aMerge branch 'master' of ssh://bilbo.iut-bm.univ-fcomte.fr/GMRES2stage

[GMRES2stage.git] / paper.tex

diff --git a/paper.tex b/paper.tex
index f2114e57fa56c7229013a9c1f5f52b112264cfaa..00bf7b7587752cf1b2a5291e640338eac1f0489d 100644 (file)
--- a/paper.tex
+++ b/paper.tex
@@ -982,8 +982,7 @@ preconditioner in PETSc please consult~\cite{petsc-web-page}.
 
 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


@@ -1050,8 +1049,40 @@ 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}


@@ -1082,6 +1113,12 @@ 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. 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. 

 %%%*********************************************************
 %%%*********************************************************
 
 %%%*********************************************************
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@@ -1104,13 +1141,14 @@ 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