\end{equation}
This solution is applied when the root except the circle unit, represented by the radius $R$ evaluated in C language as:
+
\begin{verbatim}
R = exp(log(DBL_MAX)/(2*n) );
\end{verbatim}
\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 (the derivative of P), $n$
- (Polynomial's degrees), $\Delta z_{max}$ (maximum value of stop condition)}
+ threshold), P(Polynomial to solve), Pu (the derivative of P), $n$ (Polynomial's degrees),$\Delta z_{max}$ (maximum value of stop condition)}
\KwOut {$Z$ (The solution root's vector), $ZPrec$ (the previous solution root's vector)}
\end{algorithm}
~\\
-After the initialization step, all data of the root finding problem to be solved must be copied from the CPU memory to the GPU global memory, because the GPUs only access data already present in their memories. Next, all the data-parallel arithmetic operations inside the main loop \verb=(while(...))= are executed as kernels by the GPU. The first kernel named \textit{save} in line 6 of Algorithm~\ref{alg2-cuda} consists in saving the vector of polynomial's root found at the previous time-step in GPU memory, in order to check the convergence of the roots after each iteration (line 8, Algorithm~\ref{alg2-cuda}).
+After the initialization step, all data of the root finding problem to be solved must be copied from the CPU memory to the GPU global memory, because the GPUs only access data already present in their memories. Next, all the data-parallel arithmetic operations inside the main loop \verb=(do ... while(...))= are executed as kernels by the GPU. The first kernel named \textit{save} in line 6 of Algorithm~\ref{alg2-cuda} consists in saving the vector of polynomial's root found at the previous time-step in GPU memory, in order to check the convergence of the roots after each iteration (line 8, Algorithm~\ref{alg2-cuda}).
The second kernel executes the iterative function $H$ and updates
$d\_Z$, according to Algorithm~\ref{alg3-update}. We notice that the
\begin{algorithm}[H]
\label{alg3-update}
%\LinesNumbered
-\caption{Kernel\_update}
+\caption{Kernel update}
\eIf{$(\left|d\_Z\right|<= R)$}{
-$kernel\_update((d\_Z,d\_Pcoef,d\_Pdegres,d\_Pucoef,d\_Pudegres)$\;}
+$kernel\_update((d\_Z,d\_P,d\_Pu)$\;}
{
-$kernel\_update\_ExpoLog((d\_Z,d\_Pcoef,d\_Pdegres,d\_Pucoef,d\_Pudegres))$\;
+$kernel\_update\_ExpoLog((d\_Z,d\_P,\_Pu))$\;
}
\end{algorithm}
-The first form executes formula \ref{eq:SimplePolynome} if the modulus
+The first form executes formula the EA function Eq.~\ref{Eq:Hi} if the modulus
of the current complex is less than the a certain value called the
radius i.e. ($ |z^{k}_{i}|<= R$), else the kernel executes the EA.EL
function Eq.~\ref{Log_H2}
+%%HIER END MY REVISIONS (SIDER)
\section{Experimental study}
\label{sec6}
%\subsection{Definition of the used polynomials }
\label{fig:01}
\end{figure}
%%Figure 1 %%show a comparison of execution time between the parallel and sequential version of the Ehrlich-Aberth algorithm with sparse polynomial exceed 100000,
-In Figure~\ref{fig:01}, we report the execution times of the
-Ehrlich-Aberth method on one core of a Quad-Core Xeon E5620 CPU, on
-four cores on the same machine with \textit{OpenMP} and on a Nvidia
-Tesla K40c GPU. We chose different sparse polynomials with degrees
-ranging from 100,000 to 1,000,000. We can see that the implementation
-on the GPU is faster than those implemented on the CPU.
-
-This is due to the GPU ability to compute the data-parallel functions
-faster than its CPU counterpart. However, the execution time for the
-CPU (4 cores) implementation exceed 5,000s for 250,000 degrees
-polynomials. In counterpart, the GPU implementation for the same
-polynomials do not take more 100s. With the GPU
-we can solve high degrees polynomials very quickly up to degree
-of 1,000,000. We can also notice that the GPU implementation are
-almost 47 faster then those implementation on the CPU (4
-cores). However the CPU (4 cores) implementation are almost 4 faster
-then his implementation on CPU (1 core). Furthermore, the number of
-iterations and the convergence precision are similar with the CPU
-and the GPU implementation.
-
-%%This reduction
-%of time allows us to compute roots of polynomial of more important
-%degree at the same time than with a CPU.
+In Figure~\ref{fig:01}, we report respectively the execution time of the Ehrlich-Aberth method implemented initially on one core of the Quad-Core Xeon E5620 CPU than on four cores of the same machine with \textit{OpenMP} platform and the execution time of the same method implemented on one Nvidia Tesla K40c GPU, with sparse polynomial degrees ranging from 100,000 to 1,000,000. We can see that the method implemented on the GPU are faster than those implemented on the CPU (4 cores). This is due to the GPU ability to compute the data-parallel functions faster than its CPU counterpart. However, the execution time for the CPU(4 cores) implementation exceed 5,000 s for 250,000 degrees polynomials, in counterpart the GPU implementation for the same polynomials not reach 100 s, more than again, with an execution time under to 2,500 s CPU (4 cores) implementation can resolve polynomials degrees of only 200,000, whereas GPU implementation can resolve polynomials more than 1,000,000 degrees. We can also notice that the GPU implementation are almost 47 faster then those implementation on the CPU(4 cores). However the CPU(4 cores) implementation are almost 4 faster then his implementation on CPU (1 core). Furthermore, we verify that the number of iterations and the convergence precision is the same for the both CPU and GPU implementation. %This reduction of time allows us to compute roots of polynomial of more important degree at the same time than with a CPU.
%We notice that the convergence precision is a round $10^{-7}$ for the both implementation on CPU and GPU. Consequently, we can conclude that Ehrlich-Aberth on GPU are faster and accurately then CPU implementation.
-\subsection{Influence of the number of threads on the execution times
- of different polynomials (sparse and full)}
-
-To optimize the performances of an algorithm on a GPU, it is necessary
-to maximize the use of the GPU cores. In fact, it is interesting to
-see the influence of the number of threads per block on the execution
-time of Ehrlich-Aberth algorithm. For that, we notice that the
-maximum number of threads per block for the Nvidia Tesla K40 GPU is
-1024. So the number of threads per block ranges from 8 to 1024. We
-took into account the execution time for both sparse and full of 10
-different polynomials of size 50,000 and 10 different polynomials of
-size 500,000 degrees.
+\subsection{Influence of the number of threads on the execution times of different polynomials (sparse and full)}
+To optimize the performances of an algorithm on a GPU, it is necessary to maximize the use of cores GPU (maximize the number of threads executed in parallel) and to optimize the use of the various memoirs GPU. In fact, it is interesting to see the influence of the number of threads per block on the execution time of Ehrlich-Aberth algorithm.
+For that, we notice that the maximum number of threads per block for the Nvidia Tesla K40 GPU is 1024, so we varied the number of threads per block from 8 to 1024. We took into account the execution time for both sparse and full of 10 different polynomials of size 50,000 and 10 different polynomials of size 500,000 degrees.
\begin{figure}[htbp]
\centering
\label{fig:02}
\end{figure}
-Figure~\ref{fig:02} shows that, the best execution time for both
-sparse and full polynomial are given when the threads number varies
-between 64 and 256 threads per block. We notice that with small
-polynomials the best number of threads per block is 64, whereas the
-large polynomials the best number of threads per block is
-256. However, in the following experiments we specify that the number
-of threads per block is 256.
+The figure 2 show that, the best execution time for both sparse and full polynomial are given when the threads number varies between 64 and 256 threads per bloc. We notice that with small polynomials the best number of threads per block is 64, Whereas, the large polynomials the best number of threads per block is 256. However,In the following experiments we specify that the number of thread by block is 256.
-\subsection{Influence of exponential-logarithm solution to compute very high degrees polynomials}
+\subsection{The impact of exp.log solution to compute very high degrees of polynomial}
In this experiment we report the performance of exp-log solution described in Section~\ref{sec2} to compute very high degrees polynomials.
\begin{figure}[htbp]
\centering
\includegraphics[width=0.8\textwidth]{figures/sparse_full_explog}
-\caption{The impact of exp-log solution to compute very high degrees of polynomial.}
+\caption{The impact of exp.log solution to compute very high degrees of polynomial.}
\label{fig:03}
\end{figure}
+
Figure~\ref{fig:03} shows a comparison between the execution time of
the Ehrlich-Aberth algorithm using the exp.log solution and the
execution time of the Ehrlich-Aberth algorithm without this solution,
+
\subsection{Comparison of the Durand-Kerner and the Ehrlich-Aberth methods}
In this part, we compare the Durand-Kerner and the Ehrlich-Aberth
