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7 \title{Paper1_kahina}
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9 \section{Root finding problem}
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10 we consider a polynomial of degree \textit{n} having coefficients
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11 in the complex \textit{C} and zeros $\alpha
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12 _{i},\textit{i=1,...,n}$. \\
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14 {\Large$p(x)=\sum{a_{i}x^{i}}=a_{n}\prod(x-\alpha_{i}),
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18 the root finding problem consist to find
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19 all n root of \textit{p(x)}. the problem of finding a root is
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20 equivalent to the problem of finding a fixed-point. To see this
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21 consider the fixed-point problem of finding the n-dimensional
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26 where $g : C^{n}\longrightarrow C^{n}$. Note that we can easily
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27 rewrite this fixed-point problem as a root-finding problem by
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28 setting $f (x) = x-g(x)$ and likewise we can recast the
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29 root-finding problem into a fixed-point problem by setting
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33 Often it will not be possible to solve such nonlinear equation
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34 root-finding problems analytically. When this occurs we turn to
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35 numerical methods to approximate the solution. Generally speaking,
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36 algorithms for solving problems numerically can be divided into
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37 two main groups: direct methods and iterative methods.
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39 Direct methods exist only for $n \leqslant4$,solved in closed form by G. Cardano
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40 in the mid-16th century. However, N.H. Abel in the early 19th
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41 century showed that polynomials of degree five or more could not
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42 be solved by directs methods. Since then researchers have
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43 concentrated on numerical (iterative) methods such as the famous
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44 Newton s method, Bernoulli s method of the 18th, and Graeffe s.
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45 With the advent of electronic computers, different methods has
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46 been developed such as the Jenkins-Traub method, Larkin s method,
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47 Muller s method, and several methods for simultaneous
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48 approximation of all the roots, starting with the Durand-Kerner
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52 $ Z_{i}=Z_{i}-\frac{P(Z_{i})}{\prod_{i\neq j}(z_{i}-z_{j})} $
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55 This formula is mentioned for the first time from Weiestrass [12]
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56 as part of the fundamental theorem of Algebra and is rediscovered
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57 from Ilieff [2], Docev [3], Durand [4], Kerner [5]. Another method
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58 discovered from Borsch-Supan [6] and also described and brought in
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59 the following form from Ehrlich [7] and Aberth~\cite{Aberth73}.
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62 $ Z_{i}=Z_{i}-\frac{1}{{\frac {P'(Z_{i})}
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63 {P(Z_{i})}}-{\sum_{i\neq j}(z_{i}-z_{j})}} $
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66 Aberth, Ehrlich and Farmer-Loizou [10] have proved that the above
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67 method has cubic order of convergence for simple roots.
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70 Iterative methods raise several problem when implemented e.g.
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71 specific sizes of numbers must be used to deal with this
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72 difficulty.Moreover,the convergence time of iterative methods
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73 drastically increase like the degrees of high polynomials. The
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74 parallelization of these algorithms will improve the convergence
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77 Many authors have treated the problem of parallelization of
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78 simultaneous methods. Freeman [13] has tested the DK method, EA
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79 method and another method of the fourth order proposed from Farmer
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80 and Loizou [10],on a 8- processor linear chain, for polynomial of
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81 degree up to 8. The third method often diverges, but the first two
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82 methods have speed-up 5.5 (speed-up=(Time on one processor)/(Time
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83 on p processors)). Later Freeman and Bane [14] consider
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84 asynchronous algorithms, in which each processor continues to
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85 update its approximations even although the latest values of other
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86 $z_i((k))$ have not received from the other processors, in
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87 difference with the synchronous version where it would wait. in
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88 [15]proposed two methods of parallelization for architecture with
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89 shared memory and distributed memory,it able to compute the root
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90 of polynomial degree 10000 on 430 s with only 8 pc and 2
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91 communications per iteration. Compare to the sequential it take
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92 3300 s to obtain the same results.
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94 After this few works discuses this problem until the apparition of
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95 the Compute Unified Device Architecture (CUDA) [19],a parallel
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96 computing platform and a programming model invented by NVIDIA. the
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97 computing ability of GPU has exceeded the counterpart of CPU. It
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98 is a waste of resource to be just a graphics card for GPU. CUDA
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99 adopts a totally new computing architecture to use the hardware
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100 resources provided by GPU in order to offer a stronger computing
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101 ability to the massive data computing.
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104 Indeed [16]proposed the implementation of the Durand-Kerner method
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105 on GPU (Graphics Processing Unit). The main result prove that a
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106 parallel implementation is 10 times as fast as the sequential
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107 implementation on a single CPU for high degree polynomials that is
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108 greater than about 48000.
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110 The mean part of our work is to implement the Aberth method on GPU
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111 and compare it with the Durand Kerner
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112 implementation.................To be continued..................
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116 \bibliographystyle{plain}
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117 \bibliography{biblio}
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118 %% \begin{thebibliography}{2}
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120 %% \bibitem [1] {1} O. Aberth, Iteration Methods for Finding
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121 %% all Zeros of a Polynomial Simultaneously, Math. Comput. 27, 122
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122 %% (1973) 339
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124 %% \bibitem [2] {2} Ilieff, L. (1948-50), On the approximations of Newton, Annual
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125 %% Sofia Univ. 46, 167-171.
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127 %% \bibitem [3] {3} Docev, K. (1962), An alternative method of Newton for
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128 %% simultaneous calculation of all the roots of a given algebraic
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129 %% equation, Phys. Math. J., Bulg. Acad. Sci. 5, 136-139.
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131 %% \bibitem [4]{4} Durand, E. (1960), Solution Numerique des Equations
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132 %% Algebriques, Vol. 1, Equations du Type F(x)=0, Racines d'une
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133 %% Polynome. Masson, Paris.
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135 %% \bibitem [4] {4} Aberth, O. (1973), Iterative methods for finding all zeros of
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136 %% a polynomial simultaneously, Math. Comp. 27, 339-344.
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138 %% \bibitem [5] {5} Kerner, I.O. (1966), Ein Gesamtschritteverfahren zur
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139 %% Berechnung der Nullstellen von Polynomen, Numer. Math. 8, 290-294.
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141 %% \bibitem [6]{6} Borch-Supan, W. (1963), A posteriori error for the zeros of
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142 %% polynomials, Numer. Math. 5, 380-398.
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144 %% \bibitem [7] {7} Ehrlich, L. W. (1967), A modified Newton method for
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145 %% polynomials, Comm. Ass. Comput. Mach. 10, 107-108.
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149 %% \bibitem [10] {10}Loizon, G. (1983), Higher-order iteration functions for
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150 %% simultaneously approximating polynomial zeros, Intern. J. Computer
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151 %% Math. 14, 45-58.
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153 %% \bibitem [11]{11} E. Durand, Solutions numŽeriques des Žequations algŽebriques,
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154 %% Tome 1: Equations du type F(X) = 0; Racines d
\92un polyn
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155 %% Masson, Paris 1960.
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157 %% \bibitem [12] {12} Weierstrass, K. (1903), Neuer Beweis des Satzes, dass
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158 %% jede ganze rationale function einer veranderlichen dagestellt
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159 %% werden kann als ein product aus linearen functionen derselben
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160 %% veranderlichen, Ges. Werke 3, 251-269.
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161 %% \bibitem [13] {13} Freeman, T. L. (1989), Calculating polynomial zeros on a
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162 %% local memory parallel computer, Parallel Computing 12, 351-358.
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164 %% \bibitem [14] {14} Freeman, T. L., Brankin, R. K. (1990), Asynchronous
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165 %% polynomial zero-finding algorithms, Parallel Computing 17,
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168 %% \bibitem [15] {15} Raphaël,C. François,S. (2001), Extraction de racines dans des
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169 %% polynômes creux de degré élevé. RSRCP (Réseaux et Systèmes
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170 %% Répartis, Calculateurs Parallèles), Numéro thématique :
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171 %% Algorithmes itératifs parallèles et distribués, 13(1):67--81.
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173 %% \bibitem [16]{16} Kahina, G. Raphaël, C. Abderrahmane, S. A
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174 %% parallel implementation of the Durand-Kerner algorithm for
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175 %% polynomial root-finding on GPU. In INDS 2014, Int. Conf. on
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176 %% advanced Networking, Distributed Systems and Applications, Bejaia,
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177 %% Algeria, pages 53--57, June 2014. IEEE
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179 %% \end{thebibliography}
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