Its Ehrlich and not Ehrlisch

[kahina_paper1.git] / paper.tex

diff --git a/paper.tex b/paper.tex
index 35896aeff5ed485d271336c31dfbbf0bc59ef595..4ce9747f5e6d9bddf5ddea2fb4f842d08b068ccb 100644 (file)
--- a/paper.tex
+++ b/paper.tex
@@ -79,12 +79,12 @@
 \address[mysecondaryaddress]{FEMTO-ST Institute, University of Franche-Compté }
 
 \begin{abstract}
 \address[mysecondaryaddress]{FEMTO-ST Institute, University of Franche-Compté }
 
 \begin{abstract}

-Polynomials are mathematical algebraic structures that play a great role in science and engineering. But the process of solving them  for high and large degrees is computationally demanding and still not solved. In this paper, we present the results of a parallel implementation of the Ehrlish-Aberth algorithm for the problem root finding for
+Polynomials are mathematical algebraic structures that play a great role in science and engineering. But the process of solving them  for high and large degrees is computationally demanding and still not solved. In this paper, we present the results of a parallel implementation of the Ehrlich-Aberth algorithm for the problem root finding for

 high degree polynomials on GPU architectures (Graphics Processing Unit). The main result of this work is to be able to solve high and very large degree polynomials (up to 100000) very efficiently. We also compare the results with a sequential implementation and the Durand-Kerner method on full and sparse polynomials.
 \end{abstract}
 
 \begin{keyword}
 high degree polynomials on GPU architectures (Graphics Processing Unit). The main result of this work is to be able to solve high and very large degree polynomials (up to 100000) very efficiently. We also compare the results with a sequential implementation and the Durand-Kerner method on full and sparse polynomials.
 \end{abstract}
 
 \begin{keyword}

-root finding of polynomials, high degree, iterative methods, Ehrlish-Aberth, Durant-Kerner, GPU, CUDA, CPU , Parallelization
+root finding of polynomials, high degree, iterative methods, Ehrlich-Aberth, Durant-Kerner, GPU, CUDA, CPU , Parallelization

 \end{keyword}
 
 \end{frontmatter}
 \end{keyword}
 
 \end{frontmatter}


@@ -156,7 +156,7 @@ Aberth~\cite{Aberth73} uses a different iteration formula given as fellows :
 %%\end{center}
 
 Aberth, Ehrlich and Farmer-Loizou~\cite{Loizon83} have proved that
 %%\end{center}
 
 Aberth, Ehrlich and Farmer-Loizou~\cite{Loizon83} have proved that

-the Ehrlisch-Aberth method (EA) has a cubic order of convergence for simple roots whereas the Durand-Kerner has a quadratic order of convergence.
+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.
 
 
 Iterative methods raise several problem when implemented e.g.


@@ -288,9 +288,9 @@ converges sufficiently when :
 \end{equation}
 
 
 \end{equation}
 
 

-\section{Improving the Ehrlisch-Aberth Method}
+\section{Improving the Ehrlich-Aberth Method}

 \label{sec2}
 \label{sec2}

-The Ehrlisch-Aberth method implementation suffers of overflow problems. This
+The Ehrlich-Aberth method implementation suffers of 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
 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


@@ -321,6 +321,7 @@ Applying this solution for the Aberth method we obtain the
 iteration function with 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}
 iteration function with 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}

 H_{i}(z)=z_{i}^{k}-\exp \left(\ln \left(
 p(z_{k})\right)-\ln\left(p(z_{k}^{'})\right)- \ln
 \left(1-Q(z_{k})\right)\right),
 H_{i}(z)=z_{i}^{k}-\exp \left(\ln \left(
 p(z_{k})\right)-\ln\left(p(z_{k}^{'})\right)- \ln
 \left(1-Q(z_{k})\right)\right),


@@ -329,12 +330,17 @@ p(z_{k})\right)-\ln\left(p(z_{k}^{'})\right)- \ln
 where:
 
 \begin{equation}
 where:
 
 \begin{equation}

+\label{Log_H1}

 Q(z_{k})=\exp\left( \ln (p(z_{k}))-\ln(p(z_{k}^{'}))+\ln \left(
 \sum_{k\neq j}^{n}\frac{1}{z_{k}-z_{j}}\right)\right).
 \end{equation}
 
 This solution is applied when the root except the circle unit, represented by the radius $R$ evaluated as:
 Q(z_{k})=\exp\left( \ln (p(z_{k}))-\ln(p(z_{k}^{'}))+\ln \left(
 \sum_{k\neq j}^{n}\frac{1}{z_{k}-z_{j}}\right)\right).
 \end{equation}
 
 This solution is applied when the root except the circle unit, represented by the radius $R$ evaluated as:

-$$R = \exp( \log(DBL\_MAX) / (2*n) )$$ where $DBL\_MAX$ stands for the maximum representable double value.
+\begin{equation}
+\label{R}
+R = \exp( \log(DBL\_MAX) / (2*n) )
+\end{equation}
+ where $DBL\_MAX$ stands for the maximum representable double value.

 
 \section{The implementation of simultaneous methods in a parallel computer}
 \label{secStateofArt}   
 
 \section{The implementation of simultaneous methods in a parallel computer}
 \label{secStateofArt}   


@@ -352,13 +358,13 @@ 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
 
 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 Ehrlisch-Aberth methods are the most practical choices among
+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

 them~\cite{Bini04}. These two methods have been extensively
 studied for parallelization due to their intrinsics, 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
 them~\cite{Bini04}. These two methods have been extensively
 studied for parallelization due to their intrinsics, 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

-Durand-Kerner and cubic for the Ehrlisch-Aberth). Various parallel
+Durand-Kerner and cubic for the Ehrlich-Aberth). Various parallel

 algorithms reported for these methods can be found
 in~\cite{Cosnard90, Freeman89,Freemanall90,Jana99,Janall99}.
 Freeman and Bane~\cite{Freemanall90} presented two parallel
 algorithms reported for these methods can be found
 in~\cite{Cosnard90, Freeman89,Freemanall90,Jana99,Janall99}.
 Freeman and Bane~\cite{Freemanall90} presented two parallel


@@ -368,7 +374,7 @@ each processor to communicate its current approximation to all
 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
 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 Ehrlisch-Aberth method on a model of
+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


@@ -385,9 +391,9 @@ polynomials of 48000. In this paper we present a parallel implementation of Ehli
 GPUs, which details are discussed in the sequel.
 
 
 GPUs, which details are discussed in the sequel.
 
 

-\section {A CUDA parallel Ehrlisch-Aberth method}
-In the following, we describe the parallel implementation of Ehrlisch-Aberth method on GPU 
-for solving high degree polynomials. First, the hardware and software of the GPUs are presented. Then, a CUDA parallel Ehrlisch-Aberth method are presented.
+\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.

 
 \subsection{Background on the GPU architecture}
 A GPU is viewed as an accelerator for the data-parallel and
 
 \subsection{Background on the GPU architecture}
 A GPU is viewed as an accelerator for the data-parallel and


@@ -589,17 +595,15 @@ or from GPU memory to CPU memory \verb=(cudaMemcpyDeviceToHost))=.
 %%HIER END MY REVISIONS (SIDER)
 \section{Experimental study}
 
 %%HIER END MY REVISIONS (SIDER)
 \section{Experimental study}
 

-\subsection{Definition of the polynomial used}
-We study two forms of  polynomials the sparse polynomials and the full polynomials:
-\paragraph{Sparse polynomial}: in this following form, the roots are distributed on 2 distinct circles:
+\subsection{Definition of the used polynomials }
+We study two categories of polynomials : the sparse polynomials and the full polynomials.
+\paragraph{A sparse polynomial}: is a polynomial for which only some coefficients are not null. We use in the following polonymial for which the roots are distributed on 2 distinct circles :

 \begin{equation}
        \forall \alpha_{1} \alpha_{2} \in C,\forall n_{1},n_{2} \in N^{*}; P(z)= (z^{n_{1}}-\alpha_{1})(z^{n_{2}}-\alpha_{2})
 \end{equation}
 \begin{equation}
        \forall \alpha_{1} \alpha_{2} \in C,\forall n_{1},n_{2} \in N^{*}; P(z)= (z^{n_{1}}-\alpha_{1})(z^{n_{2}}-\alpha_{2})
 \end{equation}

-This form makes it possible to associate roots having two
-different modules and thus to work on a polynomial constitute
-of four non zero terms.

 
 

-\paragraph{Full polynomial}: the second form used to obtain a full polynomial is:
+
+\paragraph{A full polynomial} is in contrast, a polynomial for which all the coefficients are not null. the second form used to obtain a full polynomial is:

 %%\begin{equation}
        %%\forall \alpha_{i} \in C,\forall n_{i}\in N^{*}; P(z)= \sum^{n}_{i=1}(z^{n^{i}}.a_{i})
 %%\end{equation}
 %%\begin{equation}
        %%\forall \alpha_{i} \in C,\forall n_{i}\in N^{*}; P(z)= \sum^{n}_{i=1}(z^{n^{i}}.a_{i})
 %%\end{equation}


@@ -607,7 +611,7 @@ of four non zero terms.
 \begin{equation}
      {\Large \forall a_{i} \in C, i\in N;  p(x)=\sum^{n}_{i=0} a_{i}.x^{i}} 
 \end{equation}
 \begin{equation}
      {\Large \forall a_{i} \in C, i\in N;  p(x)=\sum^{n}_{i=0} a_{i}.x^{i}} 
 \end{equation}

-with this form, we can have until \textit{n} non zero terms.
+With this form, we can have until \textit{n} non zero terms whereas the sparse ones have just two non zero terms.

 
 \subsection{The study condition} 
 The our experiences results concern two parameters which are
 
 \subsection{The study condition} 
 The our experiences results concern two parameters which are


@@ -621,59 +625,28 @@ E5620@2.40GHz and a GPU K40 (with 6 Go of ram).
 
 
 \subsection{Comparative study}
 
 
 \subsection{Comparative study}

-In this section, we discuss the performance Ehrlish-Aberth method  of root finding polynomials implemented on CPUs and on GPUs.
+In this section, we discuss the performance Ehrlich-Aberth method  of root finding polynomials implemented on CPUs and on GPUs.

 
 We performed a set of experiments on the sequential and the parallel algorithms, for both sparse and full polynomials and different sizes. We took into account the execution time,the  polynomial size and the number of threads per block performed by sum or each experiment on CPUs and on GPUs.
 
 
 We performed a set of experiments on the sequential and the parallel algorithms, for both sparse and full polynomials and different sizes. We took into account the execution time,the  polynomial size and the number of threads per block performed by sum or each experiment on CPUs and on GPUs.
 

-All experimental results obtained from the simulations are made in double precision data, for a convergence tolerance of the methods set to $10^{-7}$. Since we were more interested in the comparison of the performance behaviors of Ehrlish-Aberth and Durand-Kerner methods on CPUs versus on GPUs.
-
-\subsubsection{Aberth algorithm on CPU and GPU}
-
-%\begin{table}[!ht]
-%      \centering
-%              \begin{tabular} {|R{2cm}|L{2.5cm}|L{2.5cm}|L{1.5cm}|L{1.5cm}|}
-%                      \hline Polynomial's degrees & $T_{exe}$ on CPU & $T_{exe}$ on GPU & CPU iteration & GPU iteration\\
-%                              \hline 5000 & 1.90 & 0.40 & 18 & 17\\
-%                              \hline 10000 & 172.723 & 0.59 & 21 & 24\\
-%                              \hline 20000 & 172.723 & 1.52 & 21 & 25\\
-%                              \hline 30000 & 172.723 & 2.77 & 21 & 33\\
-%                              \hline 50000 & 172.723 & 3.92 & 21 & 18\\
-%                              \hline 500000 & $>$1h & 497.109 &  & 24\\
-%                              \hline 1000000 & $>$1h & 1,524.51& & 24\\
-%                              \hline 
-%              \end{tabular}
-%      \caption{the convergence of Aberth algorithm}
-%      \label{tab:theConvergenceOfAberthAlgorithm}
-%\end{table}
- 
+All experimental results obtained from the simulations are made in double precision data, for a convergence tolerance of the methods set to $10^{-7}$. Since we were more interested in the comparison of the performance behaviors of Ehrlich-Aberth and Durand-Kerner methods on CPUs versus on GPUs. The initialization values of the vector solution of the Ehrlich-Aberth method are given in section 2.2. 
+\subsubsection{The execution time in seconds of Ehrlich-Aberth algorithm on CPU core vs. on a Tesla GPU}
+
+

 \begin{figure}[H]
 \centering
   \includegraphics[width=0.8\textwidth]{figures/Compar_EA_algorithm_CPU_GPU}
 \begin{figure}[H]
 \centering
   \includegraphics[width=0.8\textwidth]{figures/Compar_EA_algorithm_CPU_GPU}

-\caption{Aberth algorithm on CPU and GPU}
+\caption{The execution time in seconds of Ehrlich-Aberth algorithm on CPU core vs. on a Tesla GPU}

 \label{fig:01}
 \end{figure}
 
 \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, 
+We report the execution times of the Ehrlich-Aberth method implemented on one core of the Quad-Core Xeon E5620 CPU and those of the same methods implemented on one Nvidia Tesla K40c GPU, with sparse polynomial degrees ranging from 100,000 to 1,000,000. We can see that the methods implemented on the GPU are faster than those implemented on the CPU. This is due to the GPU ability to compute the data-parallel functions faster than its CPU counterpart. However, the execution time for the sequential implementation exceed 16,000 s for 450,000 degrees polynomials, in counterpart  the GPU implementation for the same polynomials need only 350 s, more than again, with 1,000,000 polynomials degrees GPU implementation not reach 2,300 s degrees. While CPU implementation need more than 10 hours. We can also notice that the GPU implementation are almost 47 faster then those implementation on the CPU. Furthermore, we verify that the number of iterations is the same. This reduction of time allows us to compute roots of polynomial of more important degree at the same time than with a CPU.
+ 

 
 

-\subsubsection{The impact of the thread's number into the convergence of Aberth  algorithm}
-
-%\begin{table}[!h]
-%      \centering
-%              \begin{tabular} {|R{2.5cm}|L{2.5cm}|L{2.5cm}|}
-%                      \hline Thread's numbers & Execution time &Number of iteration\\
-%                              \hline 1024 & 523 & 27\\
-%                              \hline 512 & 449.426 & 24\\
-%                              \hline 256 & 440.805 & 24\\
-%                              \hline 128 & 456.175 & 22\\
-%                              \hline 64 & 472.862 & 23\\
-%                              \hline 32 & 830.152 & 24\\
-%                              \hline 8 & 2632.78 & 23 \\
-%                              \hline
-%                              \end{tabular}
-%                              \caption{The impact of the thread's number into the convergence of Aberth  algorithm}
-%      \label{tab:Theimpactofthethread'snumberintotheconvergenceofAberthalgorithm}
-%              
-%\end{table}

 
 

+\subsubsection{Influence of the number of threads on the execution times of different polynomials (sparse and full)}
+It is also interesting to see the influence of the number of threads per block on the execution time. For that, we notice that the maximum number of threads per block for the Nvidia Tesla K40c GPU is 1024, so we varied the number of threads per block from 8 to 1024. We took into account the execution time for both sparse and full polynomials of size 50000 and 500000 degrees.

 
 \begin{figure}[H]
 \centering
 
 \begin{figure}[H]
 \centering


@@ -682,7 +655,11 @@ All experimental results obtained from the simulations are made in double precis
 \label{fig:01}
 \end{figure}
 
 \label{fig:01}
 \end{figure}
 

+The figure 2 show that, the best execution time for both sparse and full polynomial are given while the threads number varies between 64 and 256 threads per bloc. We notice that with small polynomials the number of threads per block is 64, Whereas, the large polynomials the number of threads per block is 256. However,In the following experiments we specify that the number of thread by block is 256.
+

 \subsubsection{The impact of exp-log solution to compute very high degrees of  polynomial}
 \subsubsection{The impact of exp-log solution to compute very high degrees of  polynomial}

+
+In this experiment we report the performance of log.exp solution describe in ~\ref{sec2} to compute very high degrees polynomials.   

 \begin{figure}[H]
 \centering
   \includegraphics[width=0.8\textwidth]{figures/log_exp}
 \begin{figure}[H]
 \centering
   \includegraphics[width=0.8\textwidth]{figures/log_exp}


@@ -690,16 +667,33 @@ All experimental results obtained from the simulations are made in double precis
 \label{fig:01}
 \end{figure}
 
 \label{fig:01}
 \end{figure}
 

-\subsubsection{A comparative study between Aberth and Durand-kerner algorithm}
+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 polynomials degrees. We can see that the execution time for the both algorithms are the same while the polynomials degrees are less than 4500. After,we show clearly that the classical version of Ehrlich-Aberth algorithm (without applying log.exp) stop to converge and can not solving polynomial exceed 4500, 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 . 
+
+
+
+%we report the performances of the exp.log for the Ehrlich-Aberth algorithm for solving very high degree of polynomial. 
+
+ 
+\subsubsection{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.  

 
 \begin{figure}[H]
 \centering
   \includegraphics[width=0.8\textwidth]{figures/EA_DK}
 
 \begin{figure}[H]
 \centering
   \includegraphics[width=0.8\textwidth]{figures/EA_DK}

-\caption{Ehrlisch-Aberth and Durand-Kerner algorithm on GPU}
+\caption{The execution time of Ehrlich-Aberth versus Durand-Kerner algorithm on GPU}

 \label{fig:01}
 \end{figure}
 
 \label{fig:01}
 \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 $10^{-5}$.
+
+\begin{figure}[H]
+\centering
+  \includegraphics[width=0.8\textwidth]{figures/EA_DK_nbr}
+\caption{The iteration number of Ehrlich-Aberth versus Durand-Kerner algorithm}
+\label{fig:01}
+\end{figure}

 
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 \bibliography{mybibfile}