From c58c00531fe9eae1fa885f5ab10b3308d38feb6d Mon Sep 17 00:00:00 2001 From: lilia Date: Fri, 10 Oct 2014 13:33:52 +0200 Subject: [PATCH] 10-10-2014 10 --- paper.tex | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/paper.tex b/paper.tex index d660010..cba14da 100644 --- a/paper.tex +++ b/paper.tex @@ -846,7 +846,7 @@ chosen because they are scalable with many cores which is not the case of other In the following larger experiments are described on two large scale architectures: Curie and Juqeen... {\bf description...}\\ -{\bf Description of preconditioners} +{\bf Description of preconditioners}\\ \begin{table*}[htbp] \begin{center} @@ -867,15 +867,15 @@ In the following larger experiments are described on two large scale architectur \hline \end{tabular} -\caption{Comparison of FGMRES and TSIRM with FGMRES for example ex15 of PETSc with two preconditioner (mg and sor) with 25,000 components per core on Juqueen (threshold 1e-3, restart=30, s=12), time is expressed in seconds.} +\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 (threshold 1e-3, restart=30, s=12), time is expressed in seconds.} \label{tab:03} \end{center} \end{table*} Table~\ref{tab:03} shows the execution times and the number of iterations of -example ex15 of PETSc on the Juqueen architecture. Differents number of cores -are studied rangin from 2,048 upto 16,383. Two preconditioners have been -tested. For those experiments, the number of components (or unknown of the +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 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 @@ -883,11 +883,11 @@ small. -In this Table, we can notice that TSIRM is always faster than FGMRES. The last +In Table~\ref{tab:03}, 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 +case between CGLS and LSQR, it is clear that TSIRM is always faster than +FGMRES. For this example, the multigrid preconditioner is faster than SOR. The gain between TSIRM and FGMRES is more or less similar for the two preconditioners. Looking at the number of iterations to reach the convergence, it is obvious that TSIRM allows the reduction of the number of iterations. It -- 2.39.5