X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/0465f42aaa2cd6ca1b5a122e6461818f309140c2..e56b60da53690d349f415b94817a340eefbf0661:/hpcc.tex?ds=inline

diff --git a/hpcc.tex b/hpcc.tex
index 2bb3997..d85a917 100644
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -650,8 +650,8 @@ Note that the program was run with the following parameters:
 \item Maximum numbers of outer and inner iterations;
 \item Outer and inner precisions on the residual error;
 \item Matrix size $N_x$, $N_y$ and $N_z$;
-\item Matrix diagonal value: $6$ (See Equation~(\ref{eq:03}));
-\item Matrix off-diagonal value: $-1$;
+\item Matrix diagonal value: $6$ (see Equation~(\ref{eq:03}));
+\item Matrix off-diagonal values: $-1$;
 \item Communication mode: asynchronous.
 \end{itemize}
 
@@ -664,9 +664,9 @@ asynchronous multisplitting  compared to GMRES with two distant clusters.
 With these settings, Table~\ref{tab.cluster.2x50} shows
 that after setting the bandwidth of the  inter cluster network to  \np[Mbit/s]{5} and a latency in order of one hundredth of millisecond and a processor power
 of one GFlops, an efficiency of about \np[\%]{40} is
-obtained in asynchronous mode for a matrix size of 62 elements. It is noticed that the result remains
+obtained in asynchronous mode for a matrix size of $62^3$ elements. It is noticed that the result remains
 stable even we vary the residual error precision from \np{E-5} to \np{E-9}. By
-increasing the matrix size up to 100 elements, it was necessary to increase the
+increasing the matrix size up to $100^3$ elements, it was necessary to increase the
 CPU power of \np[\%]{50} to \np[GFlops]{1.5} to get the algorithm convergence and the same order of asynchronous mode efficiency.  Maintaining such processor power but increasing network throughput inter cluster up to
 \np[Mbit/s]{50}, the result of efficiency with a relative gain of 2.5 is obtained with
 high external precision of \np{E-11} for a matrix size from 110 to 150 side
