+\subsection{the EA method}
+the Ehrlich-Aberth method is an iterative method , contain 4 steps, start from the initial approximations of all the
+roots of the polynomial,the second step initialize the solution vector $Z$ using the Guggenheimer method to assure the distinction of the initial vector roots, than in step 3 we apply the the iterative function based on the Newton's method and Weiestrass operator[...,...], wich will make it possible to converge to the roots solution, provided that all the root are different. At the end of each application of the iterative function, a stop condition is verified consists in stopping the iterative process when the whole of the modules of the roots
+are lower than a fixed value $ε$
+\subsection{EA parallel implementation on CUDA}
+Like any parallel code, a GPU parallel implementation first
+requires to determine the sequential tasks and the
+parallelizable parts of the sequential version of the
+program/algorithm. In our case, all the operations that are easy
+to execute in parallel must be made by the GPU to accelerate
+the execution of the application, like the step 3 and step 4. On the other hand, all the
+sequential operations and the operations that have data
+dependencies between threads or recursive computations must
+be executed by only one CUDA or CPU thread (step 1 and step 2). Initially we specifies the organization of threads in parallel, need to specify the dimension of the grid Dimgrid: the number of block per grid and block by DimBlock: the number of threads per block required to process a certain task.
+
+we create the kernel, for step 3 we have two kernels, the
+first named \textit{save} is used to save vector $Z^{K-1}$ and the kernel
+\textit{update} is used to update the $Z^{K}$ vector. In step 4 a kernel is
+created to test the convergence of the method. In order to
+compute function H, we have two possibilities: either to use
+the Jacobi method, or the Gauss-Seidel method which uses the
+most recent computed roots. It is well known that the Gauss-
+Seidel mode converges more quickly. So, we used the Gauss-Seidel mode of iteration. To
+parallelize the code, we created kernels and many functions to
+be executed on the GPU for all the operations dealing with the
+computation on complex numbers and the evaluation of the
+polynomials. As said previously, we managed both functions
+of evaluation of a polynomial: the normal method, based on
+the method of Horner and the method based on the logarithm
+of the polynomial. All these methods were rather long to
+implement, as the development of corresponding kernels with
+CUDA is longer than on a CPU host. This comes in particular
+from the fact that it is very difficult to debug CUDA running
+threads like threads on a CPU host. In the following paragraph
+Algorithm 1 shows the GPU parallel implementation of Ehrlich-Aberth method.
+
+Algorithm~\ref{alg2-cuda} shows a sketch of the Ehrlich-Aberth method using CUDA.
+
+\begin{enumerate}
+\begin{algorithm}[htpb]
+\label{alg2-cuda}
+%\LinesNumbered
+\caption{CUDA Algorithm to find roots with the Ehrlich-Aberth method}
+
+\KwIn{$Z^{0}$ (Initial root's vector), $\varepsilon$ (Error tolerance
+ threshold), P (Polynomial to solve), Pu (Derivative of P), $n$ (Polynomial degrees), $\Delta z_{max}$ (Maximum value of stop condition)}
+
+\KwOut {$Z$ (Solution root's vector), $ZPrec$ (Previous solution root's vector)}
+
+\BlankLine
+
+\item Initialization of the of P\;
+\item Initialization of the of Pu\;
+\item Initialization of the solution vector $Z^{0}$\;
+\item Allocate and copy initial data to the GPU global memory\;
+\item k=0\;
+\While {$\Delta z_{max} > \epsilon$}{
+\item Let $\Delta z_{max}=0$\;
+\item $ kernel\_save(ZPrec,Z)$\;
+\item k=k+1\;
+\item $ kernel\_update(Z,P,Pu)$\;
+\item $kernel\_testConverge(\Delta z_{max},Z,ZPrec)$\;
+
+}
+\item Copy results from GPU memory to CPU memory\;
+\end{algorithm}
+\end{enumerate}
+~\\
