-page}. For line~\ref{algo:matrix_mul} the matrix-matrix multiplication is implemented and efficient since the matrix $A$ is sparse and since the matrix $S$ contains few -colums in practice. As explained previously, at least two methods seem to be -interesting to solve the least-square minimization, CGLS and LSQR. +columns in practice. As explained previously, at least two methods seem to be +interesting to solve the least-squares minimization, CGLS and LSQR. In the following we remind the CGLS algorithm. The LSQR method follows more or less the same principle but it takes more place, so we briefly explain the parallelization of CGLS which is similar to LSQR. @@ -702,19 +717,21 @@ less the same principle but it takes more place, so we briefly explain the paral \begin{algorithmic}[1] \Input $A$ (matrix), $b$ (right-hand side) \Output $x$ (solution vector)\vspace{0.2cm} - \State $r=b-Ax$ - \State $p=A'r$ - \State $s=p$ - \State $g=||s||^2_2$ - \For {$k=1,2,3,\ldots$ until convergence (g$<\epsilon_{ls}$)} \label{algo2:conv} - \State $q=Ap$ - \State $\alpha=g/||q||^2_2$ - \State $x=x+alpha*p$ - \State $r=r-alpha*q$ - \State $s=A'*r$ - \State $g_{old}=g$ - \State $g=||s||^2_2$ - \State $\beta=g/g_{old}$ + \State Let $x_0$ be an initial approximation + \State $r_0=b-Ax_0$ + \State $p_1=A^Tr_0$ + \State $s_0=p_1$ + \State $\gamma=||s_0||^2_2$ + \For {$k=1,2,3,\ldots$ until convergence ($\gamma<\epsilon_{ls}$)} \label{algo2:conv} + \State $q_k=Ap_k$ + \State $\alpha_k=\gamma/||q_k||^2_2$ + \State $x_k=x_{k-1}+\alpha_kp_k$ + \State $r_k=r_{k-1}-\alpha_kq_k$ + \State $s_k=A^Tr_k$ + \State $\gamma_{old}=\gamma$ + \State $\gamma=||s_k||^2_2$ + \State $\beta_k=\gamma/\gamma_{old}$ + \State $p_{k+1}=s_k+\beta_kp_k$ \EndFor \end{algorithmic} \label{algo:02} @@ -732,17 +749,79 @@ these operations are easy to implement in PETSc or similar environment. \section{Convergence results} \label{sec:04} -Let us recall the following result, see~\cite{Saad86}. + + +We can now claim that, \begin{proposition} -Suppose that $A$ is a positive real matrix with symmetric part $M$. Then the residual norm provided at the $m$-th step of GMRES satisfies: +\label{prop:saad} +If $A$ is either a definite positive or a positive matrix and GMRES($m$) is used as solver, then the TSIRM algorithm is convergent. + +Furthermore, let $r_k$ be the +$k$-th residue of TSIRM, then +we have the following boundaries: +\begin{itemize} +\item when $A$ is positive: \begin{equation} -||r_m|| \leqslant \left(1-\dfrac{\alpha}{\beta}\right)^{\frac{m}{2}} ||r_0|| , +||r_k|| \leqslant \left(1-\dfrac{\alpha}{\beta}\right)^{\frac{km}{2}} ||r_0|| , \end{equation} -where $\alpha = \lambda_min(M)^2$ and $\beta = \lambda_max(A^T A)$, which proves -the convergence of GMRES($m$) for all $m$ under that assumption regarding $A$. +where $M$ is the symmetric part of $A$, $\alpha = \lambda_{min}(M)^2$ and $\beta = \lambda_{max}(A^T A)$; +\item when $A$ is positive definite: +\begin{equation} +\|r_k\| \leq \left( 1-\frac{\lambda_{\mathrm{min}}^2(1/2(A^T + A))}{ \lambda_{\mathrm{max}}(A^T A)} \right)^{km/2} \|r_0\|. +\end{equation} +\end{itemize} +%In the general case, where A is not positive definite, we have +%$\|r_n\| \le \inf_{p \in P_n} \|p(A)\| \le \kappa_2(V) \inf_{p \in P_n} \max_{\lambda \in \sigma(A)} |p(\lambda)| \|r_0\|, .$ \end{proposition} +\begin{proof} +Let us first recall that the residue is under control when considering the GMRES algorithm on a positive definite matrix, and it is bounded as follows: +\begin{equation*} +\|r_k\| \leq \left( 1-\frac{\lambda_{\mathrm{min}}^2(1/2(A^T + A))}{ \lambda_{\mathrm{max}}(A^T A)} \right)^{k/2} \|r_0\| . +\end{equation*} +Additionally, when $A$ is a positive real matrix with symmetric part $M$, then the residual norm provided at the $m$-th step of GMRES satisfies: +\begin{equation*} +||r_m|| \leqslant \left(1-\dfrac{\alpha}{\beta}\right)^{\frac{m}{2}} ||r_0|| , +\end{equation*} +where $\alpha$ and $\beta$ are defined as in Proposition~\ref{prop:saad}, which proves +the convergence of GMRES($m$) for all $m$ under such assumptions regarding $A$. +These well-known results can be found, \emph{e.g.}, in~\cite{Saad86}. + +We will now prove by a mathematical induction that, for each $k \in \mathbb{N}^\ast$, +$||r_k|| \leqslant \left(1-\dfrac{\alpha}{\beta}\right)^{\frac{mk}{2}} ||r_0||$ when $A$ is positive, and $\|r_k\| \leq \left( 1-\frac{\lambda_{\mathrm{min}}^2(1/2(A^T + A))}{ \lambda_{\mathrm{max}}(A^T A)} \right)^{km/2} \|r_0\|$ when $A$ is positive definite. +The base case is obvious, as for $k=1$, the TSIRM algorithm simply consists in applying GMRES($m$) once, leading to a new residual $r_1$ that follows the inductive hypothesis due, to the results recalled above. + +Suppose now that the claim holds for all $m=1, 2, \hdots, k-1$, that is, $\forall m \in \{1,2,\hdots, k-1\}$, $||r_m|| \leqslant \left(1-\dfrac{\alpha}{\beta}\right)^{\frac{km}{2}} ||r_0||$ in the positive case, and $\|r_k\| \leq \left( 1-\frac{\lambda_{\mathrm{min}}^2(1/2(A^T + A))}{ \lambda_{\mathrm{max}}(A^T A)} \right)^{km/2} \|r_0\|$ in the definite positive one. +We will show that the statement holds too for $r_k$. Two situations can occur: +\begin{itemize} +\item If $k \not\equiv 0 ~(\textrm{mod}\ m)$, then the TSIRM algorithm consists in executing GMRES once. In that case and by using the inductive hypothesis, we obtain either $||r_k|| \leqslant \left(1-\dfrac{\alpha}{\beta}\right)^{\frac{m}{2}} ||r_{k-1}||\leqslant \left(1-\dfrac{\alpha}{\beta}\right)^{\frac{km}{2}} ||r_0||$ if $A$ is positive, or $\|r_k\| \leqslant \left( 1-\frac{\lambda_{\mathrm{min}}^2(1/2(A^T + A))}{ \lambda_{\mathrm{max}}(A^T A)} \right)^{m/2} \|r_{k-1}\|$ $\leqslant$ $\left( 1-\frac{\lambda_{\mathrm{min}}^2(1/2(A^T + A))}{ \lambda_{\mathrm{max}}(A^T A)} \right)^{km/2} \|r_{0}\|$ in the positive definite case. +\item Else, the TSIRM algorithm consists in two stages: a first GMRES($m$) execution leads to a temporary $x_k$ whose residue satisfies: +\begin{itemize} +\item $||r_k|| \leqslant \left(1-\dfrac{\alpha}{\beta}\right)^{\frac{m}{2}} ||r_{k-1}||\leqslant \left(1-\dfrac{\alpha}{\beta}\right)^{\frac{km}{2}} ||r_0||$ in the positive case, +\item $\|r_k\| \leqslant \left( 1-\frac{\lambda_{\mathrm{min}}^2(1/2(A^T + A))}{ \lambda_{\mathrm{max}}(A^T A)} \right)^{m/2} \|r_{k-1}\|$ $\leqslant$ $\left( 1-\frac{\lambda_{\mathrm{min}}^2(1/2(A^T + A))}{ \lambda_{\mathrm{max}}(A^T A)} \right)^{km/2} \|r_{0}\|$ in the positive definite one, +\end{itemize} +and a least squares resolution. +Let $\operatorname{span}(S) = \left \{ {\sum_{i=1}^k \lambda_i v_i \Big| k \in \mathbb{N}, v_i \in S, \lambda _i \in \mathbb{R}} \right \}$ be the linear span of a set of real vectors $S$. So,\\ +$\min_{\alpha \in \mathbb{R}^s} ||b-R\alpha ||_2 = \min_{\alpha \in \mathbb{R}^s} ||b-AS\alpha ||_2$ + +$\begin{array}{ll} +& = \min_{x \in span\left(S_{k-s+1}, S_{k-s+2}, \hdots, S_{k} \right)} ||b-AS\alpha ||_2\\ +& = \min_{x \in span\left(x_{k-s+1}, x_{k-s}+2, \hdots, x_{k} \right)} ||b-AS\alpha ||_2\\ +& \leqslant \min_{x \in span\left( x_{k} \right)} ||b-Ax ||_2\\ +& \leqslant \min_{\lambda \in \mathbb{R}} ||b-\lambda Ax_{k} ||_2\\ +& \leqslant ||b-Ax_{k}||_2\\ +& = ||r_k||_2\\ +& \leqslant \left(1-\dfrac{\alpha}{\beta}\right)^{\frac{km}{2}} ||r_0||, \textrm{ if $A$ is positive,}\\ +& \leqslant \left( 1-\frac{\lambda_{\mathrm{min}}^2(1/2(A^T + A))}{ \lambda_{\mathrm{max}}(A^T A)} \right)^{km/2} \|r_{0}\|, \textrm{ if $A$ is}\\ +& \textrm{positive definite,} +\end{array}$ +\end{itemize} +which concludes the induction and the proof. +\end{proof} + +%We can remark that, at each iterate, the residue of the TSIRM algorithm is lower +%than the one of the GMRES method. %%%********************************************************* %%%********************************************************* @@ -750,11 +829,13 @@ the convergence of GMRES($m$) for all $m$ under that assumption regarding $A$. \label{sec:05} -In order to see the influence of our algorithm with only one processor, we first -show a comparison with the standard version of GMRES and our algorithm. In -Table~\ref{tab:01}, we show the matrices we have used and some of them -characteristics. For all the matrices, the name, the field, the number of rows -and the number of nonzero elements are given. +In order to see the behavior of the proposal when considering only one processor, a first +comparison with GMRES or FGMRES and the new algorithm detailed previously has been experimented. +Matrices that have been used with their characteristics (names, fields, rows, and nonzero coefficients) are detailed in +Table~\ref{tab:01}. These latter, which are real-world applications matrices, +have been extracted + from the Davis collection, University of +Florida~\cite{Dav97}. \begin{table}[htbp] \begin{center} @@ -774,8 +855,9 @@ torso3 & 2D/3D problem & 259,156 & 4,429,042 \\ \label{tab:01} \end{center} \end{table} - -The following parameters have been chosen for our experiments. As by default +Chosen parameters are detailed below. +%The following parameters have been chosen for our experiments. +As by default the restart of GMRES is performed every 30 iterations, we have chosen to stop the GMRES every 30 iterations (\emph{i.e.} $max\_iter_{kryl}=30$). $s$ is set to 8. CGLS is chosen to minimize the least-squares problem with the following parameters: @@ -787,13 +869,14 @@ Core(TM) i7-3630QM CPU @ 2.40GHz with the version 3.5.1 of PETSc. In Table~\ref{tab:02}, some experiments comparing the solving of the linear systems obtained with the previous matrices with a GMRES variant and with out 2 stage algorithm are given. In the second column, it can be noticed that either -gmres or fgmres is used to solve the linear system. According to the matrices, -different preconditioner is used. With TSIRM, the same solver and the same -preconditionner is used. This Table shows that TSIRM can drastically reduce the -number of iterations to reach the convergence when the number of iterations for -the normal GMRES is more or less greater than 500. In fact this also depends on -tow parameters: the number of iterations to stop GMRES and the number of -iterations to perform the minimization. +GRMES or FGMRES (Flexible GMRES)~\cite{Saad:1993} is used to solve the linear +system. According to the matrices, different preconditioner is used. With +TSIRM, the same solver and the same preconditionner are used. This Table shows +that TSIRM can drastically reduce the number of iterations to reach the +convergence when the number of iterations for the normal GMRES is more or less +greater than 500. In fact this also depends on tow parameters: the number of +iterations to stop GMRES and the number of iterations to perform the +minimization. \begin{table}[htbp] @@ -814,7 +897,7 @@ torso3 & fgmres / sor & 37.70 & 565 & 34.97 & 510 \\ \hline \end{tabular} -\caption{Comparison of (F)GMRES and 2 stage (F)GMRES algorithms in sequential with some matrices, time is expressed in seconds.} +\caption{Comparison of (F)GMRES and TSIRM with (F)GMRES in sequential with some matrices, time is expressed in seconds.} \label{tab:02} \end{center} \end{table} @@ -823,29 +906,48 @@ torso3 & fgmres / sor & 37.70 & 565 & 34.97 & 510 \\ -In order to perform larger experiments, we have tested some example application +In order to perform larger experiments, we have tested some example applications of PETSc. Those applications are available in the ksp part which is suited for scalable linear equations solvers: \begin{itemize} \item ex15 is an example which solves in parallel an operator using a finite - difference scheme. The diagonal is equals to 4 and 4 extra-diagonals - representing the neighbors in each directions is equal to -1. This example is + difference scheme. The diagonal is equal to 4 and 4 extra-diagonals + representing the neighbors in each directions are equal to -1. This example is used in many physical phenomena, for example, heat and fluid flow, wave - propagation... + propagation, etc. \item ex54 is another example based on 2D problem discretized with quadrilateral finite elements. For this example, the user can define the scaling of material - coefficient in embedded circle, it is called $\alpha$. + coefficient in embedded circle called $\alpha$. \end{itemize} -For more technical details on these applications, interested reader are invited -to read the codes available in the PETSc sources. Those problem have been -chosen because they are scalable with many cores. We have tested other problem -but they are not scalable with many cores. +For more technical details on these applications, interested readers are invited +to read the codes available in the PETSc sources. Those problems have been +chosen because they are scalable with many cores which is not the case of other +problems that we have tested. + +In the following larger experiments are described on two large scale +architectures: Curie and Juqeen. Both these architectures are supercomputer +composed of 80,640 cores for Curie and 458,752 cores for Juqueen. Those machines +are respectively hosted by GENCI in France and Jülich Supercomputing Centre in +Germany. They belongs with other similar architectures of the PRACE initiative ( +Partnership for Advanced Computing in Europe) which aims at proposing high +performance supercomputing architecture to enhance research in Europe. The Curie +architecture is composed of Intel E5-2680 processors at 2.7 GHz with 2Gb memory +by core. The Juqueen architecture is composed of IBM PowerPC A2 at 1.6 GHz with +1Gb memory per core. Both those architecture are equiped with a dedicated high +speed network. + + +In many situations, using preconditioners is essential in order to find the +solution of a linear system. There are many preconditioners available in PETSc. +For parallel applications all the preconditioners based on matrix factorization +are not available. In our experiments, we have tested different kinds of +preconditioners, however as it is not the subject of this paper, we will not +present results with many preconditioners. In practise, we have chosen to use a +multigrid (mg) and successive over-relaxation (sor). For more details on the +preconditioner in PETSc please consult~\cite{petsc-web-page}. -In the following larger experiments are described on two large scale architectures: Curie and Juqeen... {\bf description...}\\ -{\bf Description of preconditioners} - \begin{table*}[htbp] \begin{center} \begin{tabular}{|r|r|r|r|r|r|r|r|r|} @@ -865,27 +967,26 @@ In the following larger experiments are described on two large scale architectur \hline \end{tabular} -\caption{Comparison of FGMRES and TSIRM with FGMRES for example ex15 of PETSc with two preconditioner (mg and sor) with 25,000 components per core on Juqueen (threshold 1e-3, restart=30, s=12), time is expressed in seconds.} +\caption{Comparison of FGMRES and TSIRM with FGMRES for example ex15 of PETSc with two preconditioners (mg and sor) with 25,000 components per core on Juqueen (threshold 1e-3, restart=30, s=12), time is expressed in seconds.} \label{tab:03} \end{center} \end{table*} Table~\ref{tab:03} shows the execution times and the number of iterations of -example ex15 of PETSc on the Juqueen architecture. Differents number of cores -are studied rangin from 2,048 upto 16,383. Two preconditioners have been -tested. For those experiments, the number of components (or unknown of the -problems) per processor is fixed to 25,000, also called weak scaling. This +example ex15 of PETSc on the Juqueen architecture. Different numbers of cores +are studied ranging from 2,048 up-to 16,383 with the two preconditioners {\it mg} and {\it sor}. For those experiments, the number of components (or unknowns of the +problems) per core is fixed to 25,000, also called weak scaling. This number can seem relatively small. In fact, for some applications that need a lot of memory, the number of components per processor requires sometimes to be small. -In this Table, we can notice that TSIRM is always faster than FGMRES. The last +In Table~\ref{tab:03}, we can notice that TSIRM is always faster than FGMRES. The last column shows the ratio between FGMRES and the best version of TSIRM according to the minimization procedure: CGLS or LSQR. Even if we have computed the worst -case between CGLS and LSQR, it is clear that TSIRM is alsways faster than -FGMRES. For this example, the multigrid preconditionner is faster than SOR. The +case between CGLS and LSQR, it is clear that TSIRM is always faster than +FGMRES. For this example, the multigrid preconditioner is faster than SOR. The gain between TSIRM and FGMRES is more or less similar for the two preconditioners. Looking at the number of iterations to reach the convergence, it is obvious that TSIRM allows the reduction of the number of iterations. It @@ -897,16 +998,16 @@ corresponds to 30*12, there are $max\_iter_{ls}$ which corresponds to 15. \begin{figure}[htbp] \centering \includegraphics[width=0.45\textwidth]{nb_iter_sec_ex15_juqueen} -\caption{Number of iterations per second with ex15 and the same parameters than in Table~\ref{tab:03}} +\caption{Number of iterations per second with ex15 and the same parameters than in Table~\ref{tab:03} (weak scaling)} \label{fig:01} \end{figure} In Figure~\ref{fig:01}, the number of iterations per second corresponding to -Table~\ref{tab:01} is displayed. It can be noticed that the number of -iterations per second of FMGRES is constant whereas it decrease with TSIRM with -both preconditioner. This can be explained by the fact that when the number of -core increases the time for the minimization step also increases but, generally, +Table~\ref{tab:03} is displayed. It can be noticed that the number of +iterations per second of FMGRES is constant whereas it decreases with TSIRM with +both preconditioners. This can be explained by the fact that when the number of +cores increases the time for the least-squares minimization step also increases but, generally, when the number of cores increases, the number of iterations to reach the threshold also increases, and, in that case, TSIRM is more efficient to reduce the number of iterations. So, the overall benefit of using TSIRM is interesting. @@ -921,7 +1022,7 @@ the number of iterations. So, the overall benefit of using TSIRM is interesting. \begin{tabular}{|r|r|r|r|r|r|r|r|r|} \hline - nb. cores & threshold & \multicolumn{2}{c|}{GMRES} & \multicolumn{2}{c|}{TSIRM CGLS} & \multicolumn{2}{c|}{TSIRM LSQR} & best gain \\ + nb. cores & threshold & \multicolumn{2}{c|}{FGMRES} & \multicolumn{2}{c|}{TSIRM CGLS} & \multicolumn{2}{c|}{TSIRM LSQR} & best gain \\ \cline{3-8} & & Time & \# Iter. & Time & \# Iter. & Time & \# Iter. & \\\hline \hline 2,048 & 8e-5 & 108.88 & 16,560 & 23.06 & 3,630 & 22.79 & 3,630 & 4.77 \\ @@ -934,23 +1035,55 @@ the number of iterations. So, the overall benefit of using TSIRM is interesting. \hline \end{tabular} -\caption{Comparison of FGMRES and 2 stage FGMRES algorithms for ex54 of Petsc (both with the MG preconditioner) with 25000 components per core on Curie (restart=30, s=12), time is expressed in seconds.} +\caption{Comparison of FGMRES and TSIRM with FGMRES algorithms for ex54 of Petsc (both with the MG preconditioner) with 25,000 components per core on Curie (restart=30, s=12), time is expressed in seconds.} \label{tab:04} \end{center} \end{table*} -In Table~\ref{tab:04}, some experiments with example ex54 on the Curie architecture are reported - +In Table~\ref{tab:04}, some experiments with example ex54 on the Curie +architecture are reported. For this application, we fixed $\alpha=0.6$. As it +can be seen in that Table, the size of the problem has a strong influence on the +number of iterations to reach the convergence. That is why we have preferred to +change the threshold. If we set it to $1e-3$ as with the previous application, +only one iteration is necessray to reach the convergence. So Table~\ref{tab:04} +shows the results of differents executions with differents number of cores and +differents thresholds. As with the previous example, we can observe that TSIRM +is faster than FGMRES. The ratio greatly depends on the number of iterations for +FMGRES to reach the threshold. The greater the number of iterations to reach the +convergence is, the better the ratio between our algorithm and FMGRES is. This +experiment is also a weak scaling with approximately $25,000$ components per +core. It can also be observed that the difference between CGLS and LSQR is not +significant. Both can be good but it seems not possible to know in advance which +one will be the best. + +Table~\ref{tab:05} show a strong scaling experiment with the exemple ex54 on the +Curie architecture. So in this case, the number of unknownws is fixed to +$204,919,225$ and the number of cores ranges from $512$ to $8192$ with the power +of two. The threshold is fixed to $5e-5$ and only the $mg$ preconditioner has +been tested. Here again we can see that TSIRM is faster that FGMRES. Efficiecy +of each algorithms is reported. It can be noticed that FGMRES is more efficient +than TSIRM except with $8,192$ cores and that its efficiency is greater that one +whereas the efficiency of TSIRM is lower than one. Nevertheless, the ratio of +TSIRM with any version of the least-squares method is always faster. With +$8,192$ cores when the number of iterations is far more important for FGMRES, we +can see that it is only slightly more important for TSIRM. + +In Figure~\ref{fig:02} we report the number of iterations per second for +experiments reported in Table~\ref{tab:05}. This Figure highlights that the +number of iterations per seconds is more of less the same for FGMRES and TSIRM +with a little advantage for FGMRES. It can be explained by the fact that, as we +have previously explained, that the iterations of the least-sqaure steps are not +taken into account with TSIRM. \begin{table*}[htbp] \begin{center} \begin{tabular}{|r|r|r|r|r|r|r|r|r|r|r|} \hline - nb. cores & \multicolumn{2}{c|}{GMRES} & \multicolumn{2}{c|}{TSIRM CGLS} & \multicolumn{2}{c|}{TSIRM LSQR} & best gain & \multicolumn{3}{c|}{efficiency} \\ + nb. cores & \multicolumn{2}{c|}{FGMRES} & \multicolumn{2}{c|}{TSIRM CGLS} & \multicolumn{2}{c|}{TSIRM LSQR} & best gain & \multicolumn{3}{c|}{efficiency} \\ \cline{2-7} \cline{9-11} - & Time & \# Iter. & Time & \# Iter. & Time & \# Iter. & & GMRES & TS CGLS & TS LSQR\\\hline \hline + & Time & \# Iter. & Time & \# Iter. & Time & \# Iter. & & FGMRES & TS CGLS & TS LSQR\\\hline \hline 512 & 3,969.69 & 33,120 & 709.57 & 5,790 & 622.76 & 5,070 & 6.37 & 1 & 1 & 1 \\ 1024 & 1,530.06 & 25,860 & 290.95 & 4,830 & 307.71 & 5,070 & 5.25 & 1.30 & 1.21 & 1.01 \\ 2048 & 919.62 & 31,470 & 237.52 & 8,040 & 194.22 & 6,510 & 4.73 & 1.08 & .75 & .80\\ @@ -960,11 +1093,38 @@ In Table~\ref{tab:04}, some experiments with example ex54 on the Curie architect \hline \end{tabular} -\caption{Comparison of FGMRES and 2 stage FGMRES algorithms for ex54 of Petsc (both with the MG preconditioner) with 204,919,225 components on Curie with different number of cores (restart=30, s=12, threshol 5e-5), time is expressed in seconds.} +\caption{Comparison of FGMRES and TSIRM with FGMRES for ex54 of Petsc (both with the MG preconditioner) with 204,919,225 components on Curie with different number of cores (restart=30, s=12, threshold 5e-5), time is expressed in seconds.} \label{tab:05} \end{center} \end{table*} +\begin{figure}[htbp] +\centering + \includegraphics[width=0.45\textwidth]{nb_iter_sec_ex54_curie} +\caption{Number of iterations per second with ex54 and the same parameters than in Table~\ref{tab:05} (strong scaling)} +\label{fig:02} +\end{figure} + + +Concerning the experiments some other remarks are interesting. +\begin{itemize} +\item We can tested other examples of PETSc (ex29, ex45, ex49). For all these + examples, we also obtained similar gain between GMRES and TSIRM but those + examples are not scalable with many cores. In general, we had some problems + with more than $4,096$ cores. +\item We have tested many iterative solvers available in PETSc. In fast, it is + possible to use most of them with TSIRM. From our point of view, the condition + to use a solver inside TSIRM is that the solver must have a restart + feature. More precisely, the solver must support to be stoped and restarted + without decrease its converge. That is why with GMRES we stop it when it is + naturraly restarted (i.e. with $m$ the restart parameter). The Conjugate + Gradient (CG) and all its variants do not have ``restarted'' version in PETSc, + so they are not efficient. They will converge with TSIRM but not quickly + because if we compare a normal CG with a CG for which we stop it each 16 + iterations for example, the normal CG will be for more efficient. Some + restarted CG or CG variant versions exist and may be interested to study in + future works. +\end{itemize} %%%********************************************************* %%%********************************************************* @@ -978,13 +1138,23 @@ In Table~\ref{tab:04}, some experiments with example ex54 on the Curie architect %%%********************************************************* %%%********************************************************* +A novel two-stage iterative algorithm has been proposed in this article, +in order to accelerate the convergence Krylov iterative methods. +Our TSIRM proposal acts as a merger between Krylov based solvers and +a least-squares minimization step. +The convergence of the method has been proven in some situations, while +experiments up to 16,394 cores have been led to verify that TSIRM runs +5 or 7 times faster than GMRES. + -future plan : \\ -- study other kinds of matrices, problems, inner solvers\\ -- test the influence of all the parameters\\ -- adaptative number of outer iterations to minimize\\ -- other methods to minimize the residuals?\\ -- implement our solver inside PETSc +For future work, the authors' intention is to investigate other kinds of +matrices, problems, and inner solvers. The influence of all parameters must be +tested too, while other methods to minimize the residuals must be regarded. The +number of outer iterations to minimize should become adaptative to improve the +overall performances of the proposal. Finally, this solver will be implemented +inside PETSc. This would be very interesting because it would allow us to test +all the non-linear examples and compare our algorithm with the other algorithm +implemented in PETSc. % conference papers do not normally have an appendix @@ -996,7 +1166,7 @@ future plan : \\ %%%********************************************************* \section*{Acknowledgment} This paper is partially funded by the Labex ACTION program (contract -ANR-11-LABX-01-01). We acknowledge PRACE for awarding us access to resource +ANR-11-LABX-01-01). We acknowledge PRACE for awarding us access to resources Curie and Juqueen respectively based in France and Germany. @@ -1039,5 +1209,3 @@ Curie and Juqueen respectively based in France and Germany. % that's all folks \end{document} - -