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[GMRES2stage.git] / paper.tex

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--- a/paper.tex
+++ b/paper.tex
@@ -601,7 +601,13 @@ is summarized while intended perspectives are provided.
 %%%*********************************************************
 \section{Related works}
 \label{sec:02} 
 %%%*********************************************************
 \section{Related works}
 \label{sec:02} 

-%Wherever Times is specified, Times Roman or Times New Roman may be used. If neither is available on your system, please use the font closest in appearance to Times. Avoid using bit-mapped fonts if possible. True-Type 1 or Open Type fonts are preferred. Please embed symbol fonts, as well, for math, etc.
+%GMRES method is one of the most widely used iterative solvers chosen to deal with the sparsity and the large order of linear systems. It was initially developed by Saad \& al.~\cite{Saad86} to deal with non-symmetric and non-Hermitian problems, and indefinite symmetric problems too. The convergence of the restarted GMRES with preconditioning is faster and more stable than those of some other iterative solvers. 
+
+%The next two chapters explore a few methods which are considered currently to be among the most important iterative techniques available for solving large linear systems. These techniques are based on projection processes, both orthogonal and oblique, onto Krylov subspaces, which are subspaces spanned by vectors of the form p(A)v where p is a polynomial. In short, these techniques approximate A −1 b by p(A)b, where p is a “good” polynomial. This chapter covers methods derived from, or related to, the Arnoldi orthogonalization. The next chapter covers methods based on Lanczos biorthogonalization.
+
+%Krylov subspace techniques have inceasingly been viewed as general purpose iterative methods, especially since the popularization of the preconditioning techniqes.
+
+%Preconditioned Krylov-subspace iterations are a key ingredient in many modern linear solvers, including in solvers that employ support preconditioners. 

 %%%*********************************************************
 %%%*********************************************************
 
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@@ -654,10 +660,10 @@ appropriate than a single direct method in a parallel context.
   \Input $A$ (sparse matrix), $b$ (right-hand side)
   \Output $x$ (solution vector)\vspace{0.2cm}
   \State Set the initial guess $x_0$
   \Input $A$ (sparse matrix), $b$ (right-hand side)
   \Output $x$ (solution vector)\vspace{0.2cm}
   \State Set the initial guess $x_0$

-  \For {$k=1,2,3,\ldots$ until convergence (error$<\epsilon_{tsirm}$)} \label{algo:conv}
+  \For {$k=1,2,3,\ldots$ until convergence ($error<\epsilon_{tsirm}$)} \label{algo:conv}

     \State  $[x_k,error]=Solve(A,b,x_{k-1},max\_iter_{kryl})$   \label{algo:solve}
     \State  $[x_k,error]=Solve(A,b,x_{k-1},max\_iter_{kryl})$   \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 $S_{k \mod s}=x_k$ \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:}
       \State $x_k=S\alpha$  \Comment{compute new solution}
       \State $R=AS$ \Comment{compute dense matrix} \label{algo:matrix_mul}
             \State $\alpha=Least\_Squares(R,b,max\_iter_{ls})$ \label{algo:}
       \State $x_k=S\alpha$  \Comment{compute new solution}


@@ -675,10 +681,10 @@ 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
 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 $||Ax^k-b||_2$.
+which is defined by $||Ax_k-b||_2$.

 
   Line~\ref{algo:store},
 
   Line~\ref{algo:store},

-$S_{k \mod  s}=x^k$ consists in  copying the solution  $x_k$ into the  column $k
+$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
 \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


@@ -823,13 +829,13 @@ which concludes the induction and the proof.
 \label{sec:05}
 
 
 \label{sec:05}
 
 

-In order to see the influence of our algorithm with only one processor, we first
-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.
+In order to see the behavior of the proposal when considering only one processor, a first
+comparison with GMRES or FGMRES and the new algorithm detailed previously has been experimented. 
+Matrices that have been used with their characteristics (names, fields, rows, and nonzero coefficients) are detailed in 
+Table~\ref{tab:01}.  These latter, which are real-world applications matrices, 
+have been extracted 
+ from   the  Davis  collection,   University  of
+Florida~\cite{Dav97}.

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


@@ -849,8 +855,9 @@ torso3             & 2D/3D problem & 259,156 & 4,429,042 \\
 \label{tab:01}
 \end{center}
 \end{table}
 \label{tab:01}
 \end{center}
 \end{table}

-
-The following  parameters have been chosen  for our experiments.   As by default
+Chosen parameters are detailed below.
+%The following  parameters have been chosen  for our experiments.   
+As by default

 the restart  of GMRES is performed every  30 iterations, we have  chosen to stop
 the GMRES every 30 iterations (\emph{i.e.} $max\_iter_{kryl}=30$).  $s$ is set to 8. CGLS is
 chosen  to minimize  the least-squares  problem with  the  following parameters:
 the restart  of GMRES is performed every  30 iterations, we have  chosen to stop
 the GMRES every 30 iterations (\emph{i.e.} $max\_iter_{kryl}=30$).  $s$ is set to 8. CGLS is
 chosen  to minimize  the least-squares  problem with  the  following parameters:


@@ -930,8 +937,16 @@ by core. The Juqueen architecture is composed  of IBM PowerPC A2 at 1.6 GHz with
 speed network.
 
 
 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}
 
 \begin{table*}[htbp]
 \begin{center}


@@ -959,8 +974,7 @@ speed network.
 
 Table~\ref{tab:03} shows  the execution  times and the  number of  iterations of
 example ex15  of PETSc on the  Juqueen architecture. Different  numbers of cores
 
 Table~\ref{tab:03} shows  the execution  times and the  number of  iterations of
 example ex15  of PETSc on the  Juqueen architecture. Different  numbers of cores

-are  studied ranging  from  2,048  up-to 16,383.   Two  preconditioners have  been
-tested: {\it mg} and {\it sor}.   For those experiments,  the number  of components  (or unknowns  of the
+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
 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


@@ -1027,8 +1041,40 @@ the number of iterations. So, the overall benefit of using TSIRM is interesting.
 \end{table*}
 
 
 \end{table*}
 
 

-In Table~\ref{tab:04}, some experiments with example ex54 on the Curie architecture are reported.
-
+In  Table~\ref{tab:04},  some  experiments   with  example  ex54  on  the  Curie
+architecture are reported.  For this  application, we fixed $\alpha=0.6$.  As it
+can be seen in that Table, the size of the problem has a strong influence on the
+number of iterations to reach the  convergence. That is why we have preferred to
+change the threshold.  If we set  it to $1e-3$ as with the previous application,
+only one iteration is necessray  to reach the convergence. So Table~\ref{tab:04}
+shows the results  of differents executions with differents  number of cores and
+differents thresholds. As  with the previous example, we  can observe that TSIRM
+is faster than FGMRES. The ratio greatly depends on the number of iterations for
+FMGRES to reach the threshold. The greater the number of iterations to reach the
+convergence is, the  better the ratio between our algorithm  and FMGRES is. This
+experiment is  also a  weak scaling with  approximately $25,000$  components per
+core. It can also  be observed that the difference between CGLS  and LSQR is not
+significant. Both can be good but it seems not possible to know in advance which
+one will be the best.
+
+Table~\ref{tab:05} show a strong scaling experiment with the exemple ex54 on the
+Curie  architecture. So  in  this case,  the  number of  unknownws  is fixed  to
+$204,919,225$ and the number of cores ranges from $512$ to $8192$ with the power
+of two.  The  threshold is fixed to $5e-5$ and only  the $mg$ preconditioner has
+been tested. Here  again we can see that TSIRM is  faster that FGMRES. Efficiecy
+of each algorithms is reported. It  can be noticed that FGMRES is more efficient
+than TSIRM except with $8,192$ cores and that its efficiency is greater that one
+whereas the  efficiency of TSIRM is  lower than one. Nevertheless,  the ratio of
+TSIRM  with any  version  of the  least-squares  method is  always faster.  With
+$8,192$ cores when the number of iterations is far more important for FGMRES, we
+can see that it is only slightly more important for TSIRM.
+
+In  Figure~\ref{fig:02}  we report  the  number  of  iterations per  second  for
+experiments  reported in  Table~\ref{tab:05}.  This Figure  highlights that  the
+number of iterations per  seconds is more of less the same  for FGMRES and TSIRM
+with a little advantage for FGMRES. It  can be explained by the fact that, as we
+have previously explained, that the iterations of the least-sqaure steps are not
+taken into account with TSIRM.

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


@@ -1059,6 +1105,26 @@ In Table~\ref{tab:04}, some experiments with example ex54 on the Curie architect
 \label{fig:02}
 \end{figure}
 
 \label{fig:02}
 \end{figure}
 

+
+Concerning the  experiments some  other remarks are  interesting.
+\begin{itemize}
+\item We  can tested other examples of  PETSc (ex29, ex45, ex49).  For all these
+  examples,  we also obtained  similar gain  between GMRES  and TSIRM  but those
+  examples are  not scalable with many  cores. In general, we  had some problems
+  with more than $4,096$ cores.
+\item We have tested many iterative  solvers available in PETSc.  In fast, it is
+  possible to use most of them with TSIRM. From our point of view, the condition
+  to  use  a  solver inside  TSIRM  is  that  the  solver  must have  a  restart
+  feature. More  precisely, the solver must  support to be  stoped and restarted
+  without decrease its  converge. That is why  with GMRES we stop it  when it is
+  naturraly  restarted (i.e.  with  $m$ the  restart parameter).   The Conjugate
+  Gradient (CG) and all its variants do not have ``restarted'' version in PETSc,
+  so they  are not  efficient.  They  will converge with  TSIRM but  not quickly
+  because if  we compare  a normal CG  with a CG  for which  we stop it  each 16
+  iterations  for example,  the  normal CG  will  be for  more efficient.   Some
+  restarted CG  or CG variant versions exist  and may be interested  to study in
+  future works.
+\end{itemize}

 %%%*********************************************************
 %%%*********************************************************
 
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 %%%*********************************************************
 


@@ -1081,13 +1147,14 @@ experiments up to 16,394 cores have been led to verify that TSIRM runs
 5 or  7 times  faster than GMRES.
 
 
 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.
+For  future  work, the  authors'  intention is  to  investigate  other kinds  of
+matrices, problems, and  inner solvers. The influence of  all parameters must be
+tested too, while other methods to minimize the residuals must be regarded.  The
+number of outer  iterations to minimize should become  adaptative to improve the
+overall performances of the proposal.   Finally, this solver will be implemented
+inside PETSc. This  would be very interesting because it would  allow us to test
+all the non-linear  examples and compare our algorithm  with the other algorithm
+implemented in PETSc.

 
 
 % conference papers do not normally have an appendix
 
 
 % conference papers do not normally have an appendix