X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/kahina_paper1.git/blobdiff_plain/e6206cbc48d80bf6ff86c59ba812f861c2b1cb17..19499f903293ae4b6d3313414aef5a790c81cb34:/paper.tex?ds=sidebyside diff --git a/paper.tex b/paper.tex index c49a705..0d2bbd6 100644 --- a/paper.tex +++ b/paper.tex @@ -136,7 +136,7 @@ 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 -be solved by direct methods. Since then, mathmathicians have +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. @@ -172,7 +172,7 @@ Aberth~\cite{Aberth73} uses a different iteration formula given as: where $P'(z)$ is the polynomial derivative of $P$ evaluated in the point $z$. -Aberth, Ehrlich and Farmer-Loizou~\cite{Loizon83} have proved that +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. @@ -183,24 +183,23 @@ drastically increases like the degrees of high polynomials. It is expected that parallelization of these algorithms will improve the convergence time. -Many authors have dealt with the parallelisation of +Many authors have dealt with the parallelization of simultaneous methods, i.e. that find all the zeros simultaneously. -Freeman~\cite{Freeman89} implemeted and compared DK, EA and another method of the fourth order proposed -by Farmer and Loizou~\cite{Loizon83}, on a 8- processor linear +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 5.5 -(speed-up=(Time on one processor)/(Time on p processors)). Later, +diverges, but the first two methods have speed-up 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. -Couturier and al~\cite{Raphaelall01} proposed two methods of parallelisation for +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 polynomials of degree 10000 in 430 seconds with only 8 +compute the roots of sparse polynomials of degree 10000 in 430 seconds with only 8 personal computers and 2 communications per iteration. Comparing to the sequential implementation -where it takes up to 3300 seconds to obtain the same results, the authors show an interesting speedup, indeed. +where it takes up to 3300 seconds to obtain the same results, the authors show an interesting speedup. -Very few works had been since this last work until the appearing of +Very few works had been performed since this last work until the appearing 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 @@ -210,19 +209,31 @@ computing ability to the massive data computing. Ghidouche and al~\cite{Kahinall14} proposed an implementation of the Durand-Kerner method on GPU. Their 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 about 48000. To our knowledge, it is the first time such high degree polynomials are numerically solved. - - -In this paper, we focus on the implementation of the Ehrlich-Aberth method for -high degree polynomials on GPU. The paper is organized as fellows. Initially, we recall the Ehrlich-Aberth method in Section \ref{sec1}. Improvements for the Ehrlich-Aberth method are proposed in Section \ref{sec2}. Related work to the implementation of simultaneous methods using a parallel approach is presented in Section \ref{secStateofArt}. -In Section \ref{sec5} we propose a parallel implementation of the Ehrlich-Aberth method on GPU and discuss it. Section \ref{sec6} presents and investigates our implementation and experimental study results. Finally, Section\ref{sec7} 6 concludes this paper and gives some hints for future research directions in this topic. - -\section{The Sequential Aberth method} +result showed that a parallel CUDA implementation is about 10 times faster than +the sequential implementation on a single CPU for sparse +polynomials of degree 48000. + + +In this paper, we focus on the implementation of the Ehrlich-Aberth +method for high degree polynomials on GPU. We propose an adaptation of +the exponential logarithm in order to be able to solve sparse and full +polynomial of degree up to $1,000,000$. The paper is organized as +follows. Initially, we recall the Ehrlich-Aberth method in Section +\ref{sec1}. Improvements for the Ehrlich-Aberth method are proposed in +Section \ref{sec2}. Related work to the implementation of simultaneous +methods using a parallel approach is presented in Section +\ref{secStateofArt}. In Section \ref{sec5} we propose a parallel +implementation of the Ehrlich-Aberth method on GPU and discuss +it. Section \ref{sec6} presents and investigates our implementation +and experimental study results. Finally, Section\ref{sec7} 6 concludes +this paper and gives some hints for future research directions in this +topic. + +\section{The Sequential Ehrlich-Aberth method} \label{sec1} A cubically convergent iteration method for finding zeros of -polynomials was proposed by O. Aberth~\cite{Aberth73}. In the fellowing we present the main stages of the running of the Aberth method. +polynomials was proposed by O. Aberth~\cite{Aberth73}. In the +following we present the main stages of our implementation the Ehrlich-Aberth method. %The Aberth method is a purely algebraic derivation. %To illustrate the derivation, we let $w_{i}(z)$ be the product of linear factors @@ -258,10 +269,10 @@ The initialization of a polynomial p(z) is done by setting each of the $n$ compl \subsection{Vector $z^{(0)}$ Initialization} -Like for any iterative method, we need to choose $n$ initial guess points $z^{(0)}_{i}, i = 1, . . . , n.$ +As for any iterative method, we need to choose $n$ initial guess points $z^{(0)}_{i}, i = 1, . . . , n.$ The initial guess is very important since the number of steps needed by the iterative method to reach a given approximation strongly depends on it. -In~\cite{Aberth73} the Aberth iteration is started by selecting $n$ +In~\cite{Aberth73} the Ehrlich-Aberth iteration is started by selecting $n$ equi-spaced points on a circle of center 0 and radius r, where r is an upper bound to the moduli of the zeros. Later, Bini and al.~\cite{Bini96} performed this choice by selecting complex numbers along different @@ -292,20 +303,23 @@ Here we give a second form of the iterative function used by Ehrlich-Aberth meth EA2: z^{k+1}=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 \end{equation} -we notice that the function iterative in Eq.~\ref{Eq:Hi} it the same those presented in Eq.~\ref{Eq:EA}, but we prefer used the last one seen the advantage of its use to improve the Ehrlich-Aberth method and resolve very high degrees polynomials. More detail in the section ~\ref{sec2}. +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 +Ehrlich-Aberth method and find the roots of very high degrees polynomials. More +details are given in Section ~\ref{sec2}. \subsection{Convergence Condition} -The convergence condition determines the termination of the algorithm. It consists in stopping from running the iterative function when the roots are sufficiently stable. We consider that the method converges sufficiently when: +The convergence condition determines the termination of the algorithm. It consists in stopping the iterative function when the roots are sufficiently stable. We consider that the method converges sufficiently when: \begin{equation} \label{eq:Aberth-Conv-Cond} -\forall i \in -[1,n];\frac{z_{i}^{k}-z_{i}^{k-1}}{z_{i}^{k}}<\xi +\forall i \in [1,n];\vert\frac{z_{i}^{k}-z_{i}^{k-1}}{z_{i}^{k}}\vert<\xi \end{equation} \section{Improving the Ehrlich-Aberth Method for high degree polynomials with exp.log formulation} \label{sec2} -The Ehrlich-Aberth method implementation suffers of overflow problems. This +With high degree polynomial, the Ehrlich-Aberth method implementation, +as well as the Durand-Kerner implement, suffers from overflow problems. This situation occurs, for instance, in the case where a polynomial having positive coefficients and a large degree is computed at a point $\xi$ where $|\xi| > 1$, where $|x|$ stands for the modolus of a complex $x$. Indeed, the limited number in the @@ -325,7 +339,7 @@ propose to use the logarithm and the exponential of a complex in order to comput \begin{align} \label{defexpcomplex} \forall(x,y)\in R^{*2}; \exp(x+i.y) & = \exp(x).\exp(i.y)\\ - & =\exp(x).\cos(y)+i.\exp(x).\sin(y)\label{defexpcomplex} + & =\exp(x).\cos(y)+i.\exp(x).\sin(y)\label{defexpcomplex1} \end{align} %%\end{equation} @@ -333,7 +347,7 @@ Using the logarithm (eq.~\ref{deflncomplex}) and the exponential (eq.~\ref{defex 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 logarithm: +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} @@ -346,7 +360,7 @@ where: \begin{equation} \label{Log_H1} -Q(z^{k}_{i})=\exp\left( \ln (p(z^{k}_{i}))-\ln(p'(z^{k}^{i}))+\ln \left( +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). \end{equation} @@ -365,7 +379,7 @@ R = exp(log(DBL_MAX)/(2*n) ); \label{secStateofArt} The main problem of simultaneous methods is that the necessary time needed for convergence is increased when we increase -the degree of the polynomial. The parallelisation of these +the degree of the polynomial. The parallelization of these algorithms is expected to improve the convergence time. Authors usually adopt one of the two following approaches to parallelize root finding algorithms. The first approach aims at reducing the total number of @@ -377,9 +391,9 @@ in~\cite{Benall68,Jana06,Janall99,Riceall06}. There are many schemes for the simultaneous approximation of all roots of a given polynomial. Several works on different methods and issues of root -finding have been reported in~\cite{Azad07, Gemignani07, Kalantari08, Skachek08, Zhancall08, Zhuall08}. However, Durand-Kerner and Ehrlich-Aberth methods are the most practical choices among +finding have been reported in~\cite{Azad07, Gemignani07, Kalantari08, Zhancall08, Zhuall08}. However, Durand-Kerner and Ehrlich-Aberth methods are the most practical choices among them~\cite{Bini04}. These two methods have been extensively -studied for parallelization due to their intrinsics, i.e. the +studied for parallelization due to their intrinsics parallelism, i.e. the computations involved in both methods has some inherent parallelism that can be suitably exploited by SIMD machines. Moreover, they have fast rate of convergence (quadratic for the @@ -398,21 +412,26 @@ 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 doen not mention implementations able to compute the roots of -large degree polynomials (higher then 1000) and within small or at least tractable times. Finding polynomial roots rapidly and accurately is the main objective of our work. +%Until very recently, the literature did not mention implementations +%able to compute the roots of large degree polynomials (higher then +%1000) and within small or at least tractable times. + +Finding polynomial roots rapidly and accurately is the main objective of our work. With the advent of CUDA (Compute Unified Device Architecture), finding the roots of polynomials receives a new attention because of the new possibilities to solve higher degree polynomials in less time. 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. In this paper we present a parallel implementation of Ehlisch-Aberth method on -GPUs, which details are discussed in the sequel. +polynomials of 48000. +%In this paper we present a parallel implementation of Ehrlich-Aberth +%method on GPUs for sparse and full polynomials with high degree (up +%to $1,000,000$). \section {A CUDA parallel Ehrlich-Aberth method} In the following, we describe the parallel implementation of Ehrlich-Aberth method on GPU -for solving high degree polynomials. First, the hardware and software of the GPUs are presented. Then, a CUDA parallel Ehrlich-Aberth method are presented. +for solving high degree polynomials (up to $1,000,000$). First, the hardware and software of the GPUs are presented. Then, the CUDA parallel Ehrlich-Aberth method is presented. \subsection{Background on the GPU architecture} A GPU is viewed as an accelerator for the data-parallel and @@ -470,41 +489,48 @@ provides two read-only memory spaces, the constant space and the texture space, which reside in external DRAM, and are accessed via read-only caches. -\section{ The implementation of Aberth method on GPU} +\section{ Implementation of Ehrlich-Aberth method on GPU} \label{sec5} %%\subsection{A CUDA implementation of the Aberth's method } %%\subsection{A GPU implementation of the Aberth's method } -\subsection{A sequential Aberth algorithm} -The main steps of Aberth method are shown in Algorithm.~\ref{alg1-seq} : - +\subsection{Sequential Ehrlich-Aberth algorithm} +The main steps of Ehrlich-Aberth method are shown in Algorithm.~\ref{alg1-seq} : +%\LinesNumbered \begin{algorithm}[H] \label{alg1-seq} -%\LinesNumbered -\caption{A sequential algorithm to find roots with the Aberth method} -\KwIn{$Z^{0}$(Initial root's vector),$\varepsilon$ (error tolerance threshold),P(Polynomial to solve)} -\KwOut {Z(The solution root's vector)} +\caption{A sequential algorithm to find roots with the Ehrlich-Aberth method} + +\KwIn{$Z^{0}$ (Initial root's vector), $\varepsilon$ (error tolerance + threshold), P (Polynomial to solve), $\Delta z_{max}$ (maximum value + of stop condition), k (number of iteration), n (Polynomial's degrees)} +\KwOut {Z (The solution root's vector), ZPrec (the previous solution root's vector)} \BlankLine Initialization of the coefficients of the polynomial to solve\; Initialization of the solution vector $Z^{0}$\; +$\Delta z_{max}=0$\; + k=0\; -\While {$\Delta z_{max}\succ \epsilon$}{ +\While {$\Delta z_{max} > \varepsilon$}{ Let $\Delta z_{max}=0$\; \For{$j \gets 0 $ \KwTo $n$}{ -$ZPrec\left[j\right]=Z\left[j\right]$\; -$Z\left[j\right]=H\left(j,Z\right)$\; +$ZPrec\left[j\right]=Z\left[j\right]$;// save Z at the iteration k.\ + +$Z\left[j\right]=H\left(j,Z\right)$;//update Z with the iterative function.\ } +k=k+1\; \For{$i \gets 0 $ \KwTo $n-1$}{ -$c=\frac{\left|Z\left[i\right]-ZPrec\left[i\right]\right|}{Z\left[i\right]}$\; +$c= testConverge(\Delta z_{max},ZPrec\left[j\right],Z\left[j\right])$\; \If{$c > \Delta z_{max}$ }{ $\Delta z_{max}$=c\;} } + } \end{algorithm} @@ -522,48 +548,60 @@ With the Gauss-Seidel iteration, we have: 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} %%Here a finiched my revision %% -Using Equation.~\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 its ancestor (for solving linear systems of equations), it uses the most fresh computed roots $z^{k+1}_{i}$. +Using Equation.~\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 $z^{k+1}_{i}$. The $4^{th}$ step of the algorithm checks the convergence condition using Equation.~\ref{eq:Aberth-Conv-Cond}. Both steps 3 and 4 use 1 thread to compute all the $n$ roots on CPU, which is very harmful for performance in case of the large degree polynomials. -\subsection{A Parallel implementation with CUDA } +\subsection{Parallel implementation with CUDA } On the CPU, both steps 3 and 4 contain the loop \verb=for= and a single thread executes all the instructions in the loop $n$ times. In this subsection, we explain how the GPU architecture can compute this loop and reduce the execution time. -In the GPU, the schduler assigns the execution of this loop to a group of threads organised as a grid of blocks with block containing a number of threads. All threads within a block are executed concurrently in parallel. The instructions run on the GPU are grouped in special function called kernels. It's up to the programmer, to describe the execution context, that is the size of the Grid, the number of blocks and the number of threads per block upon the call of a given kernel, according to a special syntax defined by CUDA. +In the GPU, the scheduler assigns the execution of this loop to a +group of threads organised as a grid of blocks with block containing a +number of threads. All threads within a block are executed +concurrently in parallel. The instructions run on the GPU are grouped +in special function called kernels. With CUDA, a programmer must +describe the kernel execution context: the size of the Grid, the number of blocks and the number of threads per block. -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. +%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 Aberth algorithm usind CUDA. +Algorithm~\ref{alg2-cuda} shows a sketch of the Ehrlich-Aberth algorithm using CUDA. \begin{algorithm}[H] \label{alg2-cuda} %\LinesNumbered -\caption{CUDA Algorithm to find roots with the Aberth method} +\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)} +\KwIn{$Z^{0}$ (Initial root's vector), $\varepsilon$ (error tolerance threshold), P(Polynomial to solve), $\Delta z_{max}$ (maximum value of stop condition)} -\KwOut {Z(The solution root's vector)} +\KwOut {Z (The solution root's vector)} \BlankLine -Initialization of the coeffcients of the polynomial to solve\; +Initialization of the coefficients of the polynomial to solve\; Initialization of the solution vector $Z^{0}$\; Allocate and copy initial data to the GPU global memory\; - +k=0\; \While {$\Delta z_{max}\succ \epsilon$}{ Let $\Delta z_{max}=0$\; -$ kernel\_save(d\_z^{k-1})$\; -$ kernel\_update(d\_z^{k})$\; -$kernel\_testConverge(\Delta z_{max},d_z^{k},d_z^{k-1})$\; +$ kernel\_save(d\_Z^{k-1})$\; +k=k+1\; +$ kernel\_update(d\_Z^{k})$\; +$kernel\_testConverge(\Delta z_{max},d\_Z^{k},d\_Z^{k-1})$\; + } \end{algorithm} ~\\ After the initialisation 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 $z^{k}$, according to Algorithm~\ref{alg3-update}. We notice that the update kernel is called in two forms, separated with the value of \emph{R} which determines the radius beyond which we apply the logarithm computation of the power of a complex. +The second kernel executes the iterative function $H$ and updates +$z^{k}$, according to Algorithm~\ref{alg3-update}. We notice that the +update kernel is called in two forms, separated with the value of +\emph{R} which determines the radius beyond which we apply the +exponential logarithm algorithm. \begin{algorithm}[H] \label{alg3-update} @@ -573,11 +611,15 @@ The second kernel executes the iterative function $H$ and updates $z^{k}$, accor \eIf{$(\left|Z^{(k)}\right|<= R)$}{ $kernel\_update(d\_z^{k})$\;} { -$kernel\_update\_Log(d\_z^{k})$\; +$kernel\_update\_ExpoLog(d\_z^{k})$\; } \end{algorithm} -The first form executes formula \ref{eq:SimplePolynome} 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 formulas (Eq.~\ref{deflncomplex},Eq.~\ref{defexpcomplex}). The radius $R$ is evaluated as : +The first form executes formula \ref{eq:SimplePolynome} 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. @@ -626,12 +668,6 @@ All experimental results obtained from the simulations are made in double precis \subsection{The execution time in seconds of Ehrlich-Aberth algorithm on CPU OpenMP (1 core, 4 cores) vs. on a Tesla GPU} -%\begin{figure}[H] -%\centering - % \includegraphics[width=0.8\textwidth]{figures/Compar_EA_algorithm_CPU_GPU} -%\caption{The execution time in seconds of Ehrlich-Aberth algorithm on CPU core vs. on a Tesla GPU} -%\label{fig:01} -%\end{figure} \begin{figure}[H] \centering @@ -652,7 +688,7 @@ For that, we notice that the maximum number of threads per block for the Nvidia \centering \includegraphics[width=0.8\textwidth]{figures/influence_nb_threads} \caption{Influence of the number of threads on the execution times of different polynomials (sparse and full)} -\label{fig:01} +\label{fig:02} \end{figure} 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. @@ -664,24 +700,15 @@ In this experiment we report the performance of log.exp solution describe in ~\r \centering \includegraphics[width=0.8\textwidth]{figures/sparse_full_explog} \caption{The impact of exp-log solution to compute very high degrees of polynomial.} -\label{fig:01} +\label{fig:03} \end{figure} -The figure 3, show a comparison between the execution time of the Ehrlich-Aberth algorithm applying log-exp solution and the execution time of the Ehrlich-Aberth algorithm without applying log-exp solution, with full and sparse polynomials degrees. We can see that the execution time for the both algorithms are the same while the full polynomials degrees are less than 4000 and full polynomials are less than 150,000. After,we show clearly that the classical version of Ehrlich-Aberth algorithm (without applying log.exp) stop to converge and can not solving any polynomial sparse or full. In counterpart, the new version of Ehrlich-Aberth algorithm (applying log.exp solution) can solve very high and large full polynomial exceed 100,000 degrees. +The figure 3, show a comparison between the execution time of the Ehrlich-Aberth algorithm applying exp.log solution and the execution time of the Ehrlich-Aberth algorithm without applying exp.log solution, with full and sparse polynomials degrees. We can see that the execution time for the both algorithms are the same while the full polynomials degrees are less than 4000 and full polynomials are less than 150,000. After,we show clearly that the classical version of Ehrlich-Aberth algorithm (without applying log.exp) stop to converge and can not solving any polynomial sparse or full. In counterpart, the new version of Ehrlich-Aberth algorithm (applying log.exp solution) can solve very high and large full polynomial exceed 100,000 degrees. in fact, when the modulus of the roots are up than \textit{R} given in ~\ref{R},this exceed the limited number in the mantissa of floating points representations and can not compute the iterative function given in ~\ref{eq:Aberth-H-GS} to obtain the root solution, who justify the divergence of the classical Ehrlich-Aberth algorithm. However, applying log.exp solution given in ~\ref{sec2} took into account the limit of floating using the iterative function in(Eq.~\ref{Log_H1},Eq.~\ref{Log_H2} and allows to solve a very large polynomials degrees . -%\begin{figure}[H] -\%centering - %\includegraphics[width=0.8\textwidth]{figures/log_exp_Sparse} -%\caption{The impact of exp-log solution to compute very high degrees of polynomial.} -%\label{fig:01} -%\end{figure} -%we report the performances of the exp.log for the Ehrlich-Aberth algorithm for solving very high degree of polynomial. - - \subsection{A comparative study between Ehrlich-Aberth algorithm and Durand-kerner algorithm} In this part, we are interesting to compare the simultaneous methods, Ehrlich-Aberth and Durand-Kerner in parallel computer using GPU. We took into account the execution time, the number of iteration and the polynomial's size. for the both sparse and full polynomials. @@ -689,7 +716,7 @@ In this part, we are interesting to compare the simultaneous methods, Ehrlich-Ab \centering \includegraphics[width=0.8\textwidth]{figures/EA_DK} \caption{The execution time of Ehrlich-Aberth versus Durand-Kerner algorithm on GPU} -\label{fig:01} +\label{fig:04} \end{figure} This figure show the execution time of the both algorithm EA and DK with sparse polynomial degrees ranging from 1000 to 1000000. We can see that the Ehrlich-Aberth algorithm are faster than Durand-Kerner algorithm, with an average of 25 times as fast. Then, when degrees of polynomial exceed 500000 the execution time with EA is of the order 100 whereas DK passes in the order 1000. %with double precision not exceed $10^{-5}$. @@ -698,7 +725,7 @@ This figure show the execution time of the both algorithm EA and DK with sparse \centering \includegraphics[width=0.8\textwidth]{figures/EA_DK_nbr} \caption{The iteration number of Ehrlich-Aberth versus Durand-Kerner algorithm} -\label{fig:01} +\label{fig:05} \end{figure} %\subsubsection{The execution time of Ehrlich-Aberth algorithm on OpenMP(1 core, 4 cores) vs. on a Tesla GPU}