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52 %\JOURNALNAME{\TEN{\it Int. J. System Control and Information
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54 %Vol. \theVOL, No. \theISSUE, \thePUBYEAR\hfill\thepage}}%
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58 %\thispagestyle{empty}%
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60 %\NINE\begin{tabular*}{\textwidth}{@{\extracolsep{\fill}}lcr@{}}%
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62 %Copyright \copyright\ 2012 Inderscience Enterprises Ltd. & &%
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74 \setcounter{page}{1}
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76 \LRH{F. Wang et~al.}
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78 \RRH{Metadata Based Management and Sharing of Distributed Biomedical
\r
93 \title{TSIRM: A Two-Stage Iteration with least-squares Residual Minimization algorithm to solve large sparse linear and non linear systems}
\r
96 \authorA{Rapha\"el Couturier}
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98 \affA{Femto-ST Institute, University of Bourgogne Franche-Comte, France\\
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99 E-mail: raphael.couturier@univ-fcomte.fr}
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102 \authorB{Lilia Ziane Khodja}
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103 \affB{LTAS-Mécanique numérique non linéaire, University of Liege, Belgium \\
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104 E-mail: l.zianekhodja@ulg.ac.be}
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106 \authorC{Christophe Guyeux}
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107 \affC{Femto-ST Institute, University of Bourgogne Franche-Comte, France\\
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108 E-mail: christophe.guyeux@univ-fcomte.fr}
\r
112 In this article, a two-stage iterative algorithm is proposed to improve the
\r
113 convergence of Krylov based iterative methods, typically those of GMRES
\r
114 variants. The principle of the proposed approach is to build an external
\r
115 iteration over the Krylov method, and to frequently store its current residual
\r
116 (at each GMRES restart for instance). After a given number of outer iterations,
\r
117 a least-squares minimization step is applied on the matrix composed by the saved
\r
118 residuals, in order to compute a better solution and to make new iterations if
\r
119 required. It is proven that the proposal has the same convergence properties
\r
120 than the inner embedded method itself. Experiments using up to 16,394 cores
\r
121 also show that the proposed algorithm runs around 5 or 7 times faster than
\r
125 \KEYWORD{Iterative Krylov methods; sparse linear and non linear systems; two stage iteration; least-squares residual minimization; PETSc.}
\r
127 %\REF{to this paper should be made as follows: Rodr\'{\i}guez
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128 %Bol\'{\i}var, M.P. and Sen\'{e}s Garc\'{\i}a, B. (xxxx) `The
\r
129 %corporate environmental disclosures on the internet: the case of
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130 %IBEX 35 Spanish companies', {\it International Journal of Metadata,
\r
131 %Semantics and Ontologies}, Vol. x, No. x, pp.xxx\textendash xxx.}
\r
134 Manuel Pedro Rodr\'iguez Bol\'ivar received his PhD in Accounting at
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135 the University of Granada. He is a Lecturer at the Department of
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136 Accounting and Finance, University of Granada. His research
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137 interests include issues related to conceptual frameworks of
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138 accounting, diffusion of financial information on Internet, Balanced
\r
139 Scorecard applications and environmental accounting. He is author of
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140 a great deal of research studies published at national and
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141 international journals, conference proceedings as well as book
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142 chapters, one of which has been edited by Kluwer Academic
\r
145 \noindent Bel\'en Sen\'es Garc\'ia received her PhD in Accounting at
\r
146 the University of Granada. She is a Lecturer at the Department of
\r
147 Accounting and Finance, University of Granada. Her research
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148 interests are related to cultural, institutional and historic
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149 accounting and in environmental accounting. She has published
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150 research papers at national and international journals, conference
\r
151 proceedings as well as chapters of books.\vs{8}
\r
153 \noindent Both authors have published a book about environmental
\r
154 accounting edited by the Institute of Accounting and Auditing,
\r
155 Ministry of Economic Affairs, in Spain in October 2003.
\r
162 \section{Introduction}
\r
164 Iterative methods have recently become more attractive than direct ones to solve
\r
165 very large sparse linear systems~\cite{Saad2003}. They are more efficient in a
\r
166 parallel context, supporting thousands of cores, and they require less memory
\r
167 and arithmetic operations than direct methods~\cite{bahicontascoutu}. This is
\r
168 why new iterative methods are frequently proposed or adapted by researchers, and
\r
169 the increasing need to solve very large sparse linear systems has triggered the
\r
170 development of such efficient iterative techniques suitable for parallel
\r
173 Most of the successful iterative methods currently available are based on
\r
174 so-called ``Krylov subspaces''. They consist in forming a basis of successive
\r
175 matrix powers multiplied by an initial vector, which can be for instance the
\r
176 residual. These methods use vectors orthogonality of the Krylov subspace basis
\r
177 in order to solve linear systems. The best known iterative Krylov subspace
\r
178 methods are conjugate gradient and GMRES ones (Generalized Minimal RESidual).
\r
181 However, iterative methods suffer from scalability problems on parallel
\r
182 computing platforms with many processors, due to their need of reduction
\r
183 operations, and to collective communications to achieve matrix-vector
\r
184 multiplications. The communications on large clusters with thousands of cores
\r
185 and large sizes of messages can significantly affect the performances of these
\r
186 iterative methods. As a consequence, Krylov subspace iteration methods are often
\r
187 used with preconditioners in practice, to increase their convergence and
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188 accelerate their performances. However, most of the good preconditioners are
\r
189 not scalable on large clusters.
\r
191 In this research work, a two-stage algorithm based on two nested iterations
\r
192 called inner-outer iterations is proposed. This algorithm consists in solving
\r
193 the sparse linear system iteratively with a small number of inner iterations,
\r
194 and restarting the outer step with a new solution minimizing some error
\r
195 functions over some previous residuals. For further information on two-stage
\r
196 iteration methods, interested readers are invited to
\r
197 consult~\cite{Nichols:1973:CTS}. Two-stage algorithms are easy to parallelize on
\r
198 large clusters. Furthermore, the least-squares minimization technique improves
\r
199 its convergence and performances.
\r
201 The present article is organized as follows. Related works are presented in
\r
202 Section~\ref{sec:02}. Section~\ref{sec:03} details the two-stage algorithm using
\r
203 a least-squares residual minimization, while Section~\ref{sec:04} provides
\r
204 convergence results regarding this method. Section~\ref{sec:05} shows some
\r
205 experimental results obtained on large clusters using routines of PETSc
\r
206 toolkit. This research work ends by a conclusion section, in which the proposal
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207 is summarized while intended perspectives are provided.
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211 %%%*********************************************************
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212 %%%*********************************************************
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216 %%%*********************************************************
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217 %%%*********************************************************
\r
218 \section{Related works}
\r
220 Krylov subspace iteration methods have increasingly become key
\r
221 techniques for solving linear and nonlinear systems, or eigenvalue problems,
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222 especially since the increasing development of
\r
223 preconditioners~\cite{Saad2003,Meijerink77}. One reason for the popularity of
\r
224 these methods is their generality, simplicity, and efficiency to solve systems of
\r
225 equations arising from very large and complex problems.
\r
227 GMRES is one of the most widely used Krylov iterative method for solving sparse
\r
228 and large linear systems. It has been developed by Saad \emph{et
\r
229 al.}~\cite{Saad86} as a generalized method to deal with unsymmetric and
\r
230 non-Hermitian problems, and indefinite symmetric problems too. In its original
\r
231 version called full GMRES, this algorithm minimizes the residual over the
\r
232 current Krylov subspace until convergence in at most $n$ iterations, where $n$
\r
233 is the size of the sparse matrix. Full GMRES is however too expensive in the
\r
234 case of large matrices, since the required orthogonalization process per
\r
235 iteration grows quadratically with the number of iterations. For that reason,
\r
236 GMRES is restarted in practice after each $m\ll n$ iterations, to avoid the
\r
237 storage of a large orthonormal basis. However, the convergence behavior of the
\r
238 restarted GMRES, called GMRES($m$), in many cases depends quite critically on
\r
239 the $m$ value~\cite{Huang89}. Therefore in most cases, a preconditioning
\r
240 technique is applied to the restarted GMRES method in order to improve its
\r
243 To enhance the robustness of Krylov iterative solvers, some techniques have been
\r
244 proposed allowing the use of different preconditioners, if necessary, within the
\r
245 iteration itself instead of restarting. Those techniques may lead to
\r
246 considerable savings in CPU time and memory requirements. Van der Vorst
\r
247 in~\cite{Vorst94} has for instance proposed variants of the GMRES algorithm in
\r
248 which a different preconditioner is applied in each iteration, leading to the
\r
249 so-called GMRESR family of nested methods. In fact, the GMRES method is
\r
250 effectively preconditioned with other iterative schemes (or GMRES itself), where
\r
251 the iterations of the GMRES method are called outer iterations while the
\r
252 iterations of the preconditioning process is referred to as inner iterations.
\r
253 Saad in~\cite{Saad:1993} has proposed Flexible GMRES (FGMRES) which is another
\r
254 variant of the GMRES algorithm using a variable preconditioner. In FGMRES the
\r
255 search directions are preconditioned whereas in GMRESR the residuals are
\r
256 preconditioned. However, in practice, good preconditioners are those based on
\r
257 direct methods, as ILU preconditioners, which are not easy to parallelize and
\r
258 suffer from the scalability problems on large clusters of thousands of cores.
\r
260 Recently, communication-avoiding methods have been developed to reduce the
\r
261 communication overheads in Krylov subspace iterative solvers. On modern computer
\r
262 architectures, communications between processors are much slower than
\r
263 floating-point arithmetic operations on a given
\r
264 processor. Communication-avoiding techniques reduce either communications
\r
265 between processors or data movements between levels of the memory hierarchy, by
\r
266 reformulating the communication-bound kernels (more frequently SpMV kernels) and
\r
267 the orthogonalization operations within the Krylov iterative solver. Different
\r
268 works have studied the communication-avoiding techniques for the GMRES method,
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269 so-called CA-GMRES, on multicore processors and multi-GPU
\r
270 machines~\cite{Mohiyuddin2009,Hoemmen2010,Yamazaki2014}.
\r
272 Compared to all these works and to all the other works on Krylov iterative
\r
273 methods, the originality of our work is to build a second iteration over a
\r
274 Krylov iterative method and to minimize the residuals with a least-squares
\r
275 method after a given number of outer iterations.
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277 %%%*********************************************************
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278 %%%*********************************************************
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282 %%%*********************************************************
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283 %%%*********************************************************
\r
284 \section{TSIRM: Two-stage iteration with least-squares residuals minimization algorithm}
\r
286 A two-stage algorithm is proposed to solve large sparse linear systems of the
\r
287 form $Ax=b$, where $A\in\mathbb{R}^{n\times n}$ is a sparse and square
\r
288 nonsingular matrix, $x\in\mathbb{R}^n$ is the solution vector, and
\r
289 $b\in\mathbb{R}^n$ is the right-hand side. As explained previously, the
\r
290 algorithm is implemented as an inner-outer iteration solver based on iterative
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291 Krylov methods. The main key-points of the proposed solver are given in
\r
292 Algorithm~\ref{algo:01}. It can be summarized as follows: the inner solver is a
\r
293 Krylov based one. In order to accelerate its convergence, the outer solver
\r
294 periodically applies a least-squares minimization on the residuals computed by
\r
297 At each outer iteration, the sparse linear system $Ax=b$ is partially solved
\r
298 using only $m$ iterations of an iterative method, this latter being initialized
\r
299 with the last obtained approximation. The GMRES method~\cite{Saad86}, or any of
\r
300 its variants, can potentially be used as inner solver. The current approximation
\r
301 of the Krylov method is then stored inside a $n \times s$ matrix $S$, which is
\r
302 composed by the $s$ last solutions that have been computed during the inner
\r
303 iterations phase. In the remainder, the $i$-th column vector of $S$ will be
\r
306 At each $s$ iterations, another kind of minimization step is applied in order to
\r
307 compute a new solution $x$. For that, the previous residuals of $Ax=b$ are
\r
308 computed by the inner iterations with $(b-AS)$. The minimization of the
\r
309 residuals is obtained by
\r
311 \underset{\alpha\in\mathbb{R}^{s}}{min}\|b-R\alpha\|_2
\r
314 with $R=AS$. The new solution $x$ is then computed with $x=S\alpha$.
\r
317 In practice, $R$ is a dense rectangular matrix belonging in $\mathbb{R}^{n\times
\r
318 s}$, with $s\ll n$. In order to minimize~\eqref{eq:01}, a least-squares
\r
319 method such as CGLS ~\cite{Hestenes52} or LSQR~\cite{Paige82} is used. Remark
\r
320 that these methods are more appropriate than a single direct method in a
\r
321 parallel context. CGLS has recently been used to improve the performance of multisplitting algorithms \cite{cz15:ij}.
\r
325 \begin{algorithm}[t]
\r
327 \begin{algorithmic}[1]
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328 \Input $A$ (sparse matrix), $b$ (right-hand side)
\r
329 \Output $x$ (solution vector)\vspace{0.2cm}
\r
330 \State Set the initial guess $x_0$
\r
331 \For {$k=1,2,3,\ldots$ until convergence ($error<\epsilon_{tsirm}$)} \label{algo:conv}
\r
332 \State $[x_k,error]=Solve(A,b,x_{k-1},max\_iter_{kryl})$ \label{algo:solve}
\r
333 \State $S_{k \mod s}=x_k$ \label{algo:store} \Comment{update column ($k \mod s$) of $S$}
\r
334 \If {$k \mod s=0$ {\bf and} $error>\epsilon_{kryl}$}
\r
335 \State $R=AS$ \Comment{compute dense matrix} \label{algo:matrix_mul}
\r
336 \State $\alpha=Least\_Squares(R,b,max\_iter_{ls})$ \label{algo:}
\r
337 \State $x_k=S\alpha$ \Comment{compute new solution}
\r
344 Algorithm~\ref{algo:01} summarizes the principle of the proposed method. The
\r
345 outer iteration is inside the \emph{for} loop. Line~\ref{algo:solve}, the Krylov
\r
346 method is called for a maximum of $max\_iter_{kryl}$ iterations. In practice,
\r
347 we suggest to set this parameter equal to the restart number in the GMRES-like
\r
348 method. Moreover, a tolerance threshold must be specified for the solver. In
\r
349 practice, this threshold must be much smaller than the convergence threshold of
\r
350 the TSIRM algorithm (\emph{i.e.}, $\epsilon_{tsirm}$). We also consider that
\r
351 after the call of the $Solve$ function, we obtain the vector $x_k$ and the
\r
352 $error$, which is defined by $||Ax_k-b||_2$.
\r
354 Line~\ref{algo:store}, $S_{k \mod s}=x_k$ consists in copying the solution
\r
355 $x_k$ into the column $k \mod s$ of $S$. After the minimization, the matrix
\r
356 $S$ is reused with the new values of the residuals. To solve the minimization
\r
357 problem, an iterative method is used. Two parameters are required for that:
\r
358 the maximum number of iterations ($max\_iter_{ls}$) and the threshold to stop
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359 the method ($\epsilon_{ls}$).
\r
361 Let us summarize the most important parameters of TSIRM:
\r
363 \item $\epsilon_{tsirm}$: the threshold that stops the TSIRM method;
\r
364 \item $max\_iter_{kryl}$: the maximum number of iterations for the Krylov method;
\r
365 \item $s$: the number of outer iterations before applying the minimization step;
\r
366 \item $max\_iter_{ls}$: the maximum number of iterations for the iterative least-squares method;
\r
367 \item $\epsilon_{ls}$: the threshold used to stop the least-squares method.
\r
371 The parallelization of TSIRM relies on the parallelization of all its
\r
372 parts. More precisely, except the least-squares step, all the other parts are
\r
373 obvious to achieve out in parallel. In order to develop a parallel version of
\r
374 our code, we have chosen to use PETSc~\cite{petsc-web-page}. In
\r
375 line~\ref{algo:matrix_mul}, the matrix-matrix multiplication is implemented and
\r
376 efficient since the matrix $A$ is sparse and the matrix $S$ contains few columns
\r
377 in practice. As explained previously, at least two methods seem to be
\r
378 interesting to solve the least-squares minimization, the CGLS and the LSQR
\r
381 In Algorithm~\ref{algo:02} we remind the CGLS algorithm. The LSQR method follows
\r
382 more or less the same principle but it takes more place, so we briefly explain
\r
383 the parallelization of CGLS which is similar to LSQR.
\r
385 \begin{algorithm}[t]
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387 \begin{algorithmic}[1]
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388 \Input $A$ (matrix), $b$ (right-hand side)
\r
389 \Output $x$ (solution vector)\vspace{0.2cm}
\r
390 \State Let $x_0$ be an initial approximation
\r
391 \State $r_0=b-Ax_0$
\r
392 \State $p_1=A^Tr_0$
\r
394 \State $\gamma=||s_0||^2_2$
\r
395 \For {$k=1,2,3,\ldots$ until convergence ($\gamma<\epsilon_{ls}$)} \label{algo2:conv}
\r
397 \State $\alpha_k=\gamma/||q_k||^2_2$
\r
398 \State $x_k=x_{k-1}+\alpha_kp_k$
\r
399 \State $r_k=r_{k-1}-\alpha_kq_k$
\r
400 \State $s_k=A^Tr_k$
\r
401 \State $\gamma_{old}=\gamma$
\r
402 \State $\gamma=||s_k||^2_2$
\r
403 \State $\beta_k=\gamma/\gamma_{old}$
\r
404 \State $p_{k+1}=s_k+\beta_kp_k$
\r
411 In each iteration of CGLS, there are two matrix-vector multiplications and some
\r
412 classical operations: dot product, norm, multiplication, and addition on
\r
413 vectors. All these operations are easy to implement in PETSc or similar
\r
414 environment. It should be noticed that LSQR follows the same principle, it is a
\r
415 little bit longer but it performs more or less the same operations.
\r
418 %%%*********************************************************
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419 %%%*********************************************************
\r
421 \section{Convergence results}
\r
425 We can now claim that,
\r
426 \begin{proposition}
\r
428 If $A$ is either a definite positive or a positive matrix and GMRES($m$) is used as a solver, then the TSIRM algorithm is convergent.
\r
430 Furthermore, let $r_k$ be the
\r
431 $k$-th residue of TSIRM, then
\r
432 we have the following boundaries:
\r
434 \item when $A$ is positive:
\r
436 ||r_k|| \leqslant \left(1-\dfrac{\alpha}{\beta}\right)^{\frac{km}{2}} ||r_0|| ,
\r
438 where $M$ is the symmetric part of $A$, $\alpha = \lambda_{min}(M)^2$ and $\beta = \lambda_{max}(A^T A)$;
\r
439 \item when $A$ is positive definite:
\r
441 \|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\|.
\r
444 %In the general case, where A is not positive definite, we have
\r
445 %$\|r_n\| \le \inf_{p \in P_n} \|p(A)\| \le \kappa_2(V) \inf_{p \in P_n} \max_{\lambda \in \sigma(A)} |p(\lambda)| \|r_0\|, .$
\r
449 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:
\r
451 \|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\| .
\r
453 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:
\r
455 ||r_m|| \leqslant \left(1-\dfrac{\alpha}{\beta}\right)^{\frac{m}{2}} ||r_0|| ,
\r
457 where $\alpha$ and $\beta$ are defined as in Proposition~\ref{prop:saad}, which proves
\r
458 the convergence of GMRES($m$) for all $m$ under such assumptions regarding $A$.
\r
459 These well-known results can be found, \emph{e.g.}, in~\cite{Saad86}.
\r
461 We will now prove by a mathematical induction that, for each $k \in \mathbb{N}^\ast$,
\r
462 $||r_k|| \leqslant \left(1-\dfrac{\alpha}{\beta}\right)^{\frac{mk}{2}} ||r_0||$ 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.
\r
464 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.
\r
466 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.
\r
467 We will show that the statement holds too for $r_k$. Two situations can occur:
\r
469 \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.
\r
470 \item Else, the TSIRM algorithm consists in two stages: a first GMRES($m$) execution leads to a temporary $x_k$ whose residue satisfies:
\r
472 \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,
\r
473 \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,
\r
475 and a least squares resolution.
\r
476 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,\\
\r
477 $\min_{\alpha \in \mathbb{R}^s} ||b-R\alpha ||_2 = \min_{\alpha \in \mathbb{R}^s} ||b-AS\alpha ||_2$
\r
480 & = \min_{x \in span\left(S_{k-s+1}, S_{k-s+2}, \hdots, S_{k} \right)} ||b-AS\alpha ||_2\\
\r
481 & = \min_{x \in span\left(x_{k-s+1}, x_{k-s}+2, \hdots, x_{k} \right)} ||b-AS\alpha ||_2\\
\r
482 & \leqslant \min_{x \in span\left( x_{k} \right)} ||b-Ax ||_2\\
\r
483 & \leqslant \min_{\lambda \in \mathbb{R}} ||b-\lambda Ax_{k} ||_2\\
\r
484 & \leqslant ||b-Ax_{k}||_2\\
\r
486 & \leqslant \left(1-\dfrac{\alpha}{\beta}\right)^{\frac{km}{2}} ||r_0||, \textrm{ if $A$ is positive,}\\
\r
487 & \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}\\
\r
488 & \textrm{positive definite,}
\r
491 which concludes the induction and the proof.
\r
494 Remark that a similar proposition can be formulated at each time
\r
495 the given solver satisfies an inequality of the form $||r_n|| \leqslant \mu^n ||r_0||$,
\r
496 with $|\mu|<1$. Furthermore, it is \emph{a priori} possible in some particular cases
\r
498 that the proposed TSIRM converges while the GMRES($m$) does not.
\r
500 %%%*********************************************************
\r
501 %%%*********************************************************
\r
502 \section{Experiments using PETSc}
\r
506 In order to see the behavior of our approach when considering only one processor,
\r
507 a first comparison with GMRES or FGMRES and the new algorithm detailed
\r
508 previously has been experimented. Matrices that have been used with their
\r
509 characteristics (names, fields, rows, and nonzero coefficients) are detailed in
\r
510 Table~\ref{tab:01}. These latter, which are real-world applications matrices,
\r
511 have been extracted from the Davis collection, University of
\r
512 Florida~\cite{Dav97}.
\r
514 \begin{table}[htbp]
\r
516 \begin{tabular}{|c|c|r|r|r|}
\r
518 Matrix name & Field &\# Rows & \# Nonzeros \\\hline \hline
\r
519 crashbasis & Optimization & 160,000 & 1,750,416 \\
\r
520 parabolic\_fem & Comput. fluid dynamics & 525,825 & 2,100,225 \\
\r
521 epb3 & Thermal problem & 84,617 & 463,625 \\
\r
522 atmosmodj & Comput. fluid dynamics & 1,270,432 & 8,814,880 \\
\r
523 bfwa398 & Electromagnetics pb & 398 & 3,678 \\
\r
524 torso3 & 2D/3D problem & 259,156 & 4,429,042 \\
\r
528 \caption{Main characteristics of the sparse matrices chosen from the Davis collection}
\r
532 Chosen parameters are detailed below.
\r
533 We have stopped the GMRES every 30
\r
534 iterations (\emph{i.e.}, $max\_iter_{kryl}=30$), which is the default
\r
535 setting of GMRES restart parameter. The parameter $s$ has been set to 8. CGLS
\r
536 minimizes the least-squares problem with parameters
\r
537 $\epsilon_{ls}=1e-40$ and $max\_iter_{ls}=20$. The external precision is set to
\r
538 $\epsilon_{tsirm}=1e-10$. These experiments have been performed on an Intel(R)
\r
539 Core(TM) i7-3630QM CPU @ 2.40GHz with the 3.5.1 version of PETSc.
\r
542 Experiments comparing
\r
543 a GMRES variant with TSIRM in the resolution of linear systems are given in Table~\ref{tab:02}.
\r
544 The second column describes whether GMRES or FGMRES has been used for linear systems solving.
\r
545 Different preconditioners have been used according to the matrices. With TSIRM, the same
\r
546 solver and the same preconditioner are used. This table shows that TSIRM can
\r
547 drastically reduce the number of iterations needed to reach the convergence, when the
\r
548 number of iterations for the normal GMRES is more or less greater than 500. In
\r
549 fact this also depends on two parameters: the number of iterations before stopping GMRES
\r
550 and the number of iterations to perform the minimization.
\r
553 \begin{table}[htbp]
\r
555 \begin{tabular}{|c|c|r|r|r|r|}
\r
558 \multirow{2}{*}{Matrix name} & Solver / & \multicolumn{2}{c|}{GMRES} & \multicolumn{2}{c|}{TSIRM CGLS} \\
\r
560 & precond & Time & \# Iter. & Time & \# Iter. \\\hline \hline
\r
562 crashbasis & gmres / none & 15.65 & 518 & 14.12 & 450 \\
\r
563 parabolic\_fem & gmres / ilu & 1009.94 & 7573 & 401.52 & 2970 \\
\r
564 epb3 & fgmres / sor & 8.67 & 600 & 8.21 & 540 \\
\r
565 atmosmodj & fgmres / sor & 104.23 & 451 & 88.97 & 366 \\
\r
566 bfwa398 & gmres / none & 1.42 & 9612 & 0.28 & 1650 \\
\r
567 torso3 & fgmres / sor & 37.70 & 565 & 34.97 & 510 \\
\r
571 \caption{Comparison between sequential standalone (F)GMRES and TSIRM with (F)GMRES (time in seconds).}
\r
580 In order to perform larger experiments, we have tested some example applications
\r
581 of PETSc. These applications are available in the \emph{ksp} part, which is
\r
582 suited for scalable linear equations solvers:
\r
584 \item ex15 is an example that solves in parallel an operator using a finite
\r
585 difference scheme. The diagonal is equal to 4 and 4 extra-diagonals
\r
586 representing the neighbors in each directions are equal to -1. This example is
\r
587 used in many physical phenomena, for example, heat and fluid flow, wave
\r
589 \item ex54 is another example based on a 2D problem discretized with quadrilateral
\r
590 finite elements. In this example, the user can define the scaling of material
\r
591 coefficient in embedded circle called $\alpha$.
\r
593 For more technical details on these applications, interested readers are invited
\r
594 to read the codes available in the PETSc sources. These problems have been
\r
595 chosen because they are scalable with many cores.
\r
597 In the following, larger experiments are described on two large scale
\r
598 architectures: Curie and Juqueen. Both these architectures are supercomputers
\r
599 respectively composed of 80,640 cores for Curie and 458,752 cores for
\r
600 Juqueen. Those machines are respectively hosted by GENCI in France and Jülich
\r
601 Supercomputing Center in Germany. They belong with other similar architectures
\r
602 to the PRACE initiative (Partnership for Advanced Computing in Europe), which
\r
603 aims at proposing high performance supercomputing architecture to enhance
\r
604 research in Europe. The Curie architecture is composed of Intel E5-2680
\r
605 processors at 2.7 GHz with 2Gb memory by core. The Juqueen architecture,
\r
607 composed by IBM PowerPC A2 at 1.6 GHz with 1Gb memory per core. Both those
\r
608 architectures are equipped with a dedicated high speed network.
\r
611 In many situations, using preconditioners is essential in order to find the
\r
612 solution of a linear system. There are many preconditioners available in PETSc.
\r
613 However, for parallel applications, all the preconditioners based on matrix factorization
\r
614 are not available. In our experiments, we have tested different kinds of
\r
615 preconditioners, but as it is not the subject of this paper, we will not
\r
616 present results with many preconditioners. In practice, we have chosen to use a
\r
617 multigrid (mg) and successive over-relaxation (sor). For further details on the
\r
618 preconditioners in PETSc, readers are referred to~\cite{petsc-web-page}.
\r
622 \begin{table*}[htbp]
\r
624 \begin{tabular}{|r|r|r|r|r|r|r|r|r|}
\r
627 nb. cores & precond & \multicolumn{2}{c|}{FGMRES} & \multicolumn{2}{c|}{TSIRM CGLS} & \multicolumn{2}{c|}{TSIRM LSQR} & best gain \\
\r
629 & & Time & \# Iter. & Time & \# Iter. & Time & \# Iter. & \\\hline \hline
\r
630 2,048 & mg & 403.49 & 18,210 & 73.89 & 3,060 & 77.84 & 3,270 & 5.46 \\
\r
631 2,048 & sor & 745.37 & 57,060 & 87.31 & 6,150 & 104.21 & 7,230 & 8.53 \\
\r
632 4,096 & mg & 562.25 & 25,170 & 97.23 & 3,990 & 89.71 & 3,630 & 6.27 \\
\r
633 4,096 & sor & 912.12 & 70,194 & 145.57 & 9,750 & 168.97 & 10,980 & 6.26 \\
\r
634 8,192 & mg & 917.02 & 40,290 & 148.81 & 5,730 & 143.03 & 5,280 & 6.41 \\
\r
635 8,192 & sor & 1,404.53 & 106,530 & 212.55 & 12,990 & 180.97 & 10,470 & 7.76 \\
\r
636 16,384 & mg & 1,430.56 & 63,930 & 237.17 & 8,310 & 244.26 & 7,950 & 6.03 \\
\r
637 16,384 & sor & 2,852.14 & 216,240 & 418.46 & 21,690 & 505.26 & 23,970 & 6.82 \\
\r
641 \caption{Comparison of FGMRES and TSIRM with FGMRES for example ex15 of PETSc with two preconditioners (mg and sor) having 25,000 components per core on Juqueen ($\epsilon_{tsirm}=1e-3$, $max\_iter_{kryl}=30$, $s=12$, $max\_iter_{ls}=15$, $\epsilon_{ls}=1e-40$), time is expressed in seconds.}
\r
646 Table~\ref{tab:03} shows the execution times and the number of iterations of
\r
647 example ex15 of PETSc on the Juqueen architecture. Different numbers of cores
\r
648 are studied ranging from 2,048 up-to 16,383 with the two preconditioners {\it
\r
649 mg} and {\it sor}. For those experiments, the number of components (or
\r
650 unknowns of the problems) per core is fixed at 25,000, also called weak
\r
651 scaling. This number can seem relatively small. In fact, for some applications
\r
652 that need a lot of memory, the number of components per processor requires
\r
653 sometimes to be small. Other parameters for this application are described in
\r
654 the legend of this table.
\r
658 In Table~\ref{tab:03}, we can notice that TSIRM is always faster than
\r
659 FGMRES. The last column shows the ratio between FGMRES and the best version of
\r
660 TSIRM according to the minimization procedure: CGLS or LSQR. Even if we have
\r
661 computed the worst case between CGLS and LSQR, it is clear that TSIRM is always
\r
662 faster than FGMRES. For this example, the multigrid preconditioner is faster
\r
663 than SOR. The gain between TSIRM and FGMRES is more or less similar for the two
\r
664 preconditioners. Looking at the number of iterations to reach the convergence,
\r
665 it is obvious that TSIRM allows the reduction of the number of iterations. It
\r
666 should be noticed that for TSIRM, in those experiments, only the iterations of
\r
667 the Krylov solver are taken into account. Iterations of CGLS or LSQR were not
\r
668 recorded but they are time-consuming. In general, each $max\_iter_{kryl}*s$
\r
669 iterations which corresponds to 30*12, there are $max\_iter_{ls}$ iterations for
\r
670 the least-squares method which corresponds to 15.
\r
672 \begin{figure}[htbp]
\r
674 \includegraphics[width=0.5\textwidth]{nb_iter_sec_ex15_juqueen}
\r
675 \caption{Number of iterations per second with ex15 and the same parameters as in Table~\ref{tab:03} (weak scaling)}
\r
680 In Figure~\ref{fig:01}, the number of iterations per second corresponding to
\r
681 Table~\ref{tab:03} is displayed. It can be noticed that the number of
\r
682 iterations per second of FMGRES is constant whereas it decreases with TSIRM with
\r
683 both preconditioners. This can be explained by the fact that when the number of
\r
684 cores increases, the time for the least-squares minimization step also increases
\r
685 but, generally, when the number of cores increases, the number of iterations to
\r
686 reach the threshold also increases, and, in that case, TSIRM is more efficient
\r
687 to reduce the number of iterations. So, the overall benefit of using TSIRM is
\r
695 \begin{table*}[htbp]
\r
697 \begin{tabular}{|r|r|r|r|r|r|r|r|r|}
\r
700 nb. cores & $\epsilon_{tsirm}$ & \multicolumn{2}{c|}{FGMRES} & \multicolumn{2}{c|}{TSIRM CGLS} & \multicolumn{2}{c|}{TSIRM LSQR} & best gain \\
\r
702 & & Time & \# Iter. & Time & \# Iter. & Time & \# Iter. & \\\hline \hline
\r
703 2,048 & 8e-5 & 108.88 & 16,560 & 23.06 & 3,630 & 22.79 & 3,630 & 4.77 \\
\r
704 2,048 & 6e-5 & 194.01 & 30,270 & 35.50 & 5,430 & 27.74 & 4,350 & 6.99 \\
\r
705 4,096 & 7e-5 & 160.59 & 22,530 & 35.15 & 5,130 & 29.21 & 4,350 & 5.49 \\
\r
706 4,096 & 6e-5 & 249.27 & 35,520 & 52.13 & 7,950 & 39.24 & 5,790 & 6.35 \\
\r
707 8,192 & 6e-5 & 149.54 & 17,280 & 28.68 & 3,810 & 29.05 & 3,990 & 5.21 \\
\r
708 8,192 & 5e-5 & 785.04 & 109,590 & 76.07 & 10,470 & 69.42 & 9,030 & 11.30 \\
\r
709 16,384 & 4e-5 & 718.61 & 86,400 & 98.98 & 10,830 & 131.86 & 14,790 & 7.26 \\
\r
713 \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 ($max\_iter_{kryl}=30$, $s=12$, $max\_iter_{ls}=15$, $\epsilon_{ls}=1e-40$), time is expressed in seconds.}
\r
719 In Table~\ref{tab:04}, some experiments with example ex54 on the Curie
\r
720 architecture are reported. For this application, we fixed $\alpha=0.6$. As it
\r
721 can be seen in that table, the size of the problem has a strong influence on the
\r
722 number of iterations to reach the convergence. That is why we have preferred to
\r
723 change the threshold. If we set it to $1e-3$ as with the previous application,
\r
724 only one iteration is necessary to reach the convergence. So Table~\ref{tab:04}
\r
725 shows the results of different executions with different number of cores and
\r
726 different thresholds. As with the previous example, we can observe that TSIRM is
\r
727 faster than FGMRES. The ratio greatly depends on the number of iterations for
\r
728 FMGRES to reach the threshold. The greater the number of iterations to reach the
\r
729 convergence is, the better the ratio between our algorithm and FMGRES is. This
\r
730 experiment is also a weak scaling with approximately $25,000$ components per
\r
731 core. It can also be observed that the difference between CGLS and LSQR is not
\r
732 significant. Both can be good but it seems not possible to know in advance which
\r
733 one will be the best.
\r
735 Table~\ref{tab:05} shows a strong scaling experiment with example ex54 on the
\r
736 Curie architecture. So, in this case, the number of unknowns is fixed at
\r
737 $204,919,225$ and the number of cores ranges from $512$ to $8192$ with the power
\r
738 of two. The threshold is fixed at $5e-5$ and only the $mg$ preconditioner has
\r
739 been tested. Here again we can see that TSIRM is faster than FGMRES. The
\r
740 efficiency of each algorithm is reported. It can be noticed that the efficiency
\r
741 of FGMRES is better than the TSIRM one except with $8,192$ cores and that its
\r
742 efficiency is greater than one whereas the efficiency of TSIRM is lower than
\r
743 one. Nevertheless, the ratio of TSIRM with any version of the least-squares
\r
744 method is always faster. With $8,192$ cores when the number of iterations is
\r
745 far more important for FGMRES, we can see that it is only slightly more
\r
746 important for TSIRM.
\r
748 In Figure~\ref{fig:02} we report the number of iterations per second for the
\r
749 experiments reported in Table~\ref{tab:05}. This figure highlights that the
\r
750 number of iterations per second is more or less the same for FGMRES and TSIRM
\r
751 with a little advantage for FGMRES. It can be explained by the fact that, as we
\r
752 have previously explained, the iterations of the least-squares steps are not
\r
753 taken into account with TSIRM.
\r
755 \begin{table*}[htbp]
\r
757 \begin{tabular}{|r|r|r|r|r|r|r|r|r|r|r|}
\r
760 nb. cores & \multicolumn{2}{c|}{FGMRES} & \multicolumn{2}{c|}{TSIRM CGLS} & \multicolumn{2}{c|}{TSIRM LSQR} & best gain & \multicolumn{3}{c|}{efficiency} \\
\r
761 \cline{2-7} \cline{9-11}
\r
762 & Time & \# Iter. & Time & \# Iter. & Time & \# Iter. & & FGMRES & TS CGLS & TS LSQR\\\hline \hline
\r
763 512 & 3,969.69 & 33,120 & 709.57 & 5,790 & 622.76 & 5,070 & 6.37 & 1 & 1 & 1 \\
\r
764 1024 & 1,530.06 & 25,860 & 290.95 & 4,830 & 307.71 & 5,070 & 5.25 & 1.30 & 1.21 & 1.01 \\
\r
765 2048 & 919.62 & 31,470 & 237.52 & 8,040 & 194.22 & 6,510 & 4.73 & 1.08 & .75 & .80\\
\r
766 4096 & 405.60 & 28,380 & 111.67 & 7,590 & 91.72 & 6,510 & 4.42 & 1.22 & .79 & .84 \\
\r
767 8192 & 785.04 & 109,590 & 76.07 & 10,470 & 69.42 & 9,030 & 11.30 & .32 & .58 & .56 \\
\r
772 \caption{Comparison of FGMRES and TSIRM for ex54 of PETSc (both with the MG preconditioner) with 204,919,225 components on Curie with different number of cores ($\epsilon_{tsirm}=5e-5$, $max\_iter_{kryl}=30$, $s=12$, $max\_iter_{ls}=15$, $\epsilon_{ls}=1e-40$), time is expressed in seconds.}
\r
777 \begin{figure}[htbp]
\r
779 \includegraphics[width=0.5\textwidth]{nb_iter_sec_ex54_curie}
\r
780 \caption{Number of iterations per second with ex54 and the same parameters as in Table~\ref{tab:05} (strong scaling)}
\r
785 Concerning the experiments some other remarks are interesting.
\r
787 \item We have tested other examples of PETSc (ex29, ex45, ex49). For all these
\r
788 examples, we have also obtained similar gains between GMRES and TSIRM but
\r
789 those examples are not scalable with many cores. In general, we had some
\r
790 problems with more than $4,096$ cores.
\r
791 \item We have tested many iterative solvers available in PETSc. In fact, it is
\r
792 possible to use most of them with TSIRM. From our point of view, the condition
\r
793 to use a solver inside TSIRM is that the solver must have a restart
\r
794 feature. More precisely, the solver must support to be stopped and restarted
\r
795 without decreasing its convergence. That is why with GMRES we stop it when it
\r
796 is naturally restarted (\emph{i.e.} with $m$ the restart parameter). The
\r
797 Conjugate Gradient (CG) and all its variants do not have ``restarted'' version
\r
798 in PETSc, so they are not efficient. They will converge with TSIRM but not
\r
799 quickly because if we compare a normal CG with a CG which is stopped and
\r
800 restarted every 16 iterations (for example), the normal CG will be far more
\r
801 efficient. Some restarted CG or CG variant versions exist and may be
\r
802 interesting to study in future works.
\r
804 %%%*********************************************************
\r
805 %%%*********************************************************
\r
809 %%%*********************************************************
\r
810 %%%*********************************************************
\r
811 \section{Conclusion}
\r
813 %The conclusion goes here. this is more of the conclusion
\r
814 %%%*********************************************************
\r
815 %%%*********************************************************
\r
817 A new two-stage iterative algorithm TSIRM has been proposed in this article,
\r
818 in order to accelerate the convergence of Krylov iterative methods.
\r
819 Our TSIRM proposal acts as a merger between Krylov based solvers and
\r
820 a least-squares minimization step.
\r
821 The convergence of the method has been proven in some situations, while
\r
822 experiments up to 16,394 cores have been led to verify that TSIRM runs
\r
823 5 or 7 times faster than GMRES.
\r
826 For future work, the authors' intention is to investigate other kinds of
\r
827 matrices, problems, and inner solvers. In particular, the possibility
\r
828 to obtain a convergence of TSIRM in situations where the GMRES is divergent will be
\r
829 investigated. The influence of all parameters must be
\r
830 tested too, while other methods to minimize the residuals must be regarded. The
\r
831 number of outer iterations to minimize should become adaptive to improve the
\r
832 overall performances of the proposal. Finally, this solver will be implemented
\r
833 inside PETSc, which would be of interest as it would allows us to test
\r
834 all the non-linear examples and compare our algorithm with the other algorithm
\r
835 implemented in PETSc.
\r
838 % conference papers do not normally have an appendix
\r
842 % use section* for acknowledgement
\r
843 %%%*********************************************************
\r
844 %%%*********************************************************
\r
845 \section*{Acknowledgment}
\r
846 This paper is partially funded by the Labex ACTION program (contract
\r
847 ANR-11-LABX-01-01). We acknowledge PRACE for awarding us access to resources
\r
848 Curie and Juqueen respectively based in France and Germany.
\r
854 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\r
856 \bibliography{biblio}
\r
857 \bibliographystyle{unsrt}
\r
858 \bibliographystyle{alpha}
\r
