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diff --git a/IJHPCN/paper.tex b/IJHPCN/paper.tex
index 21fa922b10633c9b24d1edaff5a87d8544c24908..4c711cc7abc41be4172091ffe50c41dd3ede076a 100644 (file)
--- a/IJHPCN/paper.tex
+++ b/IJHPCN/paper.tex
@@ -776,25 +776,6 @@ taken into account with TSIRM.
 \end{figure}\r
 \r
 \r
 \end{figure}\r
 \r
 \r

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

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@@ -803,26 +784,6 @@ Concerning the  experiments some  other remarks are  interesting.
 \r
 \subsection{Nonlinear problems in parallel}\r
 \r
 \r
 \subsection{Nonlinear problems in parallel}\r
 \r

-\begin{table*}[htbp]\r
-\begin{center}\r
-\begin{tabular}{|r|r|r|r|r|r|r|r|} \r
-\hline\r
-\r
-  nb. cores   & \multicolumn{2}{c|}{FGMRES/ASM} & \multicolumn{2}{c|}{TSIRM CGLS/ASM} & gain& \multicolumn{2}{c|}{FGMRES/HYPRE}   \\ \r
-\cline{2-5} \cline{7-8}\r
-                    & Time  & \# Iter.  & Time  & \# Iter. &        & Time  & \# Iter.   \\\hline \hline\r
-   512              & 5.54      & 685    & 2.5 &       570 & 2.21   & 128.9 & 9     \\\r
-   2048             & 14.95     & 1,560  &  4.32 &     746 & 3.48   & 335.7 & 9 \\\r
-   4096             & 25.13    & 2,369   & 5.61 &   859    & 4.48   & >1000  & -- \\\r
-   8192             & 44.35   & 3,197   &  7.6  &  1083    &  5.84  & >1000 &  --   \\\r
-\r
-\hline\r
-\r
-\end{tabular}\r
-\caption{Comparison of FGMRES  and TSIRM for ex45 of PETSc/KSP with two preconditioner (ASM and HYPRE)  having 25,000 components per core on Curie ($\epsilon_{tsirm}=1e-10$, $max\_iter_{kryl}=30$, $s=12$, $max\_iter_{ls}=15$, $\epsilon_{ls}=1e-40$),  time is expressed in seconds.}\r
-\label{tab:06}\r
-\end{center}\r
-\end{table*}\r

 \r
 \r
 \begin{figure}[htbp]\r
 \r
 \r
 \begin{figure}[htbp]\r


@@ -877,10 +838,39 @@ Concerning the  experiments some  other remarks are  interesting.
 \end{table*}\r
 \r
 \r
 \end{table*}\r
 \r
 \r

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

 \r
 %%ENDNEW\r
 \r
 \r
 %%ENDNEW\r
 \r

+\r

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 \section{Conclusion}\r
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 \section{Conclusion}\r