X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/kahina_paper1.git/blobdiff_plain/1df92e2629fef95f9b236c8d952d94c08f5f34a0..0b48d3784303d0beafc660614dfc6ec5d1232f01:/paper.tex diff --git a/paper.tex b/paper.tex index 1d70450..1a30788 100644 --- a/paper.tex +++ b/paper.tex @@ -82,7 +82,7 @@ \begin{abstract} Polynomials are mathematical algebraic structures that play a great -role in science and engineering. Finding roots of high degree +role in science and engineering. Finding the roots of high degree polynomials is computationally demanding. In this paper, we present the results of a parallel implementation of the Ehrlich-Aberth algorithm for the root finding problem for high degree polynomials on @@ -101,15 +101,15 @@ Polynomial root finding, Iterative methods, Ehrlich-Aberth, Durand-Kerner, GPU \linenumbers -\section{The problem of finding roots of a polynomial} -Polynomials are mathematical algebraic structures used in science and engineering to capture physical phenomenons and to express any outcome in the form of a function of some unknown variables. Formally speaking, a polynomial $p(x)$ of degree \textit{n} having $n$ coefficients in the complex plane \textit{C} is : +\section{The problem of finding the roots of a polynomial} +Polynomials are mathematical algebraic structures used in science and engineering to capture physical phenomena and to express any outcome in the form of a function of some unknown variables. Formally speaking, a polynomial $p(x)$ of degree \textit{n} having $n$ coefficients in the complex plane \textit{C} is : %%\begin{center} \begin{equation} {\Large p(x)=\sum_{i=0}^{n}{a_{i}x^{i}}}. \end{equation} %%\end{center} -The root finding problem consists in finding the values of all the $n$ values of the variable $x$ for which \textit{p(x)} is nullified. Such values are called zeroes of $p$. If zeros are $\alpha_{i},\textit{i=1,...,n}$ the $p(x)$ can be written as : +The root finding problem consists in finding the values of all the $n$ values of the variable $x$ for which \textit{p(x)} is nullified. Such values are called zeros of $p$. If zeros are $\alpha_{i},\textit{i=1,...,n}$ the $p(x)$ can be written as : \begin{equation} {\Large p(x)=a_{n}\prod_{i=1}^{n}(x-\alpha_{i}), a_{0} a_{n}\neq 0}. \end{equation} @@ -127,22 +127,22 @@ root-finding problem into a fixed-point problem by setting : $g(x)= f(x)-x$. \end{center} -Often it is not be possible to solve such nonlinear equation -root-finding problems analytically. When this occurs we turn to +It is often impossible to solve such nonlinear equation +root-finding problems analytically. When this occurs, we turn to numerical methods to approximate the solution. Generally speaking, algorithms for solving problems can be divided into two main groups: direct methods and iterative methods. -\\ -Direct methods exist only for $n \leq 4$, solved in closed form by G. Cardano -in the mid-16th century. However, N. H. Abel in the early 19th -century showed that polynomials of degree five or more could not + +Direct methods only exist for $n \leq 4$, solved in closed form +by G. Cardano in the mid-16th century. However, N. H. Abel in the early 19th +century proved that polynomials of degree five or more could not be solved by direct methods. Since then, mathematicians have focussed on numerical (iterative) methods such as the famous -Newton method, the Bernoulli method of the 18th, and the Graeffe method. +Newton method, the Bernoulli method of the 18th century, and the Graeffe method. Later on, with the advent of electronic computers, other methods have been developed such as the Jenkins-Traub method, the Larkin method, -the Muller method, and several methods for simultaneous +the Muller method, and several other methods for the simultaneous approximation of all the roots, starting with the Durand-Kerner (DK) method: %%\begin{center} @@ -176,30 +176,30 @@ Aberth, Ehrlich and Farmer-Loizou~\cite{Loizou83} have proved that the Ehrlich-Aberth method (EA) has a cubic order of convergence for simple roots whereas the Durand-Kerner has a quadratic order of convergence. -Iterative methods raise several problem when implemented e.g. -specific sizes of numbers must be used to deal with this -difficulty. Moreover, the convergence time of iterative methods +Moreover, the convergence times of iterative methods drastically increases like the degrees of high polynomials. It is expected that the -parallelization of these algorithms will improve the convergence -time. +parallelization of these algorithms will reduce the execution times. Many authors have dealt with the parallelization of simultaneous methods, i.e. that find all the zeros simultaneously. Freeman~\cite{Freeman89} implemented and compared DK, EA and another method of the fourth order proposed -by Farmer and Loizou~\cite{Loizou83}, on a 8-processor linear -chain, for polynomials of degree up to 8. The third method often -diverges, but the first two methods have speed-up equal to 5.5. Later, +by Farmer and Loizou~\cite{Loizou83}, on an 8-processor linear +chain, for polynomials of degree 8. The third method often +diverges, but the first two methods have speed-ups equal to 5.5. Later, Freeman and Bane~\cite{Freemanall90} considered asynchronous algorithms, in which each processor continues to update its -approximations even though the latest values of other $z_i^{k}$ -have not been received from the other processors, in contrast with synchronous algorithms where it would wait those values before making a new iteration. +approximations even though the latest values of other roots +have not yet been received from the other processors. In contrast, +synchronous algorithms wait the computation of all roots at a given +iterations before making a new one. Couturier and al.~\cite{Raphaelall01} proposed two methods of parallelization for a shared memory architecture and for distributed memory one. They were able to compute the roots of sparse polynomials of degree 10,000 in 430 seconds with only 8 -personal computers and 2 communications per iteration. Comparing to the sequential implementation -where it takes up to 3,300 seconds to obtain the same results, the authors show an interesting speedup. +personal computers and 2 communications per iteration. Compared to sequential implementations +where it takes up to 3,300 seconds to obtain the same results, the +authors' work experiment show an interesting speedup. -Very few works had been performed since this last work until the appearing of +Few works have been conducted after those works until the appearance of the Compute Unified Device Architecture (CUDA)~\cite{CUDA10}, a parallel computing platform and a programming model invented by NVIDIA. The computing power of GPUs (Graphics Processing Unit) has exceeded that of CPUs. However, CUDA adopts a totally new computing architecture to use the @@ -231,8 +231,11 @@ topic. \section{Ehrlich-Aberth method} \label{sec1} -A cubically convergent iteration method for finding zeros of -polynomials was proposed by O. Aberth~\cite{Aberth73}. The Ehrlich-Aberth method contain 4 main steps, presented in the following. +A cubically convergent iteration method to find zeros of +polynomials was proposed by O. Aberth~\cite{Aberth73}. The +Ehrlich-Aberth method contains 4 main steps, presented in what +follows. + %The Aberth method is a purely algebraic derivation. %To illustrate the derivation, we let $w_{i}(z)$ be the product of linear factors @@ -300,7 +303,7 @@ Here we give a second form of the iterative function used by Ehrlich-Aberth meth \begin{equation} \label{Eq:Hi} EA2: z^{k+1}_{i}=z_{i}^{k}-\frac{\frac{p(z_{i}^{k})}{p'(z_{i}^{k})}} -{1-\frac{p(z_{i}^{k})}{p'(z_{i}^{k})}\sum_{j=1,j\neq i}^{j=n}{\frac{1}{(z_{i}^{k}-z_{j}^{k})}}}, i=0,. . . .,n +{1-\frac{p(z_{i}^{k})}{p'(z_{i}^{k})}\sum_{j=1,j\neq i}^{j=n}{\frac{1}{(z_{i}^{k}-z_{j}^{k})}}}, i=1,. . . .,n \end{equation} It can be noticed that this equation is equivalent to Eq.~\ref{Eq:EA}, but we prefer the latter one because we can use it to improve the @@ -345,14 +348,12 @@ propose to use the logarithm and the exponential of a complex in order to comput Using the logarithm (eq.~\ref{deflncomplex}) and the exponential (eq.~\ref{defexpcomplex}) operators, we can replace any multiplications and divisions with additions and subtractions. Consequently, computations manipulate lower absolute values and the roots for large polynomial's degrees can be looked for successfully~\cite{Karimall98}. -Applying this solution for the Ehrlich-Aberth method we obtain the -iteration function with exponential and logarithm: +Applying this solution for the iteration function Eq.~\ref{Eq:Hi} of Ehrlich-Aberth method, we obtain the iteration function with exponential and logarithm: %%$$ \exp \bigl( \ln(p(z)_{k})-ln(\ln(p(z)_{k}^{'}))- \ln(1- \exp(\ln(p(z)_{k})-ln(\ln(p(z)_{k}^{'})+\ln\sum_{i\neq j}^{n}\frac{1}{z_{k}-z_{j}})$$ \begin{equation} \label{Log_H2} -EA.EL: z^{k+1}=z_{i}^{k}-\exp \left(\ln \left( -p(z_{i}^{k})\right)-\ln\left(p'(z^{k}_{i})\right)- \ln -\left(1-Q(z^{k}_{i})\right)\right), +EA.EL: z^{k+1}_{i}=z_{i}^{k}-\exp \left(\ln \left( +p(z_{i}^{k})\right)-\ln\left(p'(z^{k}_{i})\right)- \ln\left(1-Q(z^{k}_{i})\right)\right), \end{equation} where: @@ -360,14 +361,15 @@ where: \begin{equation} \label{Log_H1} Q(z^{k}_{i})=\exp\left( \ln (p(z^{k}_{i}))-\ln(p'(z^{k}_{i}))+\ln \left( -\sum_{k\neq j}^{n}\frac{1}{z^{k}_{i}-z^{k}_{j}}\right)\right). +\sum_{i\neq j}^{n}\frac{1}{z^{k}_{i}-z^{k}_{j}}\right)\right)i=1,...,n, \end{equation} -This solution is applied when the root except the circle unit, represented by the radius $R$ evaluated in C language as: +This solution is applied when the root except the circle unit, represented by the radius $R$ evaluated in C language as : +\begin{equation} +\label{R.EL} +R = exp(log(DBL\_MAX)/(2*n) ); +\end{equation} -\begin{verbatim} -R = exp(log(DBL_MAX)/(2*n) ); -\end{verbatim} %\begin{equation} @@ -385,7 +387,7 @@ Authors usually adopt one of the two following approaches to parallelize root finding algorithms. The first approach aims at reducing the total number of iterations as by Miranker ~\cite{Mirankar68,Mirankar71}, Schedler~\cite{Schedler72} and -Winogard~\cite{Winogard72}. The second approach aims at reducing the +Winograd~\cite{Winogard72}. The second approach aims at reducing the computation time per iteration, as reported in~\cite{Benall68,Jana06,Janall99,Riceall06}. @@ -408,9 +410,9 @@ other processors at the end of each iteration (synchronous). Therefore they cause a high degree of memory conflict. Recently the author in~\cite{Mirankar71} proposed two versions of parallel algorithm for the Durand-Kerner method, and Ehrlich-Aberth method on a model of -Optoelectronic Transpose Interconnection System (OTIS).The -algorithms are mapped on an OTIS-2D torus using N processors. This -solution needs N processors to compute N roots, which is not +Optoelectronic Transpose Interconnection System (OTIS). The +algorithms are mapped on an OTIS-2D torus using $N$ processors. This +solution needs $N$ processors to compute $N$ roots, which is not practical for solving polynomials with large degrees. %Until very recently, the literature did not mention implementations %able to compute the roots of large degree polynomials (higher then @@ -423,7 +425,7 @@ In~\cite{Kahinall14} we already proposed the first implementation of a root finding method on GPUs, that of the Durand-Kerner method. The main result showed that a parallel CUDA implementation is 10 times as fast as the sequential implementation on a single CPU for high degree -polynomials of 48000. +polynomials of 48,000. %In this paper we present a parallel implementation of Ehrlich-Aberth %method on GPUs for sparse and full polynomials with high degree (up %to $1,000,000$). @@ -541,20 +543,27 @@ polynomials of 48000. \subsection{Parallel implementation with CUDA } In order to implement the Ehrlich-Aberth method in CUDA, it is -possible to use the Jacobi scheme or the Gauss Seidel one. With the +possible to use the Jacobi scheme or the Gauss-Seidel one. With the Jacobi iteration, at iteration $k+1$ we need all the previous values -$z^{(k)}_{i}$ to compute the new values $z^{(k+1)}_{i}$, that is : +$z^{k}_{i}$ to compute the new values $z^{k+1}_{i}$, that is : \begin{equation} -EAJ: z^{k+1}_{i}=\frac{p(z^{k}_{i})}{p'(z^{k}_{i})-p(z^{k}_{i})\sum^{n}_{j=1 j\neq i}\frac{1}{z^{k}_{i}-z^{k}_{j}}}, i=1,...,n. +EAJ: z^{k+1}_{i}=z_{i}^{k}-\frac{\frac{p(z_{i}^{k})}{p'(z_{i}^{k})}} +{1-\frac{p(z_{i}^{k})}{p'(z_{i}^{k})}\sum_{j=1,j\neq i}^{j=n}{\frac{1}{(z_{i}^{k}-z_{j}^{k})}}}, i=1,. . . .,n. \end{equation} With the Gauss-Seidel iteration, we have: +%\begin{equation} +%\label{eq:Aberth-H-GS} +%EAGS: z^{k+1}_{i}=\frac{p(z^{k}_{i})}{p'(z^{k}_{i})-p(z^{k}_{i})(\sum^{i-1}_{j=1}\frac{1}{z^{k}_{i}-z^{k+1}_{j}}+\sum^{n}_{j=i+1}\frac{1}{z^{k}_{i}-z^{k}_{j}})}, i=1,...,n. +%\end{equation} + \begin{equation} \label{eq:Aberth-H-GS} -EAGS: z^{k+1}_{i}=\frac{p(z^{k}_{i})}{p'(z^{k}_{i})-p(z^{k}_{i})(\sum^{i-1}_{j=1}\frac{1}{z^{k}_{i}-z^{k+1}_{j}}+\sum^{n}_{j=i+1}\frac{1}{z^{k}_{i}-z^{k}_{j}})}, i=1,...,n. +EAGS: z^{k+1}_{i}=z_{i}^{k}-\frac{\frac{p(z_{i}^{k})}{p'(z_{i}^{k})}} +{1-\frac{p(z_{i}^{k})}{p'(z_{i}^{k})}(\sum^{i-1}_{j=1}\frac{1}{z^{k}_{i}-z^{k+1}_{j}}+\sum_{j=i+1}^{j=n}{\frac{1}{(z_{i}^{k}-z_{j}^{k})}})}, i=1,. . . .,n \end{equation} -%%Here a finiched my revision %% + Using Eq.~\ref{eq:Aberth-H-GS} to update the vector solution \textit{Z}, we expect the Gauss-Seidel iteration to converge more quickly because, just as any Jacobi algorithm (for solving linear systems of equations), it uses the most fresh computed roots $z^{k+1}_{i}$. @@ -574,42 +583,52 @@ quickly because, just as any Jacobi algorithm (for solving linear systems of equ %In CUDA programming, all the instructions of the \verb=for= loop are executed by the GPU as a kernel. A kernel is a function written in CUDA and defined by the \verb=__global__= qualifier added before a usual \verb=C= function, which instructs the compiler to generate appropriate code to pass it to the CUDA runtime in order to be executed on the GPU. -Algorithm~\ref{alg2-cuda} shows a sketch of the Ehrlich-Aberth algorithm using CUDA. +Algorithm~\ref{alg2-cuda} shows sketch of the Ehrlich-Aberth method using CUDA. +\begin{enumerate} \begin{algorithm}[H] \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 (the derivative of P), $n$ (Polynomial's degrees), $\Delta z_{max}$ (maximum value of stop condition)} +\KwIn{$Z^{0}$ (Initial root's vector), $\varepsilon$ (Error tolerance + threshold), P (Polynomial to solve), Pu (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)} +\KwOut {$Z$ (Solution root's vector), $ZPrec$ (Previous solution root's vector)} \BlankLine -Initialization of the of P\; -Initialization of the of Pu\; -Initialization of the solution vector $Z^{0}$\; -Allocate and copy initial data to the GPU global memory\; -k=0\; +\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 {$\Delta z_{max} > \epsilon$}{ - Let $\Delta z_{max}=0$\; -$ kernel\_save(ZPrec,Z)$\; -k=k+1\; -$ kernel\_update(Z,P,Pu)$\; -$kernel\_testConverge(\Delta z_{max},Z,ZPrec)$\; +\item Let $\Delta z_{max}=0$\; +\item $ kernel\_save(ZPrec,Z)$\; +\item k=k+1\; +\item $ kernel\_update(Z,P,Pu)$\; +\item $kernel\_testConverge(\Delta z_{max},Z,ZPrec)$\; } -Copy results from GPU memory to CPU memory\; +\item Copy results from GPU memory to CPU memory\; \end{algorithm} +\end{enumerate} ~\\ -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 -update kernel is called in two forms, separated with the value of +After the initialization step, all data of the root finding problem +must be copied from the CPU memory to the GPU global memory. 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 7 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 +10, Algorithm~\ref{alg2-cuda}). + +The second kernel executes the iterative function and updates +$Z$, according to Algorithm~\ref{alg3-update}. We notice that the +update kernel is called in two forms, according to the value \emph{R} which determines the radius beyond which we apply the exponential logarithm algorithm. @@ -619,9 +638,9 @@ exponential logarithm algorithm. \caption{Kernel update} \eIf{$(\left|Z\right|<= R)$}{ -$kernel\_update((Z,P,Pu)$\;} +$kernel\_update(Z,P,Pu)$\;} { -$kernel\_update\_ExpoLog((Z,P,Pu))$\; +$kernel\_update\_ExpoLog(Z,P,Pu)$\; } \end{algorithm} @@ -629,12 +648,10 @@ 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} -(with Eq.~\ref{deflncomplex}, Eq.~\ref{defexpcomplex}). The radius $R$ is evaluated as : - -$$R = \exp( \log(DBL\_MAX) / (2*n) )$$ where $DBL\_MAX$ stands for the maximum representable double value. +(with Eq.~\ref{deflncomplex}, Eq.~\ref{defexpcomplex}). The radius $R$ is evaluated as in Eq.~\ref{R.EL}. The last kernel checks the convergence of the roots after each update -of $Z^{(k)}$, according to formula Eq.~\ref{eq:Aberth-Conv-Cond}. We used the functions of the CUBLAS Library (CUDA Basic Linear Algebra Subroutines) to implement this kernel. +of $Z^{k}$, according to formula Eq.~\ref{eq:Aberth-Conv-Cond}. We used the functions of the CUBLAS Library (CUDA Basic Linear Algebra Subroutines) to implement this kernel. The kernel terminates its computations when all the roots have converged. It should be noticed that, as blocks of threads are @@ -683,7 +700,7 @@ all the coefficients are not null. A full polynomial is defined by: %polynomials. The execution time remains the %element-key which justifies our work of parallelization. For our tests, a CPU Intel(R) Xeon(R) CPU -E5620@2.40GHz and a GPU K40 (with 6 Go of ram) is used. +E5620@2.40GHz and a GPU K40 (with 6 Go of ram) are used. %\subsection{Comparative study} @@ -711,13 +728,33 @@ of the methods are given in Section~\ref{sec:vec_initialization}. on a CPU with OpenMP (1 core, 4 cores) and on a Tesla GPU} \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 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. +%%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 K40 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. +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 40 faster then those implementation on the CPU (4 cores). + + + + +%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 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. +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). 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 1,024, so we varied the number of threads per block from 8 to 1,024. 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] @@ -815,7 +852,8 @@ numerical applications on GPU. In future works, we plan to investigate the possibility of using several multiple GPUs simultaneously, either with multi-GPU machine or -with cluster of GPUs. +with cluster of GPUs. It may also be interesting to study the +implementation of other root finding polynomial methods on GPU.