X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/GMRES2stage.git/blobdiff_plain/23054d081ea510f87cf5c9d8ed9351034ffa8f45..f9c41ae8b1dcb1ffcddbc25c953cf8faa1aa9ed8:/paper.tex

diff --git a/paper.tex b/paper.tex
index c183ea4..fa5aeb7 100644
--- a/paper.tex
+++ b/paper.tex
@@ -548,7 +548,7 @@ Iterative Krylov methods; sparse linear systems; two stage iteration; least-squa
 % (should never be an issue)
 
 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
+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
@@ -611,19 +611,19 @@ equations arising from very large and complex problems.
 GMRES is one of the most  widely used Krylov iterative method for solving sparse
 and large  linear systems. It  has been developed by  Saad \emph{et al.}~\cite{Saad86}  as a
 generalized  method to  deal with  unsymmetric and  non-Hermitian  problems, and
-indefinite symmetric problems too. In its original version called full GMRES, it
+indefinite symmetric problems too. In its original version called full GMRES, this algorithm
 minimizes the residual over the  current Krylov subspace until convergence in at
-most $n$ iterations,  where $n$ is the  size of the sparse matrix.  It should be
-noticed that full GMRES is too expensive in the case of large matrices since the
+most $n$ iterations,  where $n$ is the  size of the sparse matrix.  
+Full GMRES is however too much expensive in the case of large matrices, since the
 required orthogonalization  process per  iteration grows quadratically  with the
-number of iterations. For that reason, in practice GMRES is restarted after each
-$m\ll n$ iterations to avoid the  storage of a large orthonormal basis. However,
+number of iterations. For that reason, GMRES is restarted in practice after each
+$m\ll n$ iterations, to avoid the  storage of a large orthonormal basis. However,
 the  convergence behavior  of the  restarted GMRES,  called GMRES($m$),  in many
-cases depends quite critically on  the value of $m$~\cite{Huang89}. Therefore in
+cases depends quite critically on  the $m$ value~\cite{Huang89}. Therefore in
 most cases, a preconditioning technique is applied to the restarted GMRES method
 in order to improve its convergence.
 
-In order to enhance the robustness of Krylov iterative solvers, some techniques have been proposed allowing the use of different preconditioners, if necessary, within the iteration instead of restarting. Those techniques may lead to considerable savings in CPU time and memory requirements. Van der Vorst in~\cite{Vorst94} has proposed variants of the GMRES algorithm in which a different preconditioner is applied in each iteration, so-called GMRESR family of nested methods. In fact, the GMRES method is effectively preconditioned with other iterative schemes (or GMRES itself), where the iterations of the GMRES method are called outer iterations while the iterations of the preconditioning process referred to as inner iterations. Saad in~\cite{Saad:1993} has proposed FGMRES which is another variant of the GMRES algorithm using a variable preconditioner. In FGMRES the search directions are preconditioned whereas in GMRESR the residuals are preconditioned. However in practice the good preconditioners are those based on direct methods, as ILU preconditioners, which are not easy to parallelize and suffer from the scalability problems on large clusters of thousands of cores.  
+To enhance the robustness of Krylov iterative solvers, some techniques have been proposed allowing the use of different preconditioners, if necessary, within the iteration instead of restarting. Those techniques may lead to considerable savings in CPU time and memory requirements. Van der Vorst in~\cite{Vorst94} has for instance proposed variants of the GMRES algorithm in which a different preconditioner is applied in each iteration, leading to the so-called GMRESR family of nested methods. In fact, the GMRES method is effectively preconditioned with other iterative schemes (or GMRES itself), where the iterations of the GMRES method are called outer iterations while the iterations of the preconditioning process is referred to as inner iterations. Saad in~\cite{Saad:1993} has proposed Flexible GMRES (FGMRES) which is another variant of the GMRES algorithm using a variable preconditioner. In FGMRES the search directions are preconditioned whereas in GMRESR the residuals are preconditioned. However, in practice, good preconditioners are those based on direct methods, as ILU preconditioners, which are not easy to parallelize and suffer from the scalability problems on large clusters of thousands of cores.  
 
 Recently, communication-avoiding methods have been developed to reduce the communication overheads in Krylov subspace iterative solvers. On modern computer architectures, communications between processors are much slower than floating-point arithmetic operations on a given processor. Communication-avoiding techniques reduce either communications between processors or data movements between levels of the memory hierarchy, by reformulating the communication-bound kernels (more frequently SpMV kernels) and the orthogonalization operations within the Krylov iterative solver. Different works have studied the communication-avoiding techniques for the GMRES method, so-called CA-GMRES, on multicore processors and multi-GPU machines~\cite{Mohiyuddin2009,Hoemmen2010,Yamazaki2014}. 
 
@@ -705,7 +705,7 @@ 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 $||Ax_k-b||_2$.
+$error$, 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
@@ -716,7 +716,7 @@ $error$ which is defined by $||Ax_k-b||_2$.
 
 Let us summarize the most important parameters of TSIRM:
 \begin{itemize}
-\item $\epsilon_{tsirm}$: the threshold to stop the TSIRM method;
+\item $\epsilon_{tsirm}$: the threshold that stops 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-squares method;
@@ -727,9 +727,9 @@ Let us summarize the most important parameters of TSIRM:
 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
+our   code,   we   have   chosen  to   use   PETSc~\cite{petsc-web-page}.    In
+line~\ref{algo:matrix_mul}, the  matrix-matrix multiplication is  implemented and
+efficient since the  matrix $A$ is sparse and the  matrix $S$ contains few
 columns in  practice. As explained  previously, at least  two methods seem  to be
 interesting to solve the least-squares minimization, CGLS and LSQR.
 
@@ -764,7 +764,7 @@ the parallelization of CGLS which is  similar to LSQR.
 
 
 In each iteration  of CGLS, there is two  matrix-vector multiplications and some
-classical  operations:  dot  product,   norm,  multiplication  and  addition  on
+classical  operations:  dot  product,   norm,  multiplication,  and  addition  on
 vectors.  All  these  operations are  easy  to  implement  in PETSc  or  similar
 environment.  It should be noticed that LSQR follows the same principle, it is a
 little bit longer but it performs more or less the same operations.
@@ -846,8 +846,11 @@ $\begin{array}{ll}
 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.
+Remark that a similar proposition can be formulated at each time
+the given solver satisfies an inequality of the form $||r_n|| \leqslant \mu^n ||r_0||$,
+with $|\mu|<1$. Furthermore, it is \emph{a priori} possible in some particular cases 
+regarding $A$, 
+that the proposed TSIRM converges while the GMRES($m$) does not.
 
 %%%*********************************************************
 %%%*********************************************************
@@ -881,24 +884,24 @@ torso3             & 2D/3D problem & 259,156 & 4,429,042 \\
 \label{tab:01}
 \end{center}
 \end{table}
-Chosen parameters  are detailed below.   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:
+Chosen parameters  are detailed below.   
+We have  stopped  the  GMRES every  30
+iterations (\emph{i.e.}, $max\_iter_{kryl}=30$), which is the default 
+setting of GMRES restart parameter.  $s$, for its part, has been set to 8. CGLS 
+ minimizes  the   least-squares  problem   with  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)
-Core(TM) i7-3630QM CPU @ 2.40GHz with the version 3.5.1 of PETSc.
+$\epsilon_{tsirm}=1e-10$.  These  experiments have been performed  on an Intel(R)
+Core(TM) i7-3630QM CPU @ 2.40GHz with the 3.5.1 version  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 TSIRM
-are given. In the  second column, it can be noticed that  either GMRES or FGMRES
-(Flexible GMRES)~\cite{Saad:1993} is used to solve the linear system.  According
-to  the matrices,  different preconditioners  are  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
+Experiments comparing 
+a GMRES variant with TSIRM in the resolution of linear systems are given in  Table~\ref{tab:02}. 
+The  second column describes whether GMRES or FGMRES has been used for linear systems solving.  
+Different preconditioners  have been used according to the matrices.  With  TSIRM, the  same
+solver and the  same preconditioner are used.  This table  shows that TSIRM can
+drastically reduce  the number of iterations needed 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 two parameters: the number of iterations to stop GMRES
+fact this also depends on two parameters: the number of iterations before stopping GMRES
 and the number of iterations to perform the minimization.
 
 
@@ -920,7 +923,7 @@ torso3             & fgmres / sor  & 37.70 & 565 & 34.97 & 510 \\
 \hline
 
 \end{tabular}
-\caption{Comparison of (F)GMRES and TSIRM with (F)GMRES in sequential with some matrices, time is expressed in seconds.}
+\caption{Comparison between sequential standalone (F)GMRES and TSIRM with (F)GMRES (time in seconds).}
 \label{tab:02}
 \end{center}
 \end{table}
@@ -930,43 +933,44 @@ torso3             & fgmres / sor  & 37.70 & 565 & 34.97 & 510 \\
 
 
 In order to perform larger experiments, we have tested some example applications
-of  PETSc. Those  applications are  available in  the \emph{ksp}  part  which is
+of  PETSc. These  applications are  available in  the \emph{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
+\item ex15  is an example  that solves in  parallel an operator using  a finite
   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, 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
+\item ex54 is another example based on a 2D problem discretized with quadrilateral
+  finite elements. In 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
+to read  the codes  available in  the PETSc sources.   These problems  have been
 chosen because they are scalable with many  cores.
 
 In  the  following   larger  experiments  are  described  on   two  large  scale
-architectures: Curie  and Juqueen.   Both these architectures  are supercomputer
+architectures: Curie  and Juqueen.   Both these architectures  are supercomputers
 respectively  composed  of  80,640  cores   for  Curie  and  458,752  cores  for
 Juqueen. Those  machines are respectively hosted  by GENCI in  France and JÃ¼lich
-Supercomputing Centre in Germany.  They belongs with other similar architectures
-of the  PRACE initiative (Partnership  for Advanced Computing  in Europe) which
+Supercomputing Center in Germany.  They belong 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.
+processors  at 2.7  GHz with  2Gb memory  by core.  The Juqueen  architecture,
+for its part, is
+composed by IBM PowerPC  A2 at  1.6 GHz with  1Gb memory  per core.  Both those
+architectures are equipped 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
+However, 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
+preconditioners, but  as it is  not the subject  of this paper, we  will not
 present results with many preconditioners. In  practice, 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}.
+multigrid (mg)  and successive  over-relaxation (sor). For  further details  on the
+preconditioners in PETSc, reader is referred to~\cite{petsc-web-page}.
 
 
 
@@ -989,7 +993,7 @@ preconditioner in PETSc please consult~\cite{petsc-web-page}.
 \hline
 
 \end{tabular}
-\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 ($\epsilon_{tsirm}=1e-3$, $max\_iter_{kryl}=30$, $s=12$, $max\_iter_{ls}=15$, $\epsilon_{ls}=1e-40$),  time is expressed in seconds.}
+\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.}
 \label{tab:03}
 \end{center}
 \end{table*}
@@ -1002,7 +1006,7 @@ 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. Other parameters  for this application  are described in
-the legend of this Table.
+the legend of this table.
 
 
 
@@ -1068,7 +1072,7 @@ the number of iterations. So, the overall benefit of using TSIRM is interesting.
 
 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
+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 necessary  to reach the convergence. So Table~\ref{tab:04}
@@ -1082,14 +1086,14 @@ 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
+Table~\ref{tab:05} shows a strong scaling experiment with the exemple ex54 on the
+Curie  architecture. So  in  this case,  the  number of  unknowns  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. Efficiency
+been tested. Here again we can  see that TSIRM is faster than FGMRES. Efficiency
 of each algorithm  is reported. It can be noticed that  the efficiency of FGMRES
 is better than  the TSIRM one except with $8,192$ cores  and that its efficiency
-is  greater   that  one   whereas  the  efficiency   of  TSIRM  is   lower  than
+is  greater   than 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
@@ -1097,7 +1101,7 @@ 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 second is more  of less the same for  FGMRES and TSIRM
+number of iterations  per second is more  or 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-squares steps are not
 taken into account with TSIRM.
@@ -1135,20 +1139,20 @@ taken into account with TSIRM.
 Concerning the  experiments some  other remarks are  interesting.
 \begin{itemize}
 \item We  have tested other examples of  PETSc (ex29, ex45, ex49).  For all these
-  examples,  we also obtained  similar gain  between GMRES  and TSIRM  but those
+  examples,  we also obtained  similar gains  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 fact, 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  stopped and restarted
-  without decrease its  converge. That is why  with GMRES we stop it  when it is
-  naturally  restarted (i.e.  with  $m$ the  restart parameter).   The Conjugate
+  without decrease its  convergence. That is why  with GMRES we stop it  when it is
+  naturally  restarted (\emph{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
+  iterations  for example,  the  normal CG  will  be far  more efficient.   Some
+  restarted CG  or CG variant versions exist  and may be interesting  to study in
   future works.
 \end{itemize}
 %%%*********************************************************
@@ -1164,8 +1168,8 @@ Concerning the  experiments some  other remarks are  interesting.
 %%%*********************************************************
 %%%*********************************************************
 
-A novel two-stage iterative  algorithm has been proposed in this article,
-in order to accelerate the convergence Krylov iterative  methods.
+A novel two-stage iterative  algorithm TSIRM has been proposed in this article,
+in order to accelerate the convergence of 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 
@@ -1174,11 +1178,13 @@ experiments up to 16,394 cores have been led to verify that TSIRM runs
 
 
 For  future  work, the  authors'  intention is  to  investigate  other kinds  of
-matrices, problems, and  inner solvers. The influence of  all parameters must be
+matrices, problems, and  inner solvers. In particular, the possibility 
+to obtain a convergence of TSIRM in situations where the GMRES is divergent will be
+investigated. 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
+number of outer  iterations to minimize should become  adaptive 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
+inside PETSc, which would be of interest as it would  allows us to test
 all the non-linear  examples and compare our algorithm  with the other algorithm
 implemented in PETSc.
 
