X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/prng_gpu.git/blobdiff_plain/1ef8a2c0156684f00384e536ee77170afcca38d4..8657185ec89ecf42d39c56a9a57ec20e61e20299:/prng_gpu.tex

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@@ -1,5 +1,1259 @@
 \documentclass{article}
+\usepackage[utf8]{inputenc}
+\usepackage[T1]{fontenc}
+\usepackage{fullpage}
+\usepackage{fancybox}
+\usepackage{amsmath}
+\usepackage{amscd}
+\usepackage{moreverb}
+\usepackage{commath}
+\usepackage{algorithm2e}
+\usepackage{listings}
+\usepackage[standard]{ntheorem}
+
+% Pour mathds : les ensembles IR, IN, etc.
+\usepackage{dsfont}
+
+% Pour avoir des intervalles d'entiers
+\usepackage{stmaryrd}
+
+\usepackage{graphicx}
+% Pour faire des sous-figures dans les figures
+\usepackage{subfigure}
+
+\usepackage{color}
+
+\newtheorem{notation}{Notation}
+
+\newcommand{\X}{\mathcal{X}}
+\newcommand{\Go}{G_{f_0}}
+\newcommand{\B}{\mathds{B}}
+\newcommand{\N}{\mathds{N}}
+\newcommand{\BN}{\mathds{B}^\mathsf{N}}
+\let\sur=\overline
+
+\newcommand{\alert}[1]{\begin{color}{blue}\textit{#1}\end{color}}
+
+\title{Efficient generation of pseudo random numbers based on chaotic iterations
+on GPU}
 \begin{document}
-qdsqsd
-qsdqsd
+
+\author{Jacques M. Bahi, Rapha\"{e}l Couturier, and Christophe
+Guyeux\thanks{Authors in alphabetic order}}
+
+\maketitle
+
+\begin{abstract}
+This is the abstract
+\end{abstract}
+
+\section{Introduction}
+
+Interet des itérations chaotiques pour générer des nombre alea\\
+Interet de générer des nombres alea sur GPU
+\alert{RC, un petit state-of-the-art sur les PRNGs sur GPU ?}
+...
+
+
+\section{Basic Recalls}
+\label{section:BASIC RECALLS}
+This section is devoted to basic definitions and terminologies in the fields of
+topological chaos and chaotic iterations.
+\subsection{Devaney's chaotic dynamical systems}
+
+In the sequel $S^{n}$ denotes the $n^{th}$ term of a sequence $S$ and $V_{i}$
+denotes the $i^{th}$ component of a vector $V$. $f^{k}=f\circ ...\circ f$
+denotes the $k^{th}$ composition of a function $f$. Finally, the following
+notation is used: $\llbracket1;N\rrbracket=\{1,2,\hdots,N\}$.
+
+
+Consider a topological space $(\mathcal{X},\tau)$ and a continuous function $f :
+\mathcal{X} \rightarrow \mathcal{X}$.
+
+\begin{definition}
+$f$ is said to be \emph{topologically transitive} if, for any pair of open sets
+$U,V \subset \mathcal{X}$, there exists $k>0$ such that $f^k(U) \cap V \neq
+\varnothing$.
+\end{definition}
+
+\begin{definition}
+An element $x$ is a \emph{periodic point} for $f$ of period $n\in \mathds{N}^*$
+if $f^{n}(x)=x$.% The set of periodic points of $f$ is denoted $Per(f).$
+\end{definition}
+
+\begin{definition}
+$f$ is said to be \emph{regular} on $(\mathcal{X}, \tau)$ if the set of periodic
+points for $f$ is dense in $\mathcal{X}$: for any point $x$ in $\mathcal{X}$,
+any neighborhood of $x$ contains at least one periodic point (without
+necessarily the same period).
+\end{definition}
+
+
+\begin{definition}
+$f$ is said to be \emph{chaotic} on $(\mathcal{X},\tau)$ if $f$ is regular and
+topologically transitive.
+\end{definition}
+
+The chaos property is strongly linked to the notion of ``sensitivity'', defined
+on a metric space $(\mathcal{X},d)$ by:
+
+\begin{definition}
+\label{sensitivity} $f$ has \emph{sensitive dependence on initial conditions}
+if there exists $\delta >0$ such that, for any $x\in \mathcal{X}$ and any
+neighborhood $V$ of $x$, there exist $y\in V$ and $n > 0$ such that
+$d\left(f^{n}(x), f^{n}(y)\right) >\delta $.
+
+$\delta$ is called the \emph{constant of sensitivity} of $f$.
+\end{definition}
+
+Indeed, Banks \emph{et al.} have proven in~\cite{Banks92} that when $f$ is
+chaotic and $(\mathcal{X}, d)$ is a metric space, then $f$ has the property of
+sensitive dependence on initial conditions (this property was formerly an
+element of the definition of chaos). To sum up, quoting Devaney
+in~\cite{Devaney}, a chaotic dynamical system ``is unpredictable because of the
+sensitive dependence on initial conditions. It cannot be broken down or
+simplified into two subsystems which do not interact because of topological
+transitivity. And in the midst of this random behavior, we nevertheless have an
+element of regularity''. Fundamentally different behaviors are consequently
+possible and occur in an unpredictable way.
+
+
+
+\subsection{Chaotic iterations}
+\label{sec:chaotic iterations}
+
+
+Let us consider  a \emph{system} with a finite  number $\mathsf{N} \in
+\mathds{N}^*$ of elements  (or \emph{cells}), so that each  cell has a
+Boolean  \emph{state}. Having $\mathsf{N}$ Boolean values for these
+ cells  leads to the definition of a particular \emph{state  of the
+system}. A sequence which  elements belong to $\llbracket 1;\mathsf{N}
+\rrbracket $ is called a \emph{strategy}. The set of all strategies is
+denoted by $\mathbb{S}.$
+
+\begin{definition}
+\label{Def:chaotic iterations}
+The      set       $\mathds{B}$      denoting      $\{0,1\}$,      let
+$f:\mathds{B}^{\mathsf{N}}\longrightarrow  \mathds{B}^{\mathsf{N}}$ be
+a  function  and  $S\in  \mathbb{S}$  be  a  strategy.  The  so-called
+\emph{chaotic      iterations}     are     defined      by     $x^0\in
+\mathds{B}^{\mathsf{N}}$ and
+\begin{equation}
+\forall    n\in     \mathds{N}^{\ast     },    \forall     i\in
+\llbracket1;\mathsf{N}\rrbracket ,x_i^n=\left\{
+\begin{array}{ll}
+  x_i^{n-1} &  \text{ if  }S^n\neq i \\
+  \left(f(x^{n-1})\right)_{S^n} & \text{ if }S^n=i.
+\end{array}\right.
+\end{equation}
+\end{definition}
+
+In other words, at the $n^{th}$ iteration, only the $S^{n}-$th cell is
+\textquotedblleft  iterated\textquotedblright .  Note  that in  a more
+general  formulation,  $S^n$  can   be  a  subset  of  components  and
+$\left(f(x^{n-1})\right)_{S^{n}}$      can     be      replaced     by
+$\left(f(x^{k})\right)_{S^{n}}$, where  $k<n$, describing for example,
+delays  transmission~\cite{Robert1986,guyeux10}.  Finally,  let us  remark that
+the term  ``chaotic'', in  the name of  these iterations,  has \emph{a
+priori} no link with the mathematical theory of chaos, recalled above.
+
+
+Let us now recall how to define a suitable metric space where chaotic iterations
+are continuous. For further explanations, see, e.g., \cite{guyeux10}.
+
+Let $\delta $ be the \emph{discrete Boolean metric}, $\delta
+(x,y)=0\Leftrightarrow x=y.$ Given a function $f$, define the function:
+\begin{equation}
+\begin{array}{lrll}
+F_{f}: & \llbracket1;\mathsf{N}\rrbracket\times \mathds{B}^{\mathsf{N}} &
+\longrightarrow & \mathds{B}^{\mathsf{N}} \\
+& (k,E) & \longmapsto & \left( E_{j}.\delta (k,j)+f(E)_{k}.\overline{\delta
+(k,j)}\right) _{j\in \llbracket1;\mathsf{N}\rrbracket},%
+\end{array}%
+\end{equation}%
+\noindent where + and . are the Boolean addition and product operations.
+Consider the phase space:
+\begin{equation}
+\mathcal{X} = \llbracket 1 ; \mathsf{N} \rrbracket^\mathds{N} \times
+\mathds{B}^\mathsf{N},
+\end{equation}
+\noindent and the map defined on $\mathcal{X}$:
+\begin{equation}
+G_f\left(S,E\right) = \left(\sigma(S), F_f(i(S),E)\right), \label{Gf}
+\end{equation}
+\noindent where $\sigma$ is the \emph{shift} function defined by $\sigma
+(S^{n})_{n\in \mathds{N}}\in \mathbb{S}\longrightarrow (S^{n+1})_{n\in
+\mathds{N}}\in \mathbb{S}$ and $i$ is the \emph{initial function} 
+$i:(S^{n})_{n\in \mathds{N}} \in \mathbb{S}\longrightarrow S^{0}\in \llbracket
+1;\mathsf{N}\rrbracket$. Then the chaotic iterations defined in
+(\ref{sec:chaotic iterations}) can be described by the following iterations:
+\begin{equation}
+\left\{
+\begin{array}{l}
+X^0 \in \mathcal{X} \\
+X^{k+1}=G_{f}(X^k).%
+\end{array}%
+\right.
+\end{equation}%
+
+With this formulation, a shift function appears as a component of chaotic
+iterations. The shift function is a famous example of a chaotic
+map~\cite{Devaney} but its presence is not sufficient enough to claim $G_f$ as
+chaotic. 
+
+To study this claim, a new distance between two points $X = (S,E), Y =
+(\check{S},\check{E})\in
+\mathcal{X}$ has been introduced in \cite{guyeux10} as follows:
+\begin{equation}
+d(X,Y)=d_{e}(E,\check{E})+d_{s}(S,\check{S}),
+\end{equation}
+\noindent where
+\begin{equation}
+\left\{
+\begin{array}{lll}
+\displaystyle{d_{e}(E,\check{E})} & = & \displaystyle{\sum_{k=1}^{\mathsf{N}%
+}\delta (E_{k},\check{E}_{k})}, \\
+\displaystyle{d_{s}(S,\check{S})} & = & \displaystyle{\dfrac{9}{\mathsf{N}}%
+\sum_{k=1}^{\infty }\dfrac{|S^k-\check{S}^k|}{10^{k}}}.%
+\end{array}%
+\right.
+\end{equation}
+
+
+This new distance has been introduced to satisfy the following requirements.
+\begin{itemize}
+\item When the number of different cells between two systems is increasing, then
+their distance should increase too.
+\item In addition, if two systems present the same cells and their respective
+strategies start with the same terms, then the distance between these two points
+must be small because the evolution of the two systems will be the same for a
+while. Indeed, the two dynamical systems start with the same initial condition,
+use the same update function, and as strategies are the same for a while, then
+components that are updated are the same too.
+\end{itemize}
+The distance presented above follows these recommendations. Indeed, if the floor
+value $\lfloor d(X,Y)\rfloor $ is equal to $n$, then the systems $E, \check{E}$
+differ in $n$ cells. In addition, $d(X,Y) - \lfloor d(X,Y) \rfloor $ is a
+measure of the differences between strategies $S$ and $\check{S}$. More
+precisely, this floating part is less than $10^{-k}$ if and only if the first
+$k$ terms of the two strategies are equal. Moreover, if the $k^{th}$ digit is
+nonzero, then the $k^{th}$ terms of the two strategies are different.
+
+Finally, it has been established in \cite{guyeux10} that,
+
+\begin{proposition}
+Let $f$ be a map from $\mathds{B}^n$ to itself. Then $G_{f}$ is continuous in
+the metric space $(\mathcal{X},d)$.
+\end{proposition}
+
+The chaotic property of $G_f$ has been firstly established for the vectorial
+Boolean negation \cite{guyeux10}. To obtain a characterization, we have secondly
+introduced the notion of asynchronous iteration graph recalled bellow.
+
+Let $f$ be a map from $\mathds{B}^n$ to itself. The
+{\emph{asynchronous iteration graph}} associated with $f$ is the
+directed graph $\Gamma(f)$ defined by: the set of vertices is
+$\mathds{B}^n$; for all $x\in\mathds{B}^n$ and $i\in \llbracket1;n\rrbracket$,
+the graph $\Gamma(f)$ contains an arc from $x$ to $F_f(i,x)$. 
+The relation between $\Gamma(f)$ and $G_f$ is clear: there exists a
+path from $x$ to $x'$ in $\Gamma(f)$ if and only if there exists a
+strategy $s$ such that the parallel iteration of $G_f$ from the
+initial point $(s,x)$ reaches the point $x'$.
+
+We have finally proven in \cite{bcgr11:ip} that,
+
+
+\begin{theorem}
+\label{Th:Caractérisation   des   IC   chaotiques}  
+Let $f:\mathds{B}^n\to\mathds{B}^n$. $G_f$ is chaotic  (according to  Devaney) 
+if and only if $\Gamma(f)$ is strongly connected.
+\end{theorem}
+
+This result of chaos has lead us to study the possibility to build a
+pseudo-random number generator (PRNG) based on the chaotic iterations. 
+As $G_f$, defined on the domain   $\llbracket 1 ;  n \rrbracket^{\mathds{N}} 
+\times \mathds{B}^n$, is build from Boolean networks $f : \mathds{B}^n
+\rightarrow \mathds{B}^n$, we can preserve the theoretical properties on $G_f$
+during implementations (due to the discrete nature of $f$). It is as if
+$\mathds{B}^n$ represents the memory of the computer whereas $\llbracket 1 ;  n
+\rrbracket^{\mathds{N}}$ is its input stream (the seeds, for instance).
+
+\section{Application to Pseudo-Randomness}
+
+\subsection{A First Pseudo-Random Number Generator}
+
+We have proposed in~\cite{bgw09:ip} a new family of generators that receives 
+two PRNGs as inputs. These two generators are mixed with chaotic iterations, 
+leading thus to a new PRNG that improves the statistical properties of each
+generator taken alone. Furthermore, our generator 
+possesses various chaos properties that none of the generators used as input
+present.
+
+\begin{algorithm}[h!]
+%\begin{scriptsize}
+\KwIn{a function $f$, an iteration number $b$, an initial configuration $x^0$
+($n$ bits)}
+\KwOut{a configuration $x$ ($n$ bits)}
+$x\leftarrow x^0$\;
+$k\leftarrow b + \textit{XORshift}(b)$\;
+\For{$i=0,\dots,k$}
+{
+$s\leftarrow{\textit{XORshift}(n)}$\;
+$x\leftarrow{F_f(s,x)}$\;
+}
+return $x$\;
+%\end{scriptsize}
+\caption{PRNG with chaotic functions}
+\label{CI Algorithm}
+\end{algorithm}
+
+\begin{algorithm}[h!]
+\KwIn{the internal configuration $z$ (a 32-bit word)}
+\KwOut{$y$ (a 32-bit word)}
+$z\leftarrow{z\oplus{(z\ll13)}}$\;
+$z\leftarrow{z\oplus{(z\gg17)}}$\;
+$z\leftarrow{z\oplus{(z\ll5)}}$\;
+$y\leftarrow{z}$\;
+return $y$\;
+\medskip
+\caption{An arbitrary round of \textit{XORshift} algorithm}
+\label{XORshift}
+\end{algorithm}
+
+
+
+
+
+This generator is synthesized in Algorithm~\ref{CI Algorithm}.
+It takes as input: a function $f$;
+an integer $b$, ensuring that the number of executed iterations is at least $b$
+and at most $2b+1$; and an initial configuration $x^0$.
+It returns the new generated configuration $x$.  Internally, it embeds two
+\textit{XORshift}$(k)$ PRNGs \cite{Marsaglia2003} that returns integers
+uniformly distributed
+into $\llbracket 1 ; k \rrbracket$.
+\textit{XORshift} is a category of very fast PRNGs designed by George Marsaglia,
+which repeatedly uses the transform of exclusive or (XOR, $\oplus$) on a number
+with a bit shifted version of it. This PRNG, which has a period of
+$2^{32}-1=4.29\times10^9$, is summed up in Algorithm~\ref{XORshift}. It is used
+in our PRNG to compute the strategy length and the strategy elements.
+
+
+We have proven in \cite{bcgr11:ip} that,
+\begin{theorem}
+  Let $f: \mathds{B}^{n} \rightarrow \mathds{B}^{n}$, $\Gamma(f)$ its
+  iteration graph, $\check{M}$ its adjacency
+  matrix and $M$ a $n\times n$ matrix defined as in the previous lemma.
+  If $\Gamma(f)$ is strongly connected, then 
+  the output of the PRNG detailed in Algorithm~\ref{CI Algorithm} follows 
+  a law that tends to the uniform distribution 
+  if and only if $M$ is a double stochastic matrix.
+\end{theorem} 
+
+
+
+\subsection{Improving the speed of the former generator}
+
+Instead of updating only one cell at each iteration, we can try to choose a
+subset of components and to update them together. Such an attempt leads
+to a kind of merger of the two sequences used in Algorithm 
+\ref{CI Algorithm}. When the updating function is the vectorial negation,
+this algorithm can be rewritten as follows:
+
+\begin{equation}
+\left\{
+\begin{array}{l}
+x^0 \in \llbracket 0, 2^\mathsf{N}-1 \rrbracket, S \in \llbracket 0, 2^\mathsf{N}-1 \rrbracket^\mathds{N} \\
+\forall n \in \mathds{N}^*, x^n = x^{n-1} \oplus S^n,
+\end{array}
+\right.
+\label{equation Oplus}
+\end{equation}
+where $\oplus$ is for the bitwise exclusive or between two integers. 
+This rewritten can be understood as follows. The $n-$th term $S^n$ of the
+sequence $S$, which is an integer of $\mathsf{N}$ binary digits, presents
+the list of cells to update in the state $x^n$ of the system (represented
+as an integer having $\mathsf{N}$ bits too). More precisely, the $k-$th 
+component of this state (a binary digit) changes if and only if the $k-$th 
+digit in the binary decomposition of $S^n$ is 1.
+
+The single basic component presented in Eq.~\ref{equation Oplus} is of 
+ordinary use as a good elementary brick in various PRNGs. It corresponds
+to the following discrete dynamical system in chaotic iterations:
+
+\begin{equation}
+\forall    n\in     \mathds{N}^{\ast     },    \forall     i\in
+\llbracket1;\mathsf{N}\rrbracket ,x_i^n=\left\{
+\begin{array}{ll}
+  x_i^{n-1} &  \text{ if  } i \notin \mathcal{S}^n \\
+  \left(f(x^{n-1})\right)_{S^n} & \text{ if }i \in \mathcal{S}^n.
+\end{array}\right.
+\end{equation}
+where $f$ is the vectorial negation and $\forall n \in \mathds{N}$, 
+$\mathcal{S}^n \subset \llbracket 1, \mathsf{N} \rrbracket$ is such that
+$k \in \mathcal{S}^n$ if and only if the $k-$th digit in the binary
+decomposition of $S^n$ is 1. Such chaotic iterations are more general
+than the ones presented in Definition \ref{Def:chaotic iterations} for 
+the fact that, instead of updating only one term at each iteration,
+we select a subset of components to change.
+
+
+Obviously, replacing Algorithm~\ref{CI Algorithm} by 
+Equation~\ref{equation Oplus}, possible when the iteration function is
+the vectorial negation, leads to a speed improvement. However, proofs
+of chaos obtained in~\cite{bg10:ij} have been established
+only for chaotic iterations of the form presented in Definition 
+\ref{Def:chaotic iterations}. The question is now to determine whether the
+use of more general chaotic iterations to generate pseudo-random numbers more 
+fastly, does not deflate their topological chaos properties.
+
+\subsection{Proofs of chaos of the general formulation of the chaotic iterations}
+
+Let us consider the discrete dynamical systems in chaotic iterations having 
+the general form:
+
+\begin{equation}
+\forall    n\in     \mathds{N}^{\ast     },    \forall     i\in
+\llbracket1;\mathsf{N}\rrbracket ,x_i^n=\left\{
+\begin{array}{ll}
+  x_i^{n-1} &  \text{ if  } i \notin \mathcal{S}^n \\
+  \left(f(x^{n-1})\right)_{S^n} & \text{ if }i \in \mathcal{S}^n.
+\end{array}\right.
+\end{equation}
+
+In other words, at the $n^{th}$ iteration, only the cells whose id is
+contained into the set $S^{n}$ are iterated.
+
+Let us now rewrite these general chaotic iterations as usual discrete dynamical
+system of the form $X^{n+1}=f(X^n)$ on an ad hoc metric space. Such a formulation
+is required in order to study the topological behavior of the system.
+
+Let us introduce the following function:
+\begin{equation}
+\begin{array}{cccc}
+ \delta: & \llbracket 1; \mathsf{N} \rrbracket \times \mathcal{P}\left(\llbracket 1; \mathsf{N} \rrbracket\right) & \longrightarrow & \mathds{B}\\
+         & (i,X) & \longmapsto  & \left\{ \begin{array}{ll} 0 & \textrm{if }i \notin X, \\ 1 & \textrm{if }i \in X,  \end{array}\right.
+\end{array} 
+\end{equation}
+where $\mathcal{P}\left(X\right)$ is for the powerset of the set $X$, that is, $Y \in \mathcal{P}\left(X\right) \Longleftrightarrow Y \subset X$.
+
+Given a function $f:\mathds{B}^\mathsf{N} \longrightarrow \mathds{B}^\mathsf{N} $, define the function:
+\begin{equation}
+\begin{array}{lrll}
+F_{f}: & \llbracket1;\mathsf{N}\rrbracket\times \mathds{B}^{\mathsf{N}} &
+\longrightarrow & \mathds{B}^{\mathsf{N}} \\
+& (k,E) & \longmapsto & \left( E_{j}.\delta (k,j)+f(E)_{k}.\overline{\delta
+(k,j)}\right) _{j\in \llbracket1;\mathsf{N}\rrbracket},%
+\end{array}%
+\end{equation}%
+\noindent where + and . are the Boolean addition and product operations.
+Consider the phase space:
+\begin{equation}
+\mathcal{X} = \llbracket 1 ; \mathsf{N} \rrbracket^\mathds{N} \times
+\mathds{B}^\mathsf{N},
+\end{equation}
+\noindent and the map defined on $\mathcal{X}$:
+\begin{equation}
+G_f\left(S,E\right) = \left(\sigma(S), F_f(i(S),E)\right), \label{Gf}
+\end{equation}
+\noindent where $\sigma$ is the \emph{shift} function defined by $\sigma
+(S^{n})_{n\in \mathds{N}}\in \mathbb{S}\longrightarrow (S^{n+1})_{n\in
+\mathds{N}}\in \mathbb{S}$ and $i$ is the \emph{initial function} 
+$i:(S^{n})_{n\in \mathds{N}} \in \mathbb{S}\longrightarrow S^{0}\in \llbracket
+1;\mathsf{N}\rrbracket$. Then the chaotic iterations defined in
+(\ref{sec:chaotic iterations}) can be described by the following iterations:
+\begin{equation}
+\left\{
+\begin{array}{l}
+X^0 \in \mathcal{X} \\
+X^{k+1}=G_{f}(X^k).%
+\end{array}%
+\right.
+\end{equation}%
+
+With this formulation, a shift function appears as a component of chaotic
+iterations. The shift function is a famous example of a chaotic
+map~\cite{Devaney} but its presence is not sufficient enough to claim $G_f$ as
+chaotic. 
+
+To study this claim, a new distance between two points $X = (S,E), Y =
+(\check{S},\check{E})\in
+\mathcal{X}$ has been introduced in \cite{guyeux10} as follows:
+\begin{equation}
+d(X,Y)=d_{e}(E,\check{E})+d_{s}(S,\check{S}),
+\end{equation}
+\noindent where
+\begin{equation}
+\left\{
+\begin{array}{lll}
+\displaystyle{d_{e}(E,\check{E})} & = & \displaystyle{\sum_{k=1}^{\mathsf{N}%
+}\delta (E_{k},\check{E}_{k})}, \\
+\displaystyle{d_{s}(S,\check{S})} & = & \displaystyle{\dfrac{9}{\mathsf{N}}%
+\sum_{k=1}^{\infty }\dfrac{|S^k-\check{S}^k|}{10^{k}}}.%
+\end{array}%
+\right.
+\end{equation}
+
+
+\section{Efficient PRNG based on Chaotic Iterations}
+
+In  order to  implement efficiently  a PRNG  based on  chaotic iterations  it is
+possible to improve  previous works [ref]. One solution  consists in considering
+that the  strategy used contains all the  bits for which the  negation is
+achieved out. Then in order to apply  the negation on these bits we can simply
+apply the  xor operator between  the current number  and the strategy. In
+order to obtain the strategy we also use a classical PRNG.
+
+Here  is an  example with  16-bits numbers  showing how  the bitwise  operations
+are
+applied.  Suppose  that $x$ and the  strategy $S^i$ are defined  in binary mode.
+Then the following table shows the result of $x$ xor $S^i$.
+$$
+\begin{array}{|cc|cccccccccccccccc|}
+\hline
+x      &=&1&0&1&1&1&0&1&0&1&0&0&1&0&0&1&0\\
+\hline
+S^i      &=&0&1&1&0&0&1&1&0&1&1&1&0&0&1&1&1\\
+\hline
+x \oplus S^i&=&1&1&0&1&1&1&0&0&0&1&1&1&0&1&0&1\\
+\hline
+
+\hline
+ \end{array}
+$$
+
+%% \begin{figure}[htbp]
+%% \begin{center}
+%% \fbox{
+%% \begin{minipage}{14cm}
+%% unsigned int CIprng() \{\\
+%%   static unsigned int x = 123123123;\\
+%%   unsigned long t1 = xorshift();\\
+%%   unsigned long t2 = xor128();\\
+%%   unsigned long t3 = xorwow();\\
+%%   x = x\textasciicircum (unsigned int)t1;\\
+%%   x = x\textasciicircum (unsigned int)(t2$>>$32);\\
+%%   x = x\textasciicircum (unsigned int)(t3$>>$32);\\
+%%   x = x\textasciicircum (unsigned int)t2;\\
+%%   x = x\textasciicircum (unsigned int)(t1$>>$32);\\
+%%   x = x\textasciicircum (unsigned int)t3;\\
+%%   return x;\\
+%% \}
+%% \end{minipage}
+%% }
+%% \end{center}
+%% \caption{sequential Chaotic Iteration PRNG}
+%% \label{algo:seqCIprng}
+%% \end{figure}
+
+
+
+\lstset{language=C,caption={C code of the sequential chaotic iterations based
+PRNG},label=algo:seqCIprng}
+\begin{lstlisting}
+unsigned int CIprng() {
+  static unsigned int x = 123123123;
+  unsigned long t1 = xorshift();
+  unsigned long t2 = xor128();
+  unsigned long t3 = xorwow();
+  x = x^(unsigned int)t1;
+  x = x^(unsigned int)(t2>>32);
+  x = x^(unsigned int)(t3>>32);
+  x = x^(unsigned int)t2;
+  x = x^(unsigned int)(t1>>32);
+  x = x^(unsigned int)t3;
+  return x;
+}
+\end{lstlisting}
+
+
+
+
+
+In listing~\ref{algo:seqCIprng}  a sequential version of  our chaotic iterations
+based   PRNG    is   presented.   The    xor   operator   is    represented   by
+\textasciicircum.  This   function  uses  three  classical   64-bits  PRNG:  the
+\texttt{xorshift},  the   \texttt{xor128}  and  the   \texttt{xorwow}.   In  the
+following,  we call  them  xor-like  PRNGSs.  These  three  PRNGs are  presented
+in~\cite{Marsaglia2003}.  As each  xor-like PRNG used works with  64-bits and as
+our PRNG works  with 32-bits, the use of \texttt{(unsigned  int)} selects the 32
+least significant bits whereas  \texttt{(unsigned int)(t3$>>$32)} selects the 32
+most  significants bits  of the  variable \texttt{t}.   So to  produce  a random
+number realizes  6 xor operations with  6 32-bits numbers produced  by 3 64-bits
+PRNG.  This version successes the  BigCrush of the TestU01 battery [P.  L’ecuyer
+  and R. Simard. Testu01].
+
+\section{Efficient prng based on chaotic iterations on GPU}
+
+In  order to benefit  from computing  power of  GPU, a  program needs  to define
+independent blocks of threads which  can be computed simultaneously. In general,
+the larger the number of threads is,  the more local memory is used and the less
+branching  instructions are  used (if,  while, ...),  the better  performance is
+obtained  on  GPU.  So  with  algorithm  \ref{algo:seqCIprng}  presented in  the
+previous section, it is possible to  build a similar program which computes PRNG
+on  GPU. In  the CUDA  [ref] environment,  threads have  a  local identificator,
+called \texttt{ThreadIdx} relative to the block containing them.
+
+
+\subsection{Naive version for GPU}
+
+From the CPU version, it is possible  to obtain a quite similar version for GPU.
+The principe consists in assigning the computation of a PRNG as in sequential to
+each thread  of the  GPU.  Of course,  it is  essential that the  three xor-like
+PRNGs  used for  our computation  have different  parameters. So  we  chose them
+randomly with  another PRNG. As the  initialisation is performed by  the CPU, we
+have chosen to use the ISAAC PRNG  [ref] to initalize all the parameters for the
+GPU version  of our  PRNG.  The  implementation of the  three xor-like  PRNGs is
+straightforward  as soon  as their  parameters have  been allocated  in  the GPU
+memory. Each xor-like  PRNGs used works with an internal  number $x$ which keeps
+the last generated random numbers. Other internal variables are also used by the
+xor-like PRNGs. More  precisely, the implementation of the  xor128, the xorshift
+and  the xorwow  respectively  require 4,  5  and 6  unsigned  long as  internal
+variables.
+
+\begin{algorithm}
+
+\KwIn{InternalVarXorLikeArray: array with internal variables of the 3 xor-like
+PRNGs in global memory\;
+NumThreads: Number of threads\;}
+\KwOut{NewNb: array containing random numbers in global memory}
+\If{threadIdx is concerned by the computation} {
+  retrieve data from InternalVarXorLikeArray[threadIdx] in local variables\;
+  \For{i=1 to n} {
+    compute a new PRNG as in Listing\ref{algo:seqCIprng}\;
+    store the new PRNG in NewNb[NumThreads*threadIdx+i]\;
+  }
+  store internal variables in InternalVarXorLikeArray[threadIdx]\;
+}
+
+\caption{main kernel for the chaotic iterations based PRNG GPU naive version}
+\label{algo:gpu_kernel}
+\end{algorithm}
+
+Algorithm~\ref{algo:gpu_kernel}  presents a naive  implementation of  PRNG using
+GPU.  According  to the available  memory in the  GPU and the number  of threads
+used simultenaously,  the number  of random numbers  that a thread  can generate
+inside   a    kernel   is   limited,   i.e.    the    variable   \texttt{n}   in
+algorithm~\ref{algo:gpu_kernel}. For example, if  $100,000$ threads are used and
+if $n=100$\footnote{in fact, we need to add the initial seed (a 32-bits number)}
+then   the  memory   required   to  store   internals   variables  of   xor-like
+PRNGs\footnote{we multiply this number by $2$ in order to count 32-bits numbers}
+and  random  number of  our  PRNG  is  equals to  $100,000\times  ((4+5+6)\times
+2+(1+100))=1,310,000$ 32-bits numbers, i.e. about $52$Mb.
+
+All the  tests performed  to pass the  BigCrush of TestU01  succeeded. Different
+number of threads, called \texttt{NumThreads} in our algorithm, have been tested
+upto $10$ millions.
+
+\begin{remark}
+Algorithm~\ref{algo:gpu_kernel}  has  the  advantage to  manipulate  independent
+PRNGs, so this version is easily usable on a cluster of computer. The only thing
+to ensure is to use a single ISAAC PRNG. For this, a simple solution consists in
+using a master node for the initialization which computes the initial parameters
+for all the differents nodes involves in the computation.
+\end{remark}
+
+\subsection{Improved version for GPU}
+
+As GPU cards using CUDA have shared memory between threads of the same block, it
+is possible  to use this  feature in order  to simplify the  previous algorithm,
+i.e. using less  than 3 xor-like PRNGs. The solution  consists in computing only
+one xor-like PRNG by thread, saving  it into shared memory and using the results
+of some  other threads in the  same block of  threads. In order to  define which
+thread uses the result of which other  one, we can use a permutation array which
+contains  the indexes  of  all threads  and  for which  a  permutation has  been
+performed.  In Algorithm~\ref{algo:gpu_kernel2}, 2 permutations arrays are used.
+The    variable   \texttt{offset}    is    computed   using    the   value    of
+\texttt{permutation\_size}.   Then we  can compute  \texttt{o1}  and \texttt{o2}
+which represent the indexes of the  other threads for which the results are used
+by the  current thread. In  the algorithm, we  consider that a  64-bits xor-like
+PRNG is used, that is why both 32-bits parts are used.
+
+This version also succeed to the BigCrush batteries of tests.
+
+\begin{algorithm}
+
+\KwIn{InternalVarXorLikeArray: array with internal variables of 1 xor-like PRNGs
+in global memory\;
+NumThreads: Number of threads\;
+tab1, tab2: Arrays containing permutations of size permutation\_size\;}
+
+\KwOut{NewNb: array containing random numbers in global memory}
+\If{threadId is concerned} {
+  retrieve data from InternalVarXorLikeArray[threadId] in local variables\;
+  offset = threadIdx\%permutation\_size\;
+  o1 = threadIdx-offset+tab1[offset]\;
+  o2 = threadIdx-offset+tab2[offset]\;
+  \For{i=1 to n} {
+    t=xor-like()\;
+    shared\_mem[threadId]=(unsigned int)t\;
+    x = x $\oplus$ (unsigned int) t\;
+    x = x $\oplus$ (unsigned int) (t>>32)\;
+    x = x $\oplus$ shared[o1]\;
+    x = x $\oplus$ shared[o2]\;
+
+    store the new PRNG in NewNb[NumThreads*threadId+i]\;
+  }
+  store internal variables in InternalVarXorLikeArray[threadId]\;
+}
+
+\caption{main kernel for the chaotic iterations based PRNG GPU efficient
+version}
+\label{algo:gpu_kernel2}
+\end{algorithm}
+
+
+
+\section{Experiments}
+
+Differents experiments have been performed in order to measure the generation
+speed.
+\begin{figure}[t]
+\begin{center}
+  \includegraphics[scale=.7]{curve_time_gpu.pdf}
+\end{center}
+\caption{Number of random numbers generated per second}
+\label{fig:time_naive_gpu}
+\end{figure}
+
+
+First of all we have compared the time to generate X random numbers with both
+the CPU version and the GPU version. 
+
+Faire une courbe du nombre de random en fonction du nombre de threads,
+éventuellement en fonction du nombres de threads par bloc.
+
+
+
+\section{The relativity of disorder}
+\label{sec:de la relativité du désordre}
+
+\subsection{Impact of the topology's finenesse}
+
+Let us firstly introduce the following notations.
+
+\begin{notation}
+$\mathcal{X}_\tau$ will denote the topological space
+$\left(\mathcal{X},\tau\right)$, whereas $\mathcal{V}_\tau (x)$ will be the set
+of all the neighborhoods of $x$ when considering the topology $\tau$ (or simply
+$\mathcal{V} (x)$, if there is no ambiguity).
+\end{notation}
+
+
+
+\begin{theorem}
+\label{Th:chaos et finesse}
+Let $\mathcal{X}$ a set and $\tau, \tau'$ two topologies on $\mathcal{X}$ s.t.
+$\tau'$ is finer than $\tau$. Let $f:\mathcal{X} \to \mathcal{X}$, continuous
+both for $\tau$ and $\tau'$.
+
+If $(\mathcal{X}_{\tau'},f)$ is chaotic according to Devaney, then
+$(\mathcal{X}_\tau,f)$ is chaotic too.
+\end{theorem}
+
+\begin{proof}
+Let us firstly establish the transitivity of $(\mathcal{X}_\tau,f)$.
+
+Let $\omega_1, \omega_2$ two open sets of $\tau$. Then $\omega_1, \omega_2 \in
+\tau'$, becaus $\tau'$ is finer than $\tau$. As $f$ is $\tau'-$transitive, we
+can deduce that $\exists n \in \mathds{N}, \omega_1 \cap f^{(n)}(\omega_2) =
+\varnothing$. Consequently, $f$ is $\tau-$transitive.
+
+Let us now consider the regularity of $(\mathcal{X}_\tau,f)$, \emph{i.e.}, for
+all $x \in \mathcal{X}$, and for all $\tau-$neighborhood $V$ of $x$, there is a
+periodic point for $f$ into $V$.
+
+Let $x \in \mathcal{X}$ and $V \in \mathcal{V}_\tau (x)$ a $\tau-$neighborhood
+of $x$. By definition, $\exists \omega \in \tau, x \in \omega \subset V$.
+
+But $\tau \subset \tau'$, so $\omega \in \tau'$, and then $V \in
+\mathcal{V}_{\tau'} (x)$. As $(\mathcal{X}_{\tau'},f)$ is regular, there is a
+periodic point for $f$ into $V$, and the regularity of $(\mathcal{X}_\tau,f)$ is
+proven. 
+\end{proof}
+
+\subsection{A given system can always be claimed as chaotic}
+
+Let $f$ an iteration function on $\mathcal{X}$ having at least a fixed point.
+Then this function is chaotic (in a certain way):
+
+\begin{theorem}
+Let $\mathcal{X}$ a nonempty set and $f: \mathcal{X} \to \X$ a function having
+at least a fixed point.
+Then $f$ is $\tau_0-$chaotic, where $\tau_0$ is the trivial (indiscrete)
+topology on $\X$.
+\end{theorem}
+
+
+\begin{proof}
+$f$ is transitive when $\forall \omega, \omega' \in \tau_0 \setminus
+\{\varnothing\}, \exists n \in \mathds{N}, f^{(n)}(\omega) \cap \omega' \neq
+\varnothing$.
+As $\tau_0 = \left\{ \varnothing, \X \right\}$, this is equivalent to look for
+an integer $n$ s.t. $f^{(n)}\left( \X \right) \cap \X \neq \varnothing$. For
+instance, $n=0$ is appropriate.
+
+Let us now consider $x \in \X$ and $V \in \mathcal{V}_{\tau_0} (x)$. Then $V =
+\mathcal{X}$, so $V$ has at least a fixed point for $f$. Consequently $f$ is
+regular, and the result is established.
+\end{proof}
+
+
+
+
+\subsection{A given system can always be claimed as non-chaotic}
+
+\begin{theorem}
+Let $\mathcal{X}$ be a set and $f: \mathcal{X} \to \X$.
+If $\X$ is infinite, then $\left( \X_{\tau_\infty}, f\right)$ is not chaotic
+(for the Devaney's formulation), where $\tau_\infty$ is the discrete topology.
+\end{theorem}
+
+\begin{proof}
+Let us prove it by contradiction, assuming that $\left(\X_{\tau_\infty},
+f\right)$ is both transitive and regular.
+
+Let $x \in \X$ and $\{x\}$ one of its neighborhood. This neighborhood must
+contain a periodic point for $f$, if we want that $\left(\X_{\tau_\infty},
+f\right)$ is regular. Then $x$ must be a periodic point of $f$.
+
+Let $I_x = \left\{ f^{(n)}(x), n \in \mathds{N}\right\}$. This set is finite
+because  $x$ is periodic, and $\mathcal{X}$ is infinite, then $\exists y \in
+\mathcal{X}, y \notin I_x$.
+
+As $\left(\X_{\tau_\infty}, f\right)$ must be transitive, for all open nonempty
+sets $A$ and $B$, an integer $n$ must satisfy $f^{(n)}(A) \cap B \neq
+\varnothing$. However $\{x\}$ and $\{y\}$ are open sets and $y \notin I_x
+\Rightarrow \forall n, f^{(n)}\left( \{x\} \right) \cap \{y\} = \varnothing$.
+\end{proof}
+
+
+
+
+
+
+\section{Chaos on the order topology}
+
+\subsection{The phase space is an interval of the real line}
+
+\subsubsection{Toward a topological semiconjugacy}
+
+In what follows, our intention is to establish, by using a topological
+semiconjugacy, that chaotic iterations over $\mathcal{X}$ can be described as
+iterations on a real interval. To do so, we must firstly introduce some
+notations and terminologies. 
+
+Let $\mathcal{S}_\mathsf{N}$ be the set of sequences belonging into $\llbracket
+1; \mathsf{N}\rrbracket$ and $\mathcal{X}_{\mathsf{N}} = \mathcal{S}_\mathsf{N}
+\times \B^\mathsf{N}$.
+
+
+\begin{definition}
+The function $\varphi: \mathcal{S}_{10} \times\mathds{B}^{10} \rightarrow \big[
+0, 2^{10} \big[$ is defined by:
+\begin{equation}
+ \begin{array}{cccl}
+\varphi: & \mathcal{X}_{10} = \mathcal{S}_{10} \times\mathds{B}^{10}&
+\longrightarrow & \big[ 0, 2^{10} \big[ \\
+ & (S,E) = \left((S^0, S^1, \hdots ); (E_0, \hdots, E_9)\right) & \longmapsto &
+\varphi \left((S,E)\right)
+\end{array}
+\end{equation}
+where $\varphi\left((S,E)\right)$ is the real number:
+\begin{itemize}
+\item whose integral part $e$ is $\displaystyle{\sum_{k=0}^9 2^{9-k} E_k}$, that
+is, the binary digits of $e$ are $E_0 ~ E_1 ~ \hdots ~ E_9$.
+\item whose decimal part $s$ is equal to $s = 0,S^0~ S^1~ S^2~ \hdots =
+\sum_{k=1}^{+\infty} 10^{-k} S^{k-1}.$ 
+\end{itemize}
+\end{definition}
+
+
+
+$\varphi$ realizes the association between a point of $\mathcal{X}_{10}$ and a
+real number into $\big[ 0, 2^{10} \big[$. We must now translate the chaotic
+iterations $\Go$ on this real interval. To do so, two intermediate functions
+over $\big[ 0, 2^{10} \big[$ must be introduced:
+
+
+\begin{definition}
+\label{def:e et s}
+Let $x \in \big[ 0, 2^{10} \big[$ and:
+\begin{itemize}
+\item $e_0, \hdots, e_9$ the binary digits of the integral part of $x$:
+$\displaystyle{\lfloor x \rfloor = \sum_{k=0}^{9} 2^{9-k} e_k}$.
+\item $(s^k)_{k\in \mathds{N}}$ the digits of $x$, where the chosen decimal
+decomposition of $x$ is the one that does not have an infinite number of 9: 
+$\displaystyle{x = \lfloor x \rfloor + \sum_{k=0}^{+\infty} s^k 10^{-k-1}}$.
+\end{itemize}
+$e$ and $s$ are thus defined as follows:
+\begin{equation}
+\begin{array}{cccl}
+e: & \big[ 0, 2^{10} \big[ & \longrightarrow & \mathds{B}^{10} \\
+ & x & \longmapsto & (e_0, \hdots, e_9)
+\end{array}
+\end{equation}
+and
+\begin{equation}
+ \begin{array}{cccc}
+s: & \big[ 0, 2^{10} \big[ & \longrightarrow & \llbracket 0, 9
+\rrbracket^{\mathds{N}} \\
+ & x & \longmapsto & (s^k)_{k \in \mathds{N}}
+\end{array}
+\end{equation}
+\end{definition}
+
+We are now able to define the function $g$, whose goal is to translate the
+chaotic iterations $\Go$ on an interval of $\mathds{R}$.
+
+\begin{definition}
+$g:\big[ 0, 2^{10} \big[ \longrightarrow \big[ 0, 2^{10} \big[$ is defined by:
+\begin{equation}
+\begin{array}{cccc}
+g: & \big[ 0, 2^{10} \big[ & \longrightarrow & \big[ 0, 2^{10} \big[ \\
+ & x & \longmapsto & g(x)
+\end{array}
+\end{equation}
+where g(x) is the real number of $\big[ 0, 2^{10} \big[$ defined bellow:
+\begin{itemize}
+\item its integral part has a binary decomposition equal to $e_0', \hdots,
+e_9'$, with:
+ \begin{equation}
+e_i' = \left\{
+\begin{array}{ll}
+e(x)_i & \textrm{ if } i \neq s^0\\
+e(x)_i + 1 \textrm{ (mod 2)} & \textrm{ if } i = s^0\\
+\end{array}
+\right.
+\end{equation}
+\item whose decimal part is $s(x)^1, s(x)^2, \hdots$
+\end{itemize}
+\end{definition}
+
+\bigskip
+
+
+In other words, if $x = \displaystyle{\sum_{k=0}^{9} 2^{9-k} e_k + 
+\sum_{k=0}^{+\infty} s^{k} ~10^{-k-1}}$, then:
+\begin{equation}
+g(x) =
+\displaystyle{\sum_{k=0}^{9} 2^{9-k} (e_k + \delta(k,s^0) \textrm{ (mod 2)}) + 
+\sum_{k=0}^{+\infty} s^{k+1} 10^{-k-1}}. 
+\end{equation}
+
+
+\subsubsection{Defining a metric on $\big[ 0, 2^{10} \big[$}
+
+Numerous metrics can be defined on the set $\big[ 0, 2^{10} \big[$, the most
+usual one being the Euclidian distance recalled bellow:
+
+\begin{notation}
+\index{distance!euclidienne}
+$\Delta$ is the Euclidian distance on $\big[ 0, 2^{10} \big[$, that is,
+$\Delta(x,y) = |y-x|^2$.
+\end{notation}
+
+\medskip
+
+This Euclidian distance does not reproduce exactly the notion of proximity
+induced by our first distance $d$ on $\X$. Indeed $d$ is finer than $\Delta$.
+This is the reason why we have to introduce the following metric:
+
+
+
+\begin{definition}
+Let $x,y \in \big[ 0, 2^{10} \big[$.
+$D$ denotes the function from $\big[ 0, 2^{10} \big[^2$ to $\mathds{R}^+$
+defined by: $D(x,y) = D_e\left(e(x),e(y)\right) + D_s\left(s(x),s(y)\right)$,
+where:
+\begin{center}
+$\displaystyle{D_e(E,\check{E}) = \sum_{k=0}^\mathsf{9} \delta (E_k,
+\check{E}_k)}$, ~~and~ $\displaystyle{D_s(S,\check{S}) = \sum_{k = 1}^\infty
+\dfrac{|S^k-\check{S}^k|}{10^k}}$.
+\end{center}
+\end{definition}
+
+\begin{proposition}
+$D$ is a distance on $\big[ 0, 2^{10} \big[$.
+\end{proposition}
+
+\begin{proof}
+The three axioms defining a distance must be checked.
+\begin{itemize}
+\item $D \geqslant 0$, because everything is positive in its definition. If
+$D(x,y)=0$, then $D_e(x,y)=0$, so the integral parts of $x$ and $y$ are equal
+(they have the same binary decomposition). Additionally, $D_s(x,y) = 0$, then
+$\forall k \in \mathds{N}^*, s(x)^k = s(y)^k$. In other words, $x$ and $y$ have
+the same $k-$th decimal digit, $\forall k \in \mathds{N}^*$. And so $x=y$.
+\item $D(x,y)=D(y,x)$.
+\item Finally, the triangular inequality is obtained due to the fact that both
+$\delta$ and $\Delta(x,y)=|x-y|$ satisfy it.
+\end{itemize}
+\end{proof}
+
+
+The convergence of sequences according to $D$ is not the same than the usual
+convergence related to the Euclidian metric. For instance, if $x^n \to x$
+according to $D$, then necessarily the integral part of each $x^n$ is equal to
+the integral part of $x$ (at least after a given threshold), and the decimal
+part of $x^n$ corresponds to the one of $x$ ``as far as required''.
+To illustrate this fact, a comparison between $D$ and the Euclidian distance is
+given Figure \ref{fig:comparaison de distances}. These illustrations show that
+$D$ is richer and more refined than the Euclidian distance, and thus is more
+precise.
+
+
+\begin{figure}[t]
+\begin{center}
+  \subfigure[Function $x \to dist(x;1,234) $ on the interval
+$(0;5)$.]{\includegraphics[scale=.35]{DvsEuclidien.pdf}}\quad
+  \subfigure[Function $x \to dist(x;3) $ on the interval
+$(0;5)$.]{\includegraphics[scale=.35]{DvsEuclidien2.pdf}}
+\end{center}
+\caption{Comparison between $D$ (in blue) and the Euclidian distane (in green).}
+\label{fig:comparaison de distances}
+\end{figure}
+
+
+
+
+\subsubsection{The semiconjugacy}
+
+It is now possible to define a topological semiconjugacy between $\mathcal{X}$
+and an interval of $\mathds{R}$:
+
+\begin{theorem}
+Chaotic iterations on the phase space $\mathcal{X}$ are simple iterations on
+$\mathds{R}$, which is illustrated by the semiconjugacy of the diagram bellow:
+\begin{equation*}
+\begin{CD}
+\left(~\mathcal{S}_{10} \times\mathds{B}^{10}, d~\right) @>G_{f_0}>>
+\left(~\mathcal{S}_{10} \times\mathds{B}^{10}, d~\right)\\
+    @V{\varphi}VV                    @VV{\varphi}V\\
+\left( ~\big[ 0, 2^{10} \big[, D~\right)  @>>g> \left(~\big[ 0, 2^{10} \big[,
+D~\right)
+\end{CD}
+\end{equation*}
+\end{theorem}
+
+\begin{proof}
+$\varphi$ has been constructed in order to be continuous and onto.
+\end{proof}
+
+In other words, $\mathcal{X}$ is approximately equal to $\big[ 0, 2^\mathsf{N}
+\big[$.
+
+
+
+
+
+
+\subsection{Study of the chaotic iterations described as a real function}
+
+
+\begin{figure}[t]
+\begin{center}
+  \subfigure[ICs on the interval
+$(0,9;1)$.]{\includegraphics[scale=.35]{ICs09a1.pdf}}\quad
+  \subfigure[ICs on the interval
+$(0,7;1)$.]{\includegraphics[scale=.35]{ICs07a95.pdf}}\\
+  \subfigure[ICs on the interval
+$(0,5;1)$.]{\includegraphics[scale=.35]{ICs05a1.pdf}}\quad
+  \subfigure[ICs on the interval
+$(0;1)$]{\includegraphics[scale=.35]{ICs0a1.pdf}}
+\end{center}
+\caption{Representation of the chaotic iterations.}
+\label{fig:ICs}
+\end{figure}
+
+
+
+
+\begin{figure}[t]
+\begin{center}
+  \subfigure[ICs on the interval
+$(510;514)$.]{\includegraphics[scale=.35]{ICs510a514.pdf}}\quad
+  \subfigure[ICs on the interval
+$(1000;1008)$]{\includegraphics[scale=.35]{ICs1000a1008.pdf}}
+\end{center}
+\caption{ICs on small intervals.}
+\label{fig:ICs2}
+\end{figure}
+
+\begin{figure}[t]
+\begin{center}
+  \subfigure[ICs on the interval
+$(0;16)$.]{\includegraphics[scale=.3]{ICs0a16.pdf}}\quad
+  \subfigure[ICs on the interval 
+$(40;70)$.]{\includegraphics[scale=.45]{ICs40a70.pdf}}\quad
+\end{center}
+\caption{General aspect of the chaotic iterations.}
+\label{fig:ICs3}
+\end{figure}
+
+
+We have written a Python program to represent the chaotic iterations with the
+vectorial negation on the real line $\mathds{R}$. Various representations of
+these CIs are given in Figures \ref{fig:ICs}, \ref{fig:ICs2} and \ref{fig:ICs3}.
+It can be remarked that the function $g$ is a piecewise linear function: it is
+linear on each interval having the form $\left[ \dfrac{n}{10},
+\dfrac{n+1}{10}\right[$, $n \in \llbracket 0;2^{10}\times 10 \rrbracket$ and its
+slope is equal to 10. Let us justify these claims:
+
+\begin{proposition}
+\label{Prop:derivabilite des ICs}
+Chaotic iterations $g$ defined on $\mathds{R}$ have derivatives of all orders on
+$\big[ 0, 2^{10} \big[$, except on the 10241 points in $I$ defined by $\left\{
+\dfrac{n}{10} ~\big/~ n \in \llbracket 0;2^{10}\times 10\rrbracket \right\}$.
+
+Furthermore, on each interval of the form $\left[ \dfrac{n}{10},
+\dfrac{n+1}{10}\right[$, with $n \in \llbracket 0;2^{10}\times 10 \rrbracket$,
+$g$ is a linear function, having a slope equal to 10: $\forall x \notin I,
+g'(x)=10$.
+\end{proposition}
+
+
+\begin{proof}
+Let $I_n = \left[ \dfrac{n}{10}, \dfrac{n+1}{10}\right[$, with $n \in \llbracket
+0;2^{10}\times 10 \rrbracket$. All the points of $I_n$ have the same integral
+prat $e$ and the same decimal part $s^0$: on the set $I_n$,  functions $e(x)$
+and $x \mapsto s(x)^0$ of Definition \ref{def:e et s} only depend on $n$. So all
+the images $g(x)$ of these points $x$:
+\begin{itemize}
+\item Have the same integral part, which is $e$, except probably the bit number
+$s^0$. In other words, this integer has approximately the same binary
+decomposition than $e$, the sole exception being the digit $s^0$ (this number is
+then either $e+2^{10-s^0}$ or $e-2^{10-s^0}$, depending on the parity of $s^0$,
+\emph{i.e.}, it is equal to $e+(-1)^{s^0}\times 2^{10-s^0}$).
+\item A shift to the left has been applied to the decimal part $y$, losing by
+doing so the common first digit $s^0$. In other words, $y$ has been mapped into
+$10\times y - s^0$.
+\end{itemize}
+To sum up, the action of $g$ on the points of $I$ is as follows: first, make a
+multiplication by 10, and second, add the same constant to each term, which is
+$\dfrac{1}{10}\left(e+(-1)^{s^0}\times 2^{10-s^0}\right)-s^0$.
+\end{proof}
+
+\begin{remark}
+Finally, chaotic iterations are elements of the large family of functions that
+are both chaotic and piecewise linear (like the tent map).
+\end{remark}
+
+
+
+\subsection{Comparison of the two metrics on $\big[ 0, 2^\mathsf{N} \big[$}
+
+The two propositions bellow allow to compare our two distances on $\big[ 0,
+2^\mathsf{N} \big[$:
+
+\begin{proposition}
+Id: $\left(~\big[ 0, 2^\mathsf{N} \big[,\Delta~\right) \to \left(~\big[ 0,
+2^\mathsf{N} \big[, D~\right)$ is not continuous. 
+\end{proposition}
+
+\begin{proof}
+The sequence $x^n = 1,999\hdots 999$ constituted by $n$ 9 as decimal part, is
+such that:
+\begin{itemize}
+\item $\Delta (x^n,2) \to 0.$
+\item But $D(x^n,2) \geqslant 1$, then $D(x^n,2)$ does not converge to 0.
+\end{itemize}
+
+The sequential characterization of the continuity concludes the demonstration.
+\end{proof}
+
+
+
+A contrario:
+
+\begin{proposition}
+Id: $\left(~\big[ 0, 2^\mathsf{N} \big[,D~\right) \to \left(~\big[ 0,
+2^\mathsf{N} \big[, \Delta ~\right)$ is a continuous fonction. 
+\end{proposition}
+
+\begin{proof}
+If $D(x^n,x) \to 0$, then $D_e(x^n,x) = 0$ at least for $n$ larger than a given
+threshold, because $D_e$ only returns integers. So, after this threshold, the
+integral parts of all the $x^n$ are equal to the integral part of $x$. 
+
+Additionally, $D_s(x^n, x) \to 0$, then $\forall k \in \mathds{N}^*, \exists N_k
+\in \mathds{N}, n \geqslant N_k \Rightarrow D_s(x^n,x) \leqslant 10^{-k}$. This
+means that for all $k$, an index $N_k$ can be found such that, $\forall n
+\geqslant N_k$, all the $x^n$ have the same $k$ firsts digits, which are the
+digits of $x$. We can deduce the convergence $\Delta(x^n,x) \to 0$, and thus the
+result.
+\end{proof}
+
+The conclusion of these propositions is that the proposed metric is more precise
+than the Euclidian distance, that is:
+
+\begin{corollary}
+$D$ is finer than the Euclidian distance $\Delta$.
+\end{corollary}
+
+This corollary can be reformulated as follows:
+
+\begin{itemize}
+\item The topology produced by $\Delta$ is a subset of the topology produced by
+$D$.
+\item $D$ has more open sets than $\Delta$.
+\item It is harder to converge for the topology $\tau_D$ inherited by $D$, than
+to converge with the one inherited by $\Delta$, which is denoted here by
+$\tau_\Delta$.
+\end{itemize}
+
+
+\subsection{Chaos of the chaotic iterations on $\mathds{R}$}
+\label{chpt:Chaos des itérations chaotiques sur R}
+
+
+
+\subsubsection{Chaos according to Devaney}
+
+We have recalled previously that the chaotic iterations $\left(\Go,
+\mathcal{X}_d\right)$ are chaotic according to the formulation of Devaney. We
+can deduce that they are chaotic on $\mathds{R}$ too, when considering the order
+topology, because:
+\begin{itemize}
+\item $\left(\Go, \mathcal{X}_d\right)$ and $\left(g, \big[ 0, 2^{10}
+\big[_D\right)$ are semiconjugate by $\varphi$,
+\item Then $\left(g, \big[ 0, 2^{10} \big[_D\right)$ is a system chaotic
+according to Devaney, because the semiconjugacy preserve this character.
+\item But the topology generated by $D$ is finer than the topology generated by
+the Euclidian distance $\Delta$ -- which is the order topology.
+\item According to Theorem \ref{Th:chaos et finesse}, we can deduce that the
+chaotic iterations $g$ are indeed chaotic, as defined by Devaney, for the order
+topology on $\mathds{R}$.
+\end{itemize}
+
+This result can be formulated as follows.
+
+\begin{theorem}
+\label{th:IC et topologie de l'ordre}
+The chaotic iterations $g$ on $\mathds{R}$ are chaotic according to the
+Devaney's formulation, when $\mathds{R}$ has his usual topology, which is the
+order topology.
+\end{theorem}
+
+Indeed this result is weaker than the theorem establishing the chaos for the
+finer topology $d$. However the Theorem \ref{th:IC et topologie de l'ordre}
+still remains important. Indeed, we have studied in our previous works a set
+different from the usual set of study ($\mathcal{X}$ instead of $\mathds{R}$),
+in order to be as close as possible from the computer: the properties of
+disorder proved theoretically will then be preserved when computing. However, we
+could wonder whether this change does not lead to a disorder of a lower quality.
+In other words, have we replaced a situation of a good disorder lost when
+computing, to another situation of a disorder preserved but of bad quality.
+Theorem \ref{th:IC et topologie de l'ordre} prove exactly the contrary.
+ 
+
+
+
+
+
+
+
+\section{Conclusion}
+\bibliographystyle{plain}
+\bibliography{mabase}
 \end{document}