[kahina_paper1.git] / Root.tex

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\author{ghidouche}\r
\title{Paper1_kahina}\r
\begin{document}\r
\section{Root finding problem}\r
we consider a polynomial of degree \textit{n} having coefficients\r
in the complex \textit{C} and zeros $\alpha\r
_{i},\textit{i=1,...,n}$. \\\r
\begin{center}\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
all n root of \textit{p(x)}. the problem of finding a root is\r
equivalent to the problem of finding a fixed-point. To see this\r
consider the fixed-point problem of finding the n-dimensional\r
vector x such that\r
\begin{center}\r
$x=g(x).  $\r
\end{center}\r
where $g : C^{n}\longrightarrow C^{n}$. Note that we can easily\r
rewrite this fixed-point problem as a root-finding problem by\r
setting $f (x) = x-g(x)$ and likewise we can recast the\r
root-finding problem into a fixed-point problem by setting\r
\begin{center}\r
$g(x)= f(x)-x$\r
\end{center}\r
Often it will not be possible to solve such nonlinear equation\r
root-finding problems analytically. When this occurs we turn to\r
numerical methods to approximate the solution. Generally speaking,\r
algorithms for solving problems numerically can be divided into\r
two main groups: direct methods and iterative methods.\r
\\\r
 Direct methods exist only for $n \leqslant4$,solved in closed form by G. Cardano\r
in the mid-16th century. However, N.H. Abel in the early 19th\r
century showed that polynomials of degree five or more could not\r
be solved by  directs methods. Since then researchers have\r
concentrated on numerical (iterative) methods such as the famous\r
Newton s method, Bernoulli s method of the 18th, and Graeffe s.\r
With the advent of electronic computers, different methods has\r
been developed such as the Jenkins-Traub method, Larkin s method,\r
Muller s method, and several methods for simultaneous\r
approximation of all the roots, starting with the Durand-Kerner\r
method:\r
\begin{center}\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
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
\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
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
specific sizes of numbers must be used to deal with this\r
difficulty.Moreover,the convergence time of iterative methods\r
drastically increase like the degrees of high polynomials. The\r
parallelization of these algorithms will improve the convergence\r
time.\r
\r
Many authors have treated the problem of parallelization of\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
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
implementation.................To be continued..................\r
\r
\r
\section{Aberth method and difficulties}\r
A cubically convergent iteration method for finding zeros of\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
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
\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
\r
\subsection{Polynomials Initialization}\r
 The initialization of polynomial P(z) with complex coefficients\r
 are given by:\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
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.\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
\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
\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
situation occurs, for instance, in the case where a polynomial\r
having positive coefficients and large degree is computed at a\r
point $\xi$ where $|\xi| > 1$.Indeed the limited number in the\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\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
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
\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
\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
    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
    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
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\r
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\r
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%% \end{thebibliography}\r
\end{document}\r