%
% 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}
-%où
-%\title{A two-stage algorithm with error minimization to solve large sparse linear systems}
-%où
-%\title{???}
+\title{TSIRM: A Two-Stage Iteration with least-squares Residual Minimization algorithm to solve large sparse linear systems}
+
% 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}
\begin{abstract}
-In this article, a two-stage iterative method is proposed to improve the
-convergence of Krylov based iterative ones, 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 while making 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
-run around 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}
% 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
%%%*********************************************************
%%%*********************************************************
-\section{Two-stage algorithm with least-square residuals minimization}
+\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
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.
\begin{algorithmic}[1]
\Input $A$ (sparse matrix), $b$ (right-hand side)
\Output $x$ (solution vector)\vspace{0.2cm}
- \State Set the initial guess $x^0$
+ \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}
+ \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 iteration 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 take more place, so we briefly explain the parallelization of CGLS which is similar to LSQR.
+less the same principle but it takes more place, so we briefly explain the parallelization of CGLS which is similar to LSQR.
\begin{algorithm}[t]
\caption{CGLS}
\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}
In each iteration of CGLS, there is two matrix-vector multiplications and some
-classical operations: dots, norm, multiplication and addition on vectors. All
+classical operations: dot product, norm, multiplication and addition on vectors. All
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 $M$ 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}
-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 $A$.
+\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.
%%%*********************************************************
%%%*********************************************************
\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 is 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{table}[htbp]
\begin{center}
\begin{tabular}{|c|c|r|r|r|}
\hline
Matrix name & Field &\# Rows & \# Nonzeros \\\hline \hline
crashbasis & Optimization & 160,000 & 1,750,416 \\
-parabolic\_fem & Computational fluid dynamics & 525,825 & 2,100,225 \\
+parabolic\_fem & Comput. fluid dynamics & 525,825 & 2,100,225 \\
epb3 & Thermal problem & 84,617 & 463,625 \\
-atmosmodj & Computational fluid dynamics & 1,270,432 & 8,814,880 \\
-bfwa398 & Electromagnetics problem & 398 & 3,678 \\
+atmosmodj & Comput. fluid dynamics & 1,270,432 & 8,814,880 \\
+bfwa398 & Electromagnetics pb & 398 & 3,678 \\
torso3 & 2D/3D problem & 259,156 & 4,429,042 \\
\hline
\caption{Main characteristics of the sparse matrices chosen from the Davis collection}
\label{tab:01}
\end{center}
-\end{table*}
-
-The following parameters have been chosen for our experiments. As by default
+\end{table}
+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, $max\_iter_{kryl}=30$). $s$ is set to 8. CGLS is
+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:
$\epsilon_{ls}=1e-40$ and $max\_iter_{ls}=20$. The external precision is set to
$\epsilon_{tsirm}=1e-10$. Those experiments have been performed on a Intel(R)
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]
\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}
-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.
-
-In the following larger experiments are described on two large scale architectures: Curie and Juqeen... {\bf description...}\\
+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}.
-{\bf Description of preconditioners}
\begin{table*}[htbp]
\begin{center}
\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. 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
+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 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
-
+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
+should be noticed that for TSIRM, in those experiments, only the iterations of
+the Krylov solver are taken into account. Iterations of CGLS or LSQR were not
+recorded but they are time-consuming. In general each $max\_iter_{kryl}*s$ which
+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: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.
+
+
+
\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 \\
\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. 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.
\begin{table*}[htbp]
\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\\
\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}
+
%%%*********************************************************
%%%*********************************************************
%%%*********************************************************
%%%*********************************************************
+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.
% conference papers do not normally have an appendix
%%%*********************************************************
\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.
% that's all folks
\end{document}
-
-
