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[GMRES2stage.git] / paper.tex

diff --git a/paper.tex b/paper.tex
index d114bdd53ae94f8c783ed289ed9747e70f167d62..87640739334500189fe37d1e42f428902550e45f 100644 (file)
--- a/paper.tex
+++ b/paper.tex
@@ -381,7 +381,7 @@
 % affiliations
 
 \author{\IEEEauthorblockN{Rapha\"el Couturier\IEEEauthorrefmark{1}, Lilia Ziane Khodja\IEEEauthorrefmark{2}, and Christophe Guyeux\IEEEauthorrefmark{1}}
 % 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\\
+\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}
 Email: \{raphael.couturier,christophe.guyeux\}@univ-fcomte.fr}
 \IEEEauthorblockA{\IEEEauthorrefmark{2} INRIA Bordeaux Sud-Ouest, France\\
 Email: lilia.ziane@inria.fr}


@@ -439,7 +439,7 @@ GMRES.
 \end{abstract}
 
 \begin{IEEEkeywords}
 \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}
 
 
 \end{IEEEkeywords}
 
 


@@ -547,38 +547,42 @@ 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)
 
 % 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
 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
 
 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


@@ -618,22 +622,29 @@ 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-squares minimization  on the residuals computed by  the inner one. %Tsolver which does not required to be changed.
 
 inner solver is a Krylov based one. In order to accelerate its convergence, the 
 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 
-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  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 $S_i$.
+
+$\|r_n\| \leq \left( 1-\frac{\lambda_{\mathrm{min}}^2(1/2(A^T + A))}{ \lambda_{\mathrm{max}}(A^T A)} \right)^{n/2} \|r_0\|,$
+In the general case, where A is not positive definite, we have

 
 

-At each $s$ iterations, the minimization step is applied in order to
+$\|r_n\| \le \inf_{p \in P_n} \|p(A)\| \le \kappa_2(V) \inf_{p \in P_n} \max_{\lambda \in \sigma(A)} |p(\lambda)| \|r_0\|, \,$
+
+
+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}
 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  $\mathbb{R}^{n\times s}$,
 
 
 In  practice, $R$  is a  dense rectangular  matrix belonging in  $\mathbb{R}^{n\times s}$,


@@ -650,9 +661,8 @@ appropriate than a single direct method in a parallel context.
   \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}
   \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}
+    \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 $\alpha=Least\_Squares(R,b,max\_iter_{ls})$ \label{algo:}
     \If {$k \mod s=0$ {\bf and} error$>\epsilon_{kryl}$}
       \State $R=AS$ \Comment{compute dense matrix} \label{algo:matrix_mul}
             \State $\alpha=Least\_Squares(R,b,max\_iter_{ls})$ \label{algo:}


@@ -663,18 +673,22 @@ appropriate than a single direct method in a parallel context.
 \label{algo:01}
 \end{algorithm}
 
 \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  $S$, where $S$ is a matrix of size $n\times s$ whose column vector $i$ is denoted by $S_i$. 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}
 
 Let us summarize the most important parameters of TSIRM:
 \begin{itemize}


@@ -735,7 +749,7 @@ these operations are easy to implement in PETSc or similar environment.
 
 \section{Convergence results}
 \label{sec:04}
 
 \section{Convergence results}
 \label{sec:04}

-Let us recall the following result, see~\cite{Saad86}.
+Let us recall the following result, see~\cite{Saad86} for further readings.

 \begin{proposition}
 \label{prop:saad}
 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:
 \begin{proposition}
 \label{prop:saad}
 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:


@@ -796,10 +810,12 @@ than the one of the GMRES method.
 
 
 In order to see the influence of our algorithm with only one processor, we first
 
 
 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.
+show a comparison with GMRES or FGMRES and our algorithm. In Table~\ref{tab:01},
+we  show the  matrices we  have  used and  some of  them characteristics.  Those
+matrices  are   chosen  from   the  Davis  collection   of  the   University  of
+Florida~\cite{Dav97}. They are matrices arising in real-world applications.  For
+all the  matrices, the name,  the field,  the number of  rows and the  number of
+nonzero elements are given.

 
 \begin{table}[htbp]
 \begin{center}
 
 \begin{table}[htbp]
 \begin{center}


@@ -832,13 +848,14 @@ 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
 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 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.
+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]
 
 
 \begin{table}[htbp]


@@ -868,7 +885,7 @@ torso3             & fgmres / sor  & 37.70 & 565 & 34.97 & 510 \\
 
 
 
 
 
 

-In order to perform larger  experiments, we have tested some example applications
+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}
 of PETSc. Those  applications are available in the ksp part  which is suited for
 scalable linear equations solvers:
 \begin{itemize}


@@ -881,11 +898,23 @@ scalable linear equations solvers:
   finite elements. For this example, the user can define the scaling of material
   coefficient in embedded circle called $\alpha$.
 \end{itemize}
   finite elements. For this example, the user can define the scaling of material
   coefficient in embedded circle called $\alpha$.
 \end{itemize}

-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.
+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 the following larger experiments are described on two large scale architectures: Curie and Juqeen... {\bf description...}\\

 
 
 {\bf Description of preconditioners}\\
 
 
 {\bf Description of preconditioners}\\


@@ -1029,13 +1058,22 @@ In Table~\ref{tab:04}, some experiments with example ex54 on the Curie architect
 %%%*********************************************************
 %%%*********************************************************
 
 %%%*********************************************************
 %%%*********************************************************
 

-
-future plan : \\
-- study other kinds of matrices, problems, inner solvers\\
-- test the influence of all parameters\\
-- adaptative number of outer iterations to minimize\\
-- other methods to minimize the residuals?\\
-- implement our solver inside PETSc
+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.
+
+
+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
 
 
 % conference papers do not normally have an appendix