X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/kahina_paper1.git/blobdiff_plain/761635b66f95dbbc02078e5129bed61f2b41a289..065c8445483c2244976fa7af7a1dfc81f6e0096e:/paper.tex?ds=sidebyside

diff --git a/paper.tex b/paper.tex
index e6a1e37..73a2b47 100644
--- a/paper.tex
+++ b/paper.tex
@@ -1,6 +1,6 @@
 \documentclass[review]{elsarticle}
 
-\usepackage{lineno,hyperref}
+\usepackage{lineno,hyperref}  
 \usepackage[utf8]{inputenc}
 %%\usepackage[T1]{fontenc}
 %%\usepackage[french]{babel}
@@ -454,7 +454,7 @@ The means steps of Aberth method can expressed as an algorithm
 like:
   
 \begin{algorithm}[H]
-\LinesNumbered
+%\LinesNumbered
 \caption{Algorithm to find root polynomial with Aberth method}
 
 \KwIn{$Z^{0}$(Initial root's vector),$\varepsilon$ (error
@@ -532,7 +532,7 @@ In theory, the $Total\_time_{exe}$ on GPU is speed up nbr\_thread times as a $To
 In CUDA platform, All the instruction of the loop \verb=for= are executed by the GPU as a kernel form. A kernel is a procedure written in CUDA and defined by a heading \verb=__global__=, which means that it is to be executed by the GPU. The following algorithm see the Aberth algorithm on GPU:
 
 \begin{algorithm}[H]
-\LinesNumbered
+%\LinesNumbered
 \caption{Algorithm to find root polynomial with Aberth method}
 
 \KwIn{$Z^{0}$(Initial root's vector),$\varepsilon$ (error
@@ -560,7 +560,7 @@ After the initialization step, all data of the root finding problem to be solved
 The second kernel executes the iterative function and update Z(k),as formula (), we notice that the kernel update are called in two forms,  separated with  the value of \emph{R} which determines the radius beyond which we apply the logarithm formula like this: 
 
 \begin{algorithm}[H]
-\LinesNumbered
+%\LinesNumbered
 \caption{A global Algorithm for the iterative function}
 
 \eIf{$(\left|Z^{(k)}\right|<= R)$}{
