X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/GMRES2stage.git/blobdiff_plain/eacf4c2eeca7315f0a4a7bc9dec99dc6778843d1..c1552aed123e4154c36983b72f5abd7c1578091f:/paper.tex

diff --git a/paper.tex b/paper.tex
index 4d59b93..a4c9b26 100644
--- a/paper.tex
+++ b/paper.tex
@@ -1035,7 +1035,21 @@ the number of iterations. So, the overall benefit of using TSIRM is interesting.
 \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.
 
 
 \begin{table*}[htbp]
