this is the commit 06.09.15

diff --git a/Root.tex b/Root.tex
index 2ee2c28e0c2d2e7ad23bd0c641a5c20d38d19c93..5134838f63ac685684c5b3041500d947f2b9bef9 100644 (file)
--- a/Root.tex
+++ b/Root.tex
@@ -11,8 +11,9 @@ we consider a polynomial of degree \textit{n} having coefficients
 in the complex \textit{C} and zeros $\alpha\r
 _{i},\textit{i=1,...,n}$. \\\r
 \begin{center}\r
 in the complex \textit{C} and zeros $\alpha\r
 _{i},\textit{i=1,...,n}$. \\\r
 \begin{center}\r

-     {\Large$p(x)=\sum{a_{i}x^{i}}=a_{n}\prod(x-\alpha_{i}),\r
-a_{0}a_{n}\neq0,$}\r
+\begin{equation}\r
+     {\Large p(x)=\sum{a_{i}x^{i}}=a_{n}\prod(x-\alpha_{i}),a_{0} a_{n}\neq 0}\r
+\end{equation}\r

 \end{center}\r
 \r
  the root finding problem consist to find\r
 \end{center}\r
 \r
  the root finding problem consist to find\r


@@ -48,24 +49,26 @@ Muller s method, and several methods for simultaneous
 approximation of all the roots, starting with the Durand-Kerner\r
 method:\r
 \begin{center}\r
 approximation of all the roots, starting with the Durand-Kerner\r
 method:\r
 \begin{center}\r

-\r
-$  Z_{i}=Z_{i}-\frac{P(Z_{i})}{\prod_{i\neq j}(z_{i}-z_{j})} $\r
+\begin{equation} Z_{i}=Z_{i}-\frac{P(Z_{i})}{\prod_{i\neq j}(z_{i}-z_{j})}\r
+\end{equation}\r

 \end{center}\r
 \r
 \end{center}\r
 \r

-This formula is mentioned for the first time from Weiestrass [12]\r
-as part of the fundamental theorem of Algebra and is rediscovered\r
-from Ilieff~\cite{Ilief50} [2], Docev [3], Durand [4], Kerner [5].\r
-Another method discovered from Borsch-Supan [6] and also described\r
-and brought in the following form from Ehrlich [7] and\r
+This formula is mentioned for the first time from\r
+Weiestrass~\cite{Weierstrass03} as part of the fundamental theorem\r
+of Algebra and is rediscovered from Ilieff~\cite{Ilie50},\r
+Docev~\cite{Docev62}, Durand~\cite{Durand60},\r
+Kerner~\cite{Kerner66}. Another method discovered from\r
+Borsch-Supan~\cite{ Borch-Supan63} and also described and brought\r
+in the following form from Ehrlich~\cite{Ehrlich67} and\r

 Aberth~\cite{Aberth73}.\r
 \begin{center}\r
 Aberth~\cite{Aberth73}.\r
 \begin{center}\r

-\r
-$  Z_{i}=Z_{i}-\frac{1}{{\frac {P'(Z_{i})}\r
-{P(Z_{i})}}-{\sum_{i\neq j}(z_{i}-z_{j})}} $\r
+\begin{equation}\r
+ Z_{i}=Z_{i}-\frac{1}{{\frac {P'(Z_{i})} {P(Z_{i})}}-{\sum_{i\neq j}(z_{i}-z_{j})}}\r
+\end{equation}\r

 \end{center}\r
 \r
 \end{center}\r
 \r

-Aberth, Ehrlich and Farmer-Loizou [10] have proved that the above\r
-method has cubic order of convergence for simple roots.\r
+Aberth, Ehrlich and Farmer-Loizou~\cite{Loizon83} have proved that\r
+the above method has cubic order of convergence for simple roots.\r

 \r
 \r
 Iterative methods raise several problem when implemented e.g.\r
 \r
 \r
 Iterative methods raise several problem when implemented e.g.\r


@@ -76,37 +79,38 @@ parallelization of these algorithms will improve the convergence
 time.\r
 \r
 Many authors have treated the problem of parallelization of\r
 time.\r
 \r
 Many authors have treated the problem of parallelization of\r

-simultaneous methods. Freeman [13] has tested the DK method, EA\r
-method and another method of the fourth order proposed from Farmer\r
-and Loizou [10],on a 8- processor linear chain, for polynomial of\r
-degree up to 8. The third method often diverges, but the first two\r
-methods have speed-up 5.5 (speed-up=(Time on one processor)/(Time\r
-on p processors)). Later Freeman and Bane [14] consider\r
-asynchronous algorithms, in which each processor continues to\r
-update its approximations even although the latest values of other\r
-$z_i((k))$ have not received from the other processors, in\r
-difference with the synchronous version where it would wait. in\r
-[15]proposed two methods of parallelization for architecture with\r
-shared memory and distributed memory,it able to compute the root\r
-of polynomial degree  10000 on 430 s with only 8 pc and 2\r
-communications per iteration. Compare to the sequential it take\r
-3300 s to obtain the same results.\r
+simultaneous methods. Freeman~\cite{Freeman89} has tested the DK\r
+method, EA method and another method of the fourth order proposed\r
+from Farmer and Loizou~\cite{Loizon83},on a 8- processor linear\r
+chain, for polynomial of degree up to 8. The third method often\r
+diverges, but the first two methods have speed-up 5.5\r
+(speed-up=(Time on one processor)/(Time on p processors)). Later\r
+Freeman and Bane~\cite{Freemanall90}  consider asynchronous\r
+algorithms, in which each processor continues to update its\r
+approximations even although the latest values of other $z_i((k))$\r
+have not received from the other processors, in difference with\r
+the synchronous version where it would wait.\r
+in~\cite{Raphaelall01}proposed two methods of parallelization for\r
+architecture with shared memory and distributed memory,it able to\r
+compute the root of polynomial degree  10000 on 430 s with only 8\r
+pc and 2 communications per iteration. Compare to the sequential\r
+it take 3300 s to obtain the same results.\r

 \r
 After this few works discuses this problem until the apparition of\r
 \r
 After this few works discuses this problem until the apparition of\r

-the Compute Unified Device Architecture (CUDA) [19],a parallel\r
-computing platform and a programming model invented by NVIDIA. the\r
-computing ability of GPU has exceeded the counterpart of CPU. It\r
-is a waste of resource to be just a graphics card for GPU.  CUDA\r
-adopts a totally new computing architecture to use the hardware\r
-resources provided by GPU in order to offer a stronger computing\r
-ability to the massive data computing.\r
-\r
-\r
-Indeed [16]proposed the implementation of the Durand-Kerner method\r
-on GPU (Graphics Processing Unit). The main result prove that a\r
-parallel implementation is 10 times as fast as the sequential\r
-implementation on a single CPU for high degree polynomials that is\r
-greater than about 48000.\r
+the Compute Unified Device Architecture (CUDA)~\cite{CUDA10},a\r
+parallel computing platform and a programming model invented by\r
+NVIDIA. the computing ability of GPU has exceeded the counterpart\r
+of CPU. It is a waste of resource to be just a graphics card for\r
+GPU.  CUDA adopts a totally new computing architecture to use the\r
+hardware resources provided by GPU in order to offer a stronger\r
+computing ability to the massive data computing.\r
+\r
+\r
+Indeed,~\cite{Kahinall14}proposed the implementation of the\r
+Durand-Kerner method on GPU (Graphics Processing Unit). The main\r
+result prove that a parallel implementation is 10 times as fast as\r
+the sequential implementation on a single CPU for high degree\r
+polynomials that is greater than about 48000.\r

 \paragraph{}\r
 The mean part of our work is to implement the Aberth method on GPU\r
 and compare it with the Durand Kerner\r
 \paragraph{}\r
 The mean part of our work is to implement the Aberth method on GPU\r
 and compare it with the Durand Kerner\r


@@ -115,18 +119,28 @@ implementation.................To be continued..................
 \r
 \section{Aberth method and difficulties}\r
 A cubically convergent iteration method for finding zeros of\r
 \r
 \section{Aberth method and difficulties}\r
 A cubically convergent iteration method for finding zeros of\r

-polynomials was proposed by O.Aberth[?].The Aberth method is a\r
-purely algebraic derivation.To illustrate the derivation, we let\r
-$w_{i}(z)$ be the product of linear factor $ w_{i}(z)=\prod_{j=1,j\r
-\neq i}^{n} (z-x_{j})$\r
+polynomials was proposed by O.Aberth~\cite{Aberth73}.The Aberth\r
+method is a purely algebraic derivation.To illustrate the\r
+derivation, we let $w_{i}(z)$ be the product of linear factor $\r
+w_{i}(z)=\prod_{j=1,j \neq i}^{n} (z-x_{j})$\r

 \r
 and rational function $R_{i}(z)$ be the correction term of\r
 \r
 and rational function $R_{i}(z)$ be the correction term of\r

-Weistrass method (?)\r
-$$R_{i}(z)=\dfrac{p(z)}{w_{i}(Z)} , i=1,2,...,n. $$\r
+Weistrass method~\cite{Weierstrass03}:\r
+\r
+\begin{equation}\r
+R_{i}(z)=\dfrac{p(z)}{w_{i}(Z)} , i=1,2,...,n.\r
+\end{equation}\r

 \r
 Differentiating the rational function $R_{i}(z)$ and applying the\r
 Newton method, we have\r
 \r
 Differentiating the rational function $R_{i}(z)$ and applying the\r
 Newton method, we have\r

-$$\dfrac{R_{i}(z)}{R_{i}^{'}(z)}= \dfrac{p(z)}{p^{'}(z)-p(z)\dfrac{w_{i}(z)}{w_{i}^{'}(z)}}= \dfrac{p(z)}{p^{'}(z)-p(z) \sum _{j=1,j \neq i}^{n}\dfrac{1}{z-x_{i}}}, i=1,2,...,n $$\r
+\r
+\begin{equation}\r
+\dfrac{R_{i}(z)}{R_{i}^{'}(z)}=\r
+\dfrac{p(z)}{p^{'}(z)-p(z)\dfrac{w_{i}(z)}{w_{i}^{'}(z)}}=\r
+\dfrac{p(z)}{p^{'}(z)-p(z) \sum _{j=1,j \neq\r
+i}^{n}\dfrac{1}{z-x_{i}}}, i=1,2,...,n\r
+\end{equation}\r
+\r

 Substituting $x_{j}$ for z we obtain the Aberth iteration method\r
 \r
 Let present the means stages of Aberth's method.\r
 Substituting $x_{j}$ for z we obtain the Aberth iteration method\r
 \r
 Let present the means stages of Aberth's method.\r


@@ -134,7 +148,10 @@ Let present the means stages of Aberth's method.
 \subsection{Polynomials Initialization}\r
  The initialization of polynomial P(z) with complex coefficients\r
  are given by:\r
 \subsection{Polynomials Initialization}\r
  The initialization of polynomial P(z) with complex coefficients\r
  are given by:\r

- $$ p(z)=\sum{a_{i}z^{n-i}}. where a_{n} \neq 0,a_{0}=1, a_{i}\subset C $$\r
+\r
+\begin{equation}\r
+  p(z)=\sum{a_{i}z^{n-i}}. where a_{n} \neq 0,a_{0}=1, a_{i}\subset C\r
+\end{equation}\r

 \r
 \r
 \subsection{Vector $Z^{0)}$ Initialization}\r
 \r
 \r
 \subsection{Vector $Z^{0)}$ Initialization}\r


@@ -142,26 +159,39 @@ Let present the means stages of Aberth's method.
 The choice of the initial points $z^{(0)}_{i} , i = 1, . . . , n,$\r
 from which starting the iteration  (2) or (3), is rather delicate\r
 since the number of steps needed by the iterative method to reach\r
 The choice of the initial points $z^{(0)}_{i} , i = 1, . . . , n,$\r
 from which starting the iteration  (2) or (3), is rather delicate\r
 since the number of steps needed by the iterative method to reach\r

-a given approximation strongly depends on it. In [1] the Aberth\r
-iteration is started by selecting n equispaced points on a circle\r
-of center 0 and radius r, where r is an upper bound to the moduli\r
-of the zeros. After[18]  performs this choice by selecting complex\r
-numbers along different circles and relies on the result of [19].\r
-\r
-$$\sigma_{0}=\frac{u+v}{2};u=\frac{\sum_{i=1}^{n}u_{i}}{n.max_{i=1}^{n}u_{i}}; v=\frac{\sum_{i=0}^{n-1}v_{i}}{n.min_{i=0}^{n-1}v_{i}};u_{i}=2.|a_{i}|^{\frac{1}{i}}; v_{i}=\frac{|\frac{a_{n}}{a_{i}}|^{\frac{1}{n-i}}}{2} $$\r
+a given approximation strongly depends on it.\r
+In~\cite{Aberth73}the Aberth iteration is started by selecting n\r
+equispaced points on a circle of center 0 and radius r, where r is\r
+an upper bound to the moduli of the zeros. After,~\cite{Bini96}\r
+performs this choice by selecting complex numbers along different\r
+circles and relies on the result of~\cite{Ostrowski41}.\r
+\r
+\begin{equation}\r
+\sigma_{0}=\frac{u+v}{2};u=\frac{\sum_{i=1}^{n}u_{i}}{n.max_{i=1}^{n}u_{i}};\r
+v=\frac{\sum_{i=0}^{n-1}v_{i}}{n.min_{i=0}^{n-1}v_{i}};u_{i}=2.|a_{i}|^{\frac{1}{i}};\r
+v_{i}=\frac{|\frac{a_{n}}{a_{i}}|^{\frac{1}{n-i}}}{2}\r
+\end{equation}\r

 \r
 \subsection{Iterative Function Hi}\r
 The operator used with Aberth's method is corresponding to the\r
 following equation which will enable the convergence towards\r
 polynomial solutions, provided all the roots are distinct.\r
 \r
 \r
 \subsection{Iterative Function Hi}\r
 The operator used with Aberth's method is corresponding to the\r
 following equation which will enable the convergence towards\r
 polynomial solutions, provided all the roots are distinct.\r
 \r

-$$ H_{i}(z)=z_{i}-\frac{1}{\frac{P^{'}(z_{i})}{P(z_{i})}-\sum_{j\neq i}{\frac{1}{z_{i}-z_{j}}}} $$\r
+\begin{equation}\r
+H_{i}(z)=z_{i}-\frac{1}{\frac{P^{'}(z_{i})}{P(z_{i})}-\sum_{j\neq\r
+i}{\frac{1}{z_{i}-z_{j}}}}\r
+\end{equation}\r

 \r
 \subsection{Convergence condition}\r
 determines the success of the termination. It consists in stopping\r
 the iterative function $H_{i}(z)$ when the are stable,the method\r
 converge sufficiently:\r
 \r
 \subsection{Convergence condition}\r
 determines the success of the termination. It consists in stopping\r
 the iterative function $H_{i}(z)$ when the are stable,the method\r
 converge sufficiently:\r

-$$  \forall i \in [1,n]; \frac{z_{i}^{(k)}-z_{i}^{(k-1)}}{z_{i}^{(k)}}< \xi$$\r
+\r
+\begin{equation}\r
+\forall i \in\r
+[1,n];\frac{z_{i}^{(k)}-z_{i}^{(k-1)}}{z_{i}^{(k)}}<\xi\r
+\end{equation}\r
+\r

 \r
 \section{Difficulties and amelioration}\r
 the Aberth method implementation suffer of overflow problems. This\r
 \r
 \section{Difficulties and amelioration}\r
 the Aberth method implementation suffer of overflow problems. This\r


@@ -171,40 +201,169 @@ point $\xi$ where $|\xi| > 1$.Indeed the limited number in the
 mantissa of floating takings the computation of P(z) wrong when z\r
 is large. for example $(10^{50}) +1+ (- 10_{50})$ will give result\r
 0 instead of 1 in reality.consequently we can't compute the roots\r
 mantissa of floating takings the computation of P(z) wrong when z\r
 is large. for example $(10^{50}) +1+ (- 10_{50})$ will give result\r
 0 instead of 1 in reality.consequently we can't compute the roots\r

-for large polynomial's degree. This problem was discuss in [17]\r
-for the Durand-Kerner method, the authors propose to use the\r
-logratihm and the exponential of a complex:\r
-\r
-$$ \forall(x,y)\in R^{*2}; \ln (x+i.y)=\ln(x^{2}+y^{2}) 2+i.\arcsin(y\sqrt{x^{2}+y^{2}})_{\left] -\pi, \pi\right[ } $$\r
-$$ \forall(x,y)\in R^{*2}; \exp(x+i.y)= \exp(x).\exp(i.y)$$\r
-$$                                       =\exp(x).\cos(y)+i.\exp(x).\sin(y)$$\r
-\r
+for large polynomial's degree. This problem was discuss in\r
+~\cite{Karimall98} for the Durand-Kerner method, the authors\r
+propose to use the logratihm and the exponential of a complex:\r
+\r
+\begin{equation}\r
+ \forall(x,y)\in R^{*2}; \ln (x+i.y)=\ln(x^{2}+y^{2})\r
+2+i.\arcsin(y\sqrt{x^{2}+y^{2}})_{\left] -\pi, \pi\right[ }\r
+\end{equation}\r
+%%\begin{equation}\r
+\begin{align}\r
+ \forall(x,y)\in R^{*2}; \exp(x+i.y)&= \exp(x).\exp(i.y)\\\r
+                                    &=\exp(x).\cos(y)+i.\exp(x).\sin(y)\r
+\end{align}\r
+%%\end{equation}\r

 \r
 The application of logarithm can replace any multiplications and\r
 divisions with additions and subtractions; consequently it\r
 manipulates lower absolute values and can be compute the roots for\r
 \r
 The application of logarithm can replace any multiplications and\r
 divisions with additions and subtractions; consequently it\r
 manipulates lower absolute values and can be compute the roots for\r

-large polynomial's degree exceed 1000[17].\r
+large polynomial's degree exceed~\cite{Karimall98}.\r

 \r
 Applying this solution for the Aberth method we obtain the\r
 iteration function with logarithm:\r
 %%$$ \exp \bigl(  \ln(p(z)_{k})-ln(\ln(p(z)_{k}^{'}))- \ln(1- \exp(\ln(p(z)_{k})-ln(\ln(p(z)_{k}^{'})+\ln\sum_{i\neq j}^{n}\frac{1}{z_{k}-z_{j}})$$\r
 \r
 Applying this solution for the Aberth method we obtain the\r
 iteration function with logarithm:\r
 %%$$ \exp \bigl(  \ln(p(z)_{k})-ln(\ln(p(z)_{k}^{'}))- \ln(1- \exp(\ln(p(z)_{k})-ln(\ln(p(z)_{k}^{'})+\ln\sum_{i\neq j}^{n}\frac{1}{z_{k}-z_{j}})$$\r

+\begin{equation}\r
+H_{i}(z)=z_{i}^{k}-\exp \left(\ln \left(\r
+p(z_{k})\right)-\ln\left(p(z_{k}^{'})\right)- \ln\r
+\left(1-Q(z_{k})\right)\right)\r
+\end{equation}\r
+where:\r

 \r
 \r

-$$ H_{i}(z)=z_{i}^{k}-\exp \left(\ln \left( p(z_{k})\right)-\ln\left(p(z_{k}^{'})\right)- \ln\left(1- \exp\left( \ln (p(z_{k}))-\ln(p(z_{k}^{'}))+\ln \left( \sum_{k\neq j}^{n}\frac{1}{z_{k}-z_{j}}\right)\right) \right) \right)$$\r
-\r
+\begin{equation}\r
+Q(z_{k})=\exp\left( \ln (p(z_{k}))-\ln(p(z_{k}^{'}))+\ln \left(\r
+\sum_{k\neq j}^{n}\frac{1}{z_{k}-z_{j}}\right)\right)\r
+\end{equation}\r

 \r
 \r
 this solution is applying when it is necessary\r
 \r
 \section{The implementation of simultaneous methods in a parallel computer}\r
 \r
 \r
 this solution is applying when it is necessary\r
 \r
 \section{The implementation of simultaneous methods in a parallel computer}\r

+    The main problem of the simultaneous methods is that the necessary\r
+time needed for the convergence is increased with the increasing\r
+of the degree of the polynomial. The parallelization of these\r
+algorithms will improve the convergence time. Researchers usually\r
+adopt one of the two following approaches to parallelize root\r
+finding algorithms. One approach is to reduce the total number of\r
+iterations as implemented by Miranker\r
+~\cite{Mirankar68,Mirankar71}, Schedler~\cite{Schedler72} and\r
+Winogard~\cite{Winogard72}. Another approach is to reduce the\r
+computation time per iteration, as reported\r
+in~\cite{Benall68,Jana06,Janall99,Riceall06}. There are many\r
+schemes for simultaneous approximations of all roots of a given\r
+polynomial. Several works on different methods and issues of root\r
+finding have been reported in~\cite{Azad07,Gemignani07,Kalantari08\r
+,Skachek08,Zhancall08,Zhuall08}. However, Durand-Kerner and\r
+Ehrlich methods are the most practical choices among\r
+them~\cite{Bini04}. These two methods have been extensively\r
+studied for parallelization due to their following advantages. The\r
+computation involved in these methods has some inherent\r
+parallelism that can be suitably exploited by SIMD machines.\r
+Moreover, they have fast rate of convergence (quadratic for the\r
+Durand-Kerner method and cubic for the Ehrlich). Various parallel\r
+algorithms reported for these methods can be found\r
+in~\cite{Cosnard90, Freeman89,Freemanall90,,Jana99,Janall99}.\r
+Freeman and Bane~\cite{Freemanall90} presented two parallel\r
+algorithms on a local memory MIMD computer with the compute-to\r
+communication time ratio O(n). However, their algorithms require\r
+each processor to communicate its current approximation to all\r
+other processors at the end of each iteration. Therefore they\r
+cause a high degree of memory conflict. Recently the author\r
+in~\cite{Mirankar71} proposed two versions of parallel algorithm\r
+for the Durand-Kerner method, and Aberth method on an on model of\r
+Optoelectronic Transpose Interconnection System (OTIS).The\r
+algorithms are mapped on an OTIS-2D torus using N processors. This\r
+solution need N processors to compute N roots, that it is not\r
+practical (is not suitable to compute large polynomial's degrees).\r
+Until then, the related works are not able to compute the root of\r
+the large polynomial's degrees (higher then 1000) and with small\r
+time.\r

 \r
 \r

-\r
+    Finding polynomial roots rapidly and accurately it is our\r
+objective, with the apparition of the CUDA(Compute Unified Device\r
+Architecture), finding the roots of polynomials becomes rewarding\r
+and very interesting, CUDA adopts a totally new computing\r
+architecture to use the hardware resources provided by GPU in\r
+order to offer a stronger computing ability to the massive data\r
+computing.in~\cite{Kahinall14} we proposed the first implantation\r
+of the root finding polynomials method on GPU (Graphics Processing\r
+Unit),which is the Durand-Kerner method. The main result prove\r
+that a parallel implementation is 10 times as fast as the\r
+sequential implementation on a single CPU for high degree\r
+polynomials that is greater than about 48000. Indeed, in this\r
+paper we present a parallel implementation of Aberth's method on\r
+GPU, more details are discussed in the following of this paper.\r
+\r
+\section {A parallel implementation of Aberth's method}\r
+\subsection{Background on the GPU architecture}\r
+A GPU is viewed as an accelerator for the data-parallel and\r
+intensive arithmetic computations. It draws its computing power\r
+from the parallel nature of its hardware and software\r
+architectures. A GPU is composed of hundreds of Streaming\r
+Processors (SPs) organized in several blocks called Streaming\r
+Multiprocessors (SMs). It also has a memory hierarchy. It has a\r
+private read-write local memory per SP, fast shared memory and\r
+read-only constant and texture caches per SM and a read-write\r
+global memory shared by all its SPs~\cite{NVIDIA10}\r
+\r
+    On a CPU equipped with a GPU, all the data-parallel and intensive\r
+functions of an application running on the CPU are off-loaded onto\r
+the GPU in order to accelerate their computations. A similar\r
+data-parallel function is executed on a GPU as a kernel by\r
+thousands or even millions of parallel threads, grouped together\r
+as a grid of thread blocks. Therefore, each SM of the GPU executes\r
+one or more thread blocks in SIMD fashion (Single  Instruction,\r
+Multiple Data) and in turn each SP of a GPU SM runs one or more\r
+threads within a block in SIMT fashion (Single Instruction,\r
+Multiple threads). Indeed at any given clock cycle, the threads\r
+execute the same instruction of a kernel, but each of them\r
+operates on different data.\r
+ GPUs only work on data filled in their\r
+global memories and the final results of their kernel executions\r
+must be communicated to their CPUs. Hence, the data must be\r
+transferred in and out of the GPU. However, the speed of memory\r
+copy between the GPU and the CPU is slower than the memory\r
+bandwidths of the GPU memories and, thus, it dramatically affects\r
+the performances of GPU computations. Accordingly, it is necessary\r
+to limit data transfers between the GPU and its CPU during the\r
+computations.\r
+\subsection{Background on the CUDA Programming Model}\r
+\r
+The CUDA programming model is similar in style to a single program\r
+multiple-data (SPMD) softwaremodel. The GPU is treated as a\r
+coprocessor that executes data-parallel kernel functions. CUDA\r
+provides three key abstractions, a hierarchy of thread groups,\r
+shared memories, and barrier synchronization. Threads have a three\r
+level hierarchy. A grid is a set of thread blocks that execute a\r
+kernel function. Each grid consists of blocks of threads. Each\r
+block is composed of hundreds of threads. Threads within one block\r
+can share data using shared memory and can be synchronized at a\r
+barrier. All threads within a block are executed concurrently on a\r
+multithreaded architecture.The programmer specifies the number of\r
+threads per block, and the number of blocks per grid. A thread in\r
+the CUDA programming language is much lighter weight than a thread\r
+in traditional operating systems. A thread in CUDA typically\r
+processes one data element at a time. The CUDA programming model\r
+has two shared read-write memory spaces, the shared memory space\r
+and the global memory space. The shared memory is local to a block\r
+and the global memory space is accessible by all blocks. CUDA also\r
+provides two read-only memory spaces, the constant space and the\r
+texture space, which reside in external DRAM, and are accessed via\r
+read-only caches\r
+\r
+\subsection{A parallel implementation of the Aberth's method }\r
+\subsection{A CUDA implementation of the Aberth's method }\r
+\subsection{A GPU implementation of the Aberth's method }\r
+\subsubsection{the  step to parallelize}\r
+\subsubsection{the kernel corresponding }\r
+\subsubsection{Comparison between sequential algorithm and GPU algorithm }\r

 \bibliographystyle{plain}\r
 \bibliography{biblio}\r
 %% \begin{thebibliography}{2}\r
 \r
 %% \bibitem [1] {1} O. Aberth, Iteration Methods for Finding\r
 \r
 \bibliographystyle{plain}\r
 \bibliography{biblio}\r
 %% \begin{thebibliography}{2}\r
 \r
 %% \bibitem [1] {1} O. Aberth, Iteration Methods for Finding\r
 \r

-\r

 %% all Zeros of a Polynomial Simultaneously, Math. Comput. 27, 122\r
 %% (1973) 339\96344.\r
 \r
 %% all Zeros of a Polynomial Simultaneously, Math. Comput. 27, 122\r
 %% (1973) 339\96344.\r
 \r


@@ -245,17 +404,9 @@ this solution is applying when it is necessary
 %% jede ganze rationale function einer veranderlichen dagestellt\r
 %% werden kann als ein product aus linearen functionen derselben\r
 %% veranderlichen, Ges. Werke 3, 251-269.\r
 %% jede ganze rationale function einer veranderlichen dagestellt\r
 %% werden kann als ein product aus linearen functionen derselben\r
 %% veranderlichen, Ges. Werke 3, 251-269.\r

-%% \bibitem [13] {13} Freeman, T. L. (1989), Calculating polynomial zeros on a\r
-%% local memory parallel computer, Parallel Computing 12, 351-358.\r

 \r
 \r

-%% \bibitem [14] {14} Freeman, T. L., Brankin, R. K. (1990), Asynchronous\r
-%% polynomial zero-finding algorithms, Parallel Computing 17,\r
-%% 673-681.\r

 \r
 \r

-%% \bibitem [15] {15} Raphaël,C. François,S. (2001), Extraction de racines dans des\r
-%% polynômes creux de degré élevé. RSRCP (Réseaux et Systèmes\r
-%% Répartis, Calculateurs Parallèles), Numéro thématique :\r
-%% Algorithmes itératifs parallèles et distribués, 13(1):67--81.\r
+%%\r

 \r
 %% \bibitem [16]{16} Kahina, G. Raphaël, C. Abderrahmane, S. A\r
 %% parallel implementation of the Durand-Kerner algorithm for\r
 \r
 %% \bibitem [16]{16} Kahina, G. Raphaël, C. Abderrahmane, S. A\r
 %% parallel implementation of the Durand-Kerner algorithm for\r


@@ -272,5 +423,67 @@ this solution is applying when it is necessary
 %%200.\r
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 %%Bull. A.M.S., 47 (1941) 742\96746.\r
 %%200.\r
 %% \bibitem [19] {19} A. Ostrowski, On a Theorem by J.L. Walsh Concerning the Moduli of Roots of Algebraic Equations,\r
 %%Bull. A.M.S., 47 (1941) 742\96746.\r

+\r
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+%%numerical analysis. SIAM Rev 524\96547\r
+\r
+%ù\bibitem [22] {22}Bini DA, Gemignani L (2004) Inverse power and\r
+%%Durand\96Kerner iterations for univariate polynomial root-finding.\r
+%%Comput Math Appl 47:447\96459\r
+\r
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+%algorithm for determining all roots of a polynomial with real\r
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+%%computer communications. Plenum, New York\r
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+%%on an MIMD multicomputer. Parallel Comput 15:75\9685\r
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+%%(MMT). In: Proc of the 2nd int conference on information\r
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+%%multiple roots of polynomials. Lect Notes Comput Sci 5262:674\96681\r
+\r
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+%%\bibitem [43] {43}Polynomial interpolation and polynomial root finding on OTIS-Mesh.\r
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+%%India, December 14\9616, pp 189\96196\r
+\r
+\r
+\r

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