X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/book_gpu.git/blobdiff_plain/b4a21f0b9226126a2c50f54a5518be5ef7c60749..55ce7168c6e69a2462d76c95dc9a5298ceedb04f:/BookGPU/Chapters/chapter13/ch13.tex

diff --git a/BookGPU/Chapters/chapter13/ch13.tex b/BookGPU/Chapters/chapter13/ch13.tex
index b60105d..db2ab78 100755
--- a/BookGPU/Chapters/chapter13/ch13.tex
+++ b/BookGPU/Chapters/chapter13/ch13.tex
@@ -134,7 +134,7 @@ where $b=\{b_{1},b_{2},b_{3}\}$, $\|b\|_{2}$ denotes the Euclidean norm of $b$,
 $v=e^{-a}.u$ represents the general change of variables such that $a=\frac{b^{t}(x,y,z)}{2\eta}$.
 Consequently, the numerical resolution of the diffusion problem (the self-adjoint
 operator~(\ref{ch13:eq:04})) is done by optimization algorithms, in contrast to that
-of the convection-diffusion problem (non self-adjoint operator~(\ref{ch13:eq:03}))
+of the convection-diffusion problem (nonself-adjoint operator~(\ref{ch13:eq:03}))
 which is done by relaxation algorithms. In the case of our studied algorithm, the convergence\index{convergence}
 is ensured by M-matrix property; then, the performance is linked to the magnitude of
 the spectral radius of the iteration matrix, which is independent of the condition
@@ -698,6 +698,7 @@ $800^{3}$                     & $222,108.09$       & $1,769,232$      & $188,790
 
 \begin{table}
 \centering
+\begin{scriptsize}
 \begin{tabular}{|c|c|c|c|c|c|c|c|}
 \hline
 \multirow{2}{*}{\bf Pb. size} & \multicolumn{3}{c|}{\bf Synchronous}                 & \multicolumn{3}{c|}{\bf Asynchronous}                & \multirow{2}{*}{\bf Gain\%}  \\ \cline{2-7}
@@ -712,6 +713,7 @@ $768^{3}$                    & $4,112.68$         & $831,144$        & $50.13$
 
 $800^{3}$                    & $3,950.87$         & $899,088$        & $56.22$         & $3,636.57$        & $834,900$        & $51.91$     & $7.95$ \\ \hline 
 \end{tabular}
+\end{scriptsize}
 \vspace{0.5cm}
 \caption{Execution times in seconds of the parallel projected Richardson method implemented on a cluster of 12 GPUs.}
 \label{ch13:tab:02}
@@ -745,7 +747,7 @@ consequently it also depends on the number of computing nodes.
 %%--------------------------%%
 \section{Red-black ordering technique}
 \label{ch13:sec:06}
-As is wellknown, the Jacobi method\index{iterative method!Jacobi} is characterized
+As is well known, the Jacobi method\index{iterative method!Jacobi} is characterized
 by a slow convergence\index{convergence} rate compared to some iterative methods\index{iterative method}
 (for example, Gauss-Seidel method\index{iterative method!Gauss-Seidel}). So, in this
 section, we present some solutions to reduce the execution time and the number of
@@ -776,7 +778,7 @@ vector elements leads to using twice the initial number of memory transactions.
 we apply the point red-black ordering\index{iterative method!red-black ordering}
 accordingly to the $y$-coordinate, as is shown in Figure~\ref{ch13:fig:06.02}. In
 this case, the vector elements having even $y$-coordinate are computed in parallel
-using the values of those having odd $y$-coordinate and then viceversa. Moreover,
+using the values of those having odd $y$-coordinate and then vice-versa. Moreover,
 in the GPU implementation of the parallel projected Richardson method (Section~\ref{ch13:sec:04}),
 we have shown that a subproblem of size $(NX\times ny\times nz)$ is decomposed into
 $nz$ grids of size $(NX\times ny)$. Then, each kernel is executed in parallel by