-problems) per processor is fixed to 25,000. 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.
-
-In this Table, we can notice that TSARM is always faster than FGMRES. The last
-column shows the ratio between FGMRES and the best version of TSARM according to
-the minimization procedure: CGLS or LSQR.
-
-
-\begin{figure}
+problems) per processor 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.
+
+In this Table, we can notice that TSIRM is always faster than FGMRES. The last
+column shows the ratio between FGMRES and the best version of TSIRM according to
+the minimization procedure: CGLS or LSQR. Even if we have computed the worst
+case between CGLS and LSQR, it is clear that TSIRM is alsways faster than
+FGMRES. For this example, the multigrid preconditionner is faster than SOR. The
+gain between TSIRM and FGMRES is more or less similar for the two
+preconditioners.
+
+In Figure~\ref{fig:01}, the number of iterations per second corresponding to
+Table~\ref{tab:01} is displayed. It should be noticed that for TSIRM, only the
+iterations of the Krylov solver are taken into account. Iterations of CGLS or
+LSQR are not recorded but they are time-consuming. It can be noticed that the
+number of iterations per second of FMGRES is constant whereas it decrease with
+TSIRM with both preconditioner. This can be explained by the fact that when the
+number of core increases the time for the minimization step also increases but
+it is also more efficient to reduce the number of iterations.
+
+
+\begin{figure}[htbp]
