X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/kahina_paper1.git/blobdiff_plain/84bdc1ad86104770f6df03822fbbd4284fa7a7b1..5870d4e72db0c954d03231f1d807de7aee47ff74:/paper.tex?ds=inline diff --git a/paper.tex b/paper.tex index b3e3076..f66bf99 100644 --- a/paper.tex +++ b/paper.tex @@ -185,22 +185,21 @@ time. 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{Loizon83}, 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 parallelization 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 +209,30 @@ 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 fellowing we present the main stages of the running of 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 @@ -261,7 +271,7 @@ The initialization of a polynomial p(z) is done by setting each of the $n$ compl Like 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 @@ -470,20 +480,20 @@ 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{ The 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{A sequential Ehrlich-Aberth algorithm} +The main steps of Ehrlich-Aberth method are shown in Algorithm.~\ref{alg1-seq} : \begin{algorithm}[H] \label{alg1-seq} %\LinesNumbered -\caption{A sequential algorithm to find roots with the Aberth method} +\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)} \KwOut {Z(The solution root's vector)} @@ -534,12 +544,12 @@ In the GPU, the schduler assigns the execution of this loop to a group of thread 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)} @@ -667,7 +677,7 @@ In this experiment we report the performance of log.exp solution describe in ~\r \label{fig:01} \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 .