@@ -529,7+532,7 @@ In theory, the $Total\_time_{exe}$ on GPU is speed up nbr\_thread times as a $To
In CUDA platform, All the instruction of the loop \verb=for= are executed by the GPU as a kernel form. A kernel is a procedure written in CUDA and defined by a heading \verb=__global__=, which means that it is to be executed by the GPU. The following algorithm see the Aberth algorithm on GPU:
\begin{algorithm}[H]
In CUDA platform, All the instruction of the loop \verb=for= are executed by the GPU as a kernel form. A kernel is a procedure written in CUDA and defined by a heading \verb=__global__=, which means that it is to be executed by the GPU. The following algorithm see the Aberth algorithm on GPU:
\begin{algorithm}[H]
-\LinesNumbered
+%\LinesNumbered
\caption{Algorithm to find root polynomial with Aberth method}
@@ -557,7+560,7 @@ After the initialization step, all data of the root finding problem to be solved
The second kernel executes the iterative function and update Z(k),as formula (), we notice that the kernel update are called in two forms, separated with the value of \emph{R} which determines the radius beyond which we apply the logarithm formula like this:
\begin{algorithm}[H]
The second kernel executes the iterative function and update Z(k),as formula (), we notice that the kernel update are called in two forms, separated with the value of \emph{R} which determines the radius beyond which we apply the logarithm formula like this:
\begin{algorithm}[H]
-\LinesNumbered
+%\LinesNumbered
\caption{A global Algorithm for the iterative function}
\eIf{$(\left|Z^{(k)}\right|<= R)$}{
\caption{A global Algorithm for the iterative function}
\eIf{$(\left|Z^{(k)}\right|<= R)$}{
@@ -672,4+675,4 @@ We initially carried out the convergence of Aberth algorithm with various sizes