X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/GMRES2stage.git/blobdiff_plain/34a7850bea7d50191ce0ab301b26d2c6d11ef2ff..afeb88ec33744268bc138cf5d9cc5406a9599763:/paper.tex

diff --git a/paper.tex b/paper.tex
index a4545fd..d00cbe1 100644
--- a/paper.tex
+++ b/paper.tex
@@ -626,6 +626,7 @@ GMRES method~\cite{Saad86}, or any of its variants, can potentially be used as
 inner solver. The current approximation of the Krylov method is then stored inside a $n \times s$ matrix
 $S$, which is composed by the $s$ last solutions that have been computed during 
 the inner iterations phase.
+In the remainder, the $i$-th column vector of $S$ will be denoted by $S_i$. 
 
 At each $s$ iterations, another kind of minimization step is applied in order to
 compute a new  solution $x$. For that, the previous  residuals of $Ax=b$ are computed by
@@ -667,11 +668,12 @@ appropriate than a single direct method in a parallel context.
 Algorithm~\ref{algo:01}  summarizes  the principle  of  the proposed  method.  The  outer
 iteration is  inside the \emph{for}  loop. Line~\ref{algo:solve}, the Krylov  method is
 called for a  maximum of $max\_iter_{kryl}$ iterations.  In practice, we  suggest to set this parameter
-equals to  the restart  number of the  GMRES-like method. Moreover,  a tolerance
+equal to  the restart  number in the  GMRES-like method. Moreover,  a tolerance
 threshold must be specified for the  solver. In practice, this threshold must be
-much  smaller  than the  convergence  threshold  of  the TSIRM  algorithm  (\emph{i.e.}
+much  smaller  than the  convergence  threshold  of  the TSIRM  algorithm  (\emph{i.e.},
 $\epsilon_{tsirm}$).  Line~\ref{algo:store}, $S_{k \mod s}=x^k$ consists in copying the
-solution  $x_k$  into the  column  $k \mod s$ of  the  matrix  $S$, where $S$ is a matrix of size $n\times s$ whose column vector $i$ is denoted by $S_i$. After  the
+solution  $x_k$  into the  column  $k \mod s$ of $S$.
+After  the
 minimization, the matrix $S$ is reused with the new values of the residuals.  To
 solve the minimization problem, an  iterative method is used. Two parameters are
 required for that: the maximum number of iterations and the threshold to stop the
@@ -736,7 +738,7 @@ these operations are easy to implement in PETSc or similar environment.
 
 \section{Convergence results}
 \label{sec:04}
-Let us recall the following result, see~\cite{Saad86}.
+Let us recall the following result, see~\cite{Saad86} for further readings.
 \begin{proposition}
 \label{prop:saad}
 Suppose that $A$ is a positive real matrix with symmetric part $M$. Then the residual norm provided at the $m$-th step of GMRES satisfies:
@@ -886,7 +888,17 @@ For more technical details on  these applications, interested readers are invite
 to  read the  codes available  in the  PETSc sources.   Those problems  have been
 chosen because they  are scalable with many cores which is not the case of other problems that we have tested.
 
-In the following larger experiments are described on two large scale architectures: Curie and Juqeen... {\bf description...}\\
+In  the  following   larger  experiments  are  described  on   two  large  scale
+architectures:  Curie and  Juqeen.  Both  these architectures  are supercomputer
+composed of 80,640 cores for Curie and 458,752 cores for Juqueen. Those machines
+are respectively hosted  by GENCI in France and  Jülich Supercomputing Centre in
+Germany. They belongs with other similar architectures of the PRACE initiative (
+Partnership  for Advanced  Computing in  Europe)  which aims  at proposing  high
+performance supercomputing architecture to enhance research in Europe. The Curie
+architecture is composed of Intel E5-2680  processors at 2.7 GHz with 2Gb memory
+by core. The Juqueen architecture is composed  of IBM PowerPC A2 at 1.6 GHz with
+1Gb memory per core.
+
 
 
 {\bf Description of preconditioners}\\