figure 05

diff --git a/paper.tex b/paper.tex
index d2016d1230f3656cf1aa04adcd699cbb682d012e..357094db6fd98c707695ef1e39ba277280f8c2c7 100644 (file)
--- a/paper.tex
+++ b/paper.tex
@@ -744,9 +744,9 @@ This figure show the execution time of the both algorithm EA and DK with sparse
 \label{fig:05}
 \end{figure}
 
 \label{fig:05}
 \end{figure}
 

-%\subsubsection{The execution time of Ehrlich-Aberth algorithm on OpenMP(1 core, 4 cores) vs. on a Tesla GPU}
+This figure show the evaluation of the number of iteration according to degree of polynomial from both EA and DK algorithms, we can see that the iteration number of DK is of order 100 while EA is of order 10. Indeed the computing of derivative of P (the polynomial to resolve) in the iterative function(Eq.~\ref{Eq:Hi}) executed by EA, offers him a possibility to converge more quickly. In counterpart the DK operator(Eq.~\ref{DK}) need low operation, consequently low execution time per iteration,but it need lot of iteration to converge.

 
 

-\section{Conclusion and perspective}
+ \section{Conclusion and perspective}

 \label{sec7}
 In this paper we have presented the parallel implementation Ehrlich-Aberth method on GPU and on CPU (openMP) for the problem of finding roots polynomial. Moreover, we have improved the classical Ehrlich-Aberth method witch suffer of overflow problems, the exp.log solution applying to the iterative function to resolve high degree polynomial.
 
 \label{sec7}
 In this paper we have presented the parallel implementation Ehrlich-Aberth method on GPU and on CPU (openMP) for the problem of finding roots polynomial. Moreover, we have improved the classical Ehrlich-Aberth method witch suffer of overflow problems, the exp.log solution applying to the iterative function to resolve high degree polynomial.