Avancées et réécritures

[prng_gpu.git] / prng_gpu.tex

diff --git a/prng_gpu.tex b/prng_gpu.tex
index d1fb7a67a7f66791e989f8b6111d6aa4b3c52ec0..5a3324b58e99e2d82aaef87e590f0a21e58e8806 100644 (file)
--- a/prng_gpu.tex
+++ b/prng_gpu.tex
@@ -34,10 +34,12 @@
 
 \newcommand{\alert}[1]{\begin{color}{blue}\textit{#1}\end{color}}
 
 
 \newcommand{\alert}[1]{\begin{color}{blue}\textit{#1}\end{color}}
 

-\title{Efficient generation of pseudo random numbers based on chaotic iterations on GPU}
+\title{Efficient generation of pseudo random numbers based on chaotic iterations
+on GPU}

 \begin{document}
 
 \begin{document}
 

-\author{Jacques M. Bahi, Rapha\"{e}l Couturier, and Christophe Guyeux\thanks{Authors in alphabetic order}}
+\author{Jacques M. Bahi, Rapha\"{e}l Couturier, and Christophe
+Guyeux\thanks{Authors in alphabetic order}}

 
 \maketitle
 
 
 \maketitle
 


@@ -55,41 +57,65 @@ Interet de générer des nombres alea sur GPU
 
 \section{Basic Recalls}
 \label{section:BASIC RECALLS}
 
 \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.
+This section is devoted to basic definitions and terminologies in the fields of
+topological chaos and chaotic iterations.

 \subsection{Devaney's chaotic dynamical systems}
 
 \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: $\llbracket1;N\rrbracket=\{1,2,\hdots,N\}$.
+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 $f : \mathcal{X} \rightarrow \mathcal{X}$.
+Consider a topological space $(\mathcal{X},\tau)$ and a continuous function $f :
+\mathcal{X} \rightarrow \mathcal{X}$.

 
 \begin{definition}
 
 \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 $f^k(U) \cap V \neq \varnothing$.
+$f$ 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}
 \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 $Per(f).$
+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}
 \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).
+$f$ 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}
 \end{definition}
 
 
 \begin{definition}

-$f$ is said to be \emph{chaotic} on $(\mathcal{X},\tau)$ if $f$ is regular and topologically transitive.
+$f$ is said to be \emph{chaotic} on $(\mathcal{X},\tau)$ if $f$ is regular and
+topologically transitive.

 \end{definition}
 
 \end{definition}
 

-The chaos property is strongly linked to the notion of ``sensitivity'', defined on a metric space $(\mathcal{X},d)$ by:
+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}
 
 \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 $.
+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}
 
 
 $\delta$ 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.
+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.

 
 
 
 
 
 


@@ -112,14 +138,14 @@ $f:\mathds{B}^{\mathsf{N}}\longrightarrow  \mathds{B}^{\mathsf{N}}$ 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
 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.
 \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
 \end{definition}
 
 In other words, at the $n^{th}$ iteration, only the $S^{n}-$th cell is


@@ -132,46 +158,57 @@ the term  ``chaotic'', in  the name of  these iterations,  has \emph{a
 priori} no link with the mathematical theory of chaos, recalled above.
 
 
 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 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*}
+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}%
 \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*}%
+\end{equation}%

 \noindent where + and . are the Boolean addition and product operations.
 Consider the phase space:
 \noindent where + and . are the Boolean addition and product operations.
 Consider the phase space:

-\begin{equation*}
+\begin{equation}

 \mathcal{X} = \llbracket 1 ; \mathsf{N} \rrbracket^\mathds{N} \times
 \mathds{B}^\mathsf{N},
 \mathcal{X} = \llbracket 1 ; \mathsf{N} \rrbracket^\mathds{N} \times
 \mathds{B}^\mathsf{N},

-\end{equation*}
+\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 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*}
+\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.
 \left\{
 \begin{array}{l}
 X^0 \in \mathcal{X} \\
 X^{k+1}=G_{f}(X^k).%
 \end{array}%
 \right.

-\end{equation*}%
+\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. 
+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
+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:
 \mathcal{X}$ has been introduced in \cite{guyeux10} as follows:

-\begin{equation*}
+\begin{equation}

 d(X,Y)=d_{e}(E,\check{E})+d_{s}(S,\check{S}),
 d(X,Y)=d_{e}(E,\check{E})+d_{s}(S,\check{S}),

-\end{equation*}
+\end{equation}

 \noindent where
 \noindent where

-\begin{equation*}
+\begin{equation}

 \left\{
 \begin{array}{lll}
 \displaystyle{d_{e}(E,\check{E})} & = & \displaystyle{\sum_{k=1}^{\mathsf{N}%
 \left\{
 \begin{array}{lll}
 \displaystyle{d_{e}(E,\check{E})} & = & \displaystyle{\sum_{k=1}^{\mathsf{N}%


@@ -180,23 +217,38 @@ d(X,Y)=d_{e}(E,\check{E})+d_{s}(S,\check{S}),
 \sum_{k=1}^{\infty }\dfrac{|S^k-\check{S}^k|}{10^{k}}}.%
 \end{array}%
 \right.
 \sum_{k=1}^{\infty }\dfrac{|S^k-\check{S}^k|}{10^{k}}}.%
 \end{array}%
 \right.

-\end{equation*}
+\end{equation}

 
 
 This new distance has been introduced to satisfy the following requirements.
 \begin{itemize}
 
 
 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.
+\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}
 \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.
+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}
 
 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 $(\mathcal{X},d)$.
+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}
 
 \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.
+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
 
 Let $f$ be a map from $\mathds{B}^n$ to itself. The
 {\emph{asynchronous iteration graph}} associated with $f$ is the


@@ -208,7 +260,7 @@ 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 $x'$.
 
 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{FCT11} that,
+We have finally proven in \cite{bcgr11:ip} that,

 
 
 \begin{theorem}
 
 
 \begin{theorem}


@@ -217,25 +269,34 @@ 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}
 
 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).
+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}
 
 
 \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 
 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.
+possesses various chaos properties that none of the generators used as input
+present.

 
 \begin{algorithm}[h!]
 %\begin{scriptsize}
 
 \begin{algorithm}[h!]
 %\begin{scriptsize}

-\KwIn{a function $f$, an iteration number $b$, an initial configuration $x^0$ ($n$ bits)}
+\KwIn{a function $f$, an iteration number $b$, an initial configuration $x^0$
+($n$ bits)}

 \KwOut{a configuration $x$ ($n$ bits)}
 $x\leftarrow x^0$\;
 \KwOut{a configuration $x$ ($n$ bits)}
 $x\leftarrow x^0$\;

-$k\leftarrow b + \textit{XORshift}(b+1)$\;
-\For{$i=0,\dots,k-1$}
+$k\leftarrow b + \textit{XORshift}(b)$\;
+\For{$i=0,\dots,k$}

 {
 $s\leftarrow{\textit{XORshift}(n)}$\;
 $x\leftarrow{F_f(s,x)}$\;
 {
 $s\leftarrow{\textit{XORshift}(n)}$\;
 $x\leftarrow{F_f(s,x)}$\;


@@ -247,7 +308,6 @@ return $x$\;
 \end{algorithm}
 
 \begin{algorithm}[h!]
 \end{algorithm}
 
 \begin{algorithm}[h!]

-%\SetAlgoLined                        %%RAPH: cette ligne provoque une erreur chez moi

 \KwIn{the internal configuration $z$ (a 32-bit word)}
 \KwOut{$y$ (a 32-bit word)}
 $z\leftarrow{z\oplus{(z\ll13)}}$\;
 \KwIn{the internal configuration $z$ (a 32-bit word)}
 \KwOut{$y$ (a 32-bit word)}
 $z\leftarrow{z\oplus{(z\ll13)}}$\;


@@ -264,30 +324,404 @@ return $y$\;
 
 
 
 
 
 

-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.
+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}
+

 
 
 
 

-We have proven in \cite{FCT11} that,
+\section{Experiments}

 
 

-\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} 
+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. 

 
 

-\alert{Mettre encore un peu de blabla sur le PRNG, puis enchaîner en disant que, ok, on peut préserver le chaos quand on passe sur machine, mais que le chaos dont il s'agit a été prouvé pour une distance bizarroïde sur un espace non moins hémoroïde, d'où ce qui suit}
+Faire une courbe du nombre de random en fonction du nombre de threads,
+éventuellement en fonction du nombres de threads par bloc.

 
 
 
 
 
 


@@ -299,45 +733,69 @@ We have proven in \cite{FCT11} that,
 Let us firstly introduce the following notations.
 
 \begin{notation}
 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).
+$\mathcal{X}_\tau$ 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}
 \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 $\tau'$.
+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.
+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 $(\mathcal{X}_\tau,f)$.
 
 \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 $\omega_1, \omega_2$ 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 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, $\exists \omega \in \tau, x \in \omega \subset V$.
+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.
+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}
 
 \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):
+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}
 
 \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 $\X$.
+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}
 \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.
+$f$ 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}
 
 
 \end{proof}
 
 


@@ -347,17 +805,26 @@ Let us now consider $x \in \X$ and $V \in \mathcal{V}_{\tau_0} (x)$. Then $V = \
 
 \begin{theorem}
 Let $\mathcal{X}$ be a set and $f: \mathcal{X} \to \X$.
 
 \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.
+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}
 \end{theorem}
 
 \begin{proof}

-Let us prove it by contradiction, assuming that $\left(\X_{\tau_\infty}, f\right)$ is both transitive and regular.
+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 $x \in \X$ 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 $\exists y \in \mathcal{X}, y \notin I_x$.
+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 $y \notin I_x \Rightarrow \forall n, f^{(n)}\left( \{x\} \right) \cap \{y\} = \varnothing$.
+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}
 
 
 \end{proof}
 
 


@@ -371,77 +838,94 @@ As $\left(\X_{\tau_\infty}, f\right)$ must be transitive, for all open nonempty
 
 \subsubsection{Toward a topological semiconjugacy}
 
 
 \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. 
+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 $\mathcal{X}_{\mathsf{N}} = \mathcal{S}_\mathsf{N} \times \B^\mathsf{N}$.
+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}
 
 
 \begin{definition}

-The function $\varphi: \mathcal{S}_{10} \times\mathds{B}^{10} \rightarrow \big[ 0, 2^{10} \big[$ is defined by:
-$$
-\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)
+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{array}

-$$
-\noindent where $\varphi\left((S,E)\right)$ is the real number:
+\end{equation}
+where $\varphi\left((S,E)\right)$ is the real number:

 \begin{itemize}
 \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 $s = 0,S^0~ S^1~ S^2~ \hdots = \sum_{k=1}^{+\infty} 10^{-k} S^{k-1}.$ 
+\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}
 
 
 
 \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:
+$\varphi$ 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}
 
 
 \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: 
+\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:
 $\displaystyle{x = \lfloor x \rfloor + \sum_{k=0}^{+\infty} s^k 10^{-k-1}}$.
 \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}
 \begin{array}{cccl}
 e: & \big[ 0, 2^{10} \big[ & \longrightarrow & \mathds{B}^{10} \\
  & x & \longmapsto & (e_0, \hdots, e_9)
 \end{array}

-$$
-\noindent and
-$$
-\begin{array}{cccl}
-s: & \big[ 0, 2^{10} \big[ & \longrightarrow & \llbracket 0, 9 \rrbracket^{\mathds{N}} \\
+\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}
  & x & \longmapsto & (s^k)_{k \in \mathds{N}}
 \end{array}

-$$
+\end{equation}

 \end{definition}
 
 \end{definition}
 

-We are now able to define the function $g$, whose goal is to translate the chaotic iterations $\Go$ on an interval of $\mathds{R}$.
+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{definition}
 $g:\big[ 0, 2^{10} \big[ \longrightarrow \big[ 0, 2^{10} \big[$ is defined by:

-$$
-\begin{array}{cccl}
+\begin{equation}
+\begin{array}{cccc}

 g: & \big[ 0, 2^{10} \big[ & \longrightarrow & \big[ 0, 2^{10} \big[ \\
 g: & \big[ 0, 2^{10} \big[ & \longrightarrow & \big[ 0, 2^{10} \big[ \\

-& \\

  & x & \longmapsto & g(x)
 \end{array}
  & x & \longmapsto & g(x)
 \end{array}

-$$
-\noindent where g(x) is the real number of $\big[ 0, 2^{10} \big[$ defined bellow:
+\end{equation}
+where g(x) is the real number of $\big[ 0, 2^{10} \big[$ defined bellow:

 \begin{itemize}
 \begin{itemize}

-\item its integral part has a binary decomposition equal to $e_0', \hdots, e_9'$, with:
-$$
+\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.
 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}
 \item whose decimal part is $s(x)^1, s(x)^2, \hdots$
 \end{itemize}
 \end{definition}


@@ -449,28 +933,43 @@ $$
 \bigskip
 
 
 \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: $$g(x) = \displaystyle{\sum_{k=0}^{9} 2^{9-k} (e_k + \delta(k,s^0) \textrm{ (mod 2)}) +  \sum_{k=0}^{+\infty} s^{k+1} 10^{-k-1}}.$$
+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 $\big[ 0, 2^{10} \big[$}
 
 
 \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:
+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}
 
 \begin{notation}
 \index{distance!euclidienne}

-$\Delta$ is the Euclidian distance on $\big[ 0, 2^{10} \big[$, that is, $\Delta(x,y) = |y-x|^2$.
+$\Delta$ is the Euclidian distance on $\big[ 0, 2^{10} \big[$, that is,
+$\Delta(x,y) = |y-x|^2$.

 \end{notation}
 
 \medskip
 
 \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:
+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 $x,y \in \big[ 0, 2^{10} \big[$.
 
 
 
 \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:
+$D$ 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}
 \begin{center}

-$\displaystyle{D_e(E,\check{E}) = \sum_{k=0}^\mathsf{9} \delta (E_k, \check{E}_k)}$, ~~and~ $\displaystyle{D_s(S,\check{S}) = \sum_{k = 1}^\infty \dfrac{|S^k-\check{S}^k|}{10^k}}$.
+$\displaystyle{D_e(E,\check{E}) = \sum_{k=0}^\mathsf{9} \delta (E_k,
+\check{E}_k)}$, ~~and~ $\displaystyle{D_s(S,\check{S}) = \sum_{k = 1}^\infty
+\dfrac{|S^k-\check{S}^k|}{10^k}}$.

 \end{center}
 \end{definition}
 
 \end{center}
 \end{definition}
 


@@ -481,21 +980,35 @@ $D$ is a distance on $\big[ 0, 2^{10} \big[$.
 \begin{proof}
 The three axioms defining a distance must be checked.
 \begin{itemize}
 \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 $D \geqslant 0$, 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 $D(x,y)=D(y,x)$.

-\item Finally, the triangular inequality is obtained due to the fact that both $\delta$ and $\Delta(x,y)=|x-y|$ satisfy it.
+\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}
 
 
 \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.
+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}
 
 
 \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}}
+  \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{center}
 \caption{Comparison between $D$ (in blue) and the Euclidian distane (in green).}
 \label{fig:comparaison de distances}


@@ -506,15 +1019,19 @@ To illustrate this fact, a comparison between $D$ and the Euclidian distance is
 
 \subsubsection{The semiconjugacy}
 
 
 \subsubsection{The semiconjugacy}
 

-It is now possible to define a topological semiconjugacy between $\mathcal{X}$ and an interval of $\mathds{R}$:
+It is now possible to define a topological semiconjugacy between $\mathcal{X}$
+and an interval of $\mathds{R}$:

 
 \begin{theorem}
 
 \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:
+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}
 \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)\\
+\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\\
     @V{\varphi}VV                    @VV{\varphi}V\\

-\left( ~\big[ 0, 2^{10} \big[, D~\right)  @>>g> \left(~\big[ 0, 2^{10} \big[, D~\right)
+\left( ~\big[ 0, 2^{10} \big[, D~\right)  @>>g> \left(~\big[ 0, 2^{10} \big[,
+D~\right)

 \end{CD}
 \end{equation*}
 \end{theorem}
 \end{CD}
 \end{equation*}
 \end{theorem}


@@ -523,7 +1040,8 @@ Chaotic iterations on the phase space $\mathcal{X}$ are simple iterations on $\m
 $\varphi$ has been constructed in order to be continuous and onto.
 \end{proof}
 
 $\varphi$ has been constructed in order to be continuous and onto.
 \end{proof}
 

-In other words, $\mathcal{X}$ is approximately equal to $\big[ 0, 2^\mathsf{N} \big[$.
+In other words, $\mathcal{X}$ is approximately equal to $\big[ 0, 2^\mathsf{N}
+\big[$.

 
 
 
 
 
 


@@ -535,10 +1053,14 @@ In other words, $\mathcal{X}$ is approximately equal to $\big[ 0, 2^\mathsf{N} \
 
 \begin{figure}[t]
 \begin{center}
 
 \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}}
+  \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{center}
 \caption{Representation of the chaotic iterations.}
 \label{fig:ICs}


@@ -549,8 +1071,10 @@ In other words, $\mathcal{X}$ is approximately equal to $\big[ 0, 2^\mathsf{N} \
 
 \begin{figure}[t]
 \begin{center}
 
 \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}}
+  \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{center}
 \caption{ICs on small intervals.}
 \label{fig:ICs2}


@@ -558,49 +1082,78 @@ In other words, $\mathcal{X}$ is approximately equal to $\big[ 0, 2^\mathsf{N} \
 
 \begin{figure}[t]
 \begin{center}
 
 \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
+  \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}
 
 
 \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:
+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}
 
 \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: $\forall x \notin I, g'(x)=10$.
+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}
 \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 $x$:
+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}
 \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 $10\times y - s^0$.
+\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}
 \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 $\dfrac{1}{10}\left(e+(-1)^{s^0}\times 2^{10-s^0}\right)-s^0$.
+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}
 \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).
+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 $\big[ 0, 2^\mathsf{N} \big[$}
 
 \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 $\big[ 0, 2^\mathsf{N} \big[$:
+The two propositions bellow allow to compare our two distances on $\big[ 0,
+2^\mathsf{N} \big[$:

 
 \begin{proposition}
 
 \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. 
+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}
 \end{proposition}
 
 \begin{proof}

-The sequence $x^n = 1,999\hdots 999$ constituted by $n$ 9 as decimal part, is such that:
+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.
 \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.


@@ -614,16 +1167,25 @@ The sequential characterization of the continuity concludes the demonstration.
 A contrario:
 
 \begin{proposition}
 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. 
+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}
 \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.
+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}
 
 \end{proof}
 

-The conclusion of these propositions is that the proposed metric is more precise than the Euclidian distance, that is:
+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 $\Delta$.
 
 \begin{corollary}
 $D$ is finer than the Euclidian distance $\Delta$.


@@ -632,9 +1194,12 @@ $D$ is finer than the Euclidian distance $\Delta$.
 This corollary can be reformulated as follows:
 
 \begin{itemize}
 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 The topology produced by $\Delta$ is a subset of the topology produced by
+$D$.

 \item $D$ has more open sets than $\Delta$.
 \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 $\tau_\Delta$.
+\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}
 
 
 \end{itemize}
 
 


@@ -645,245 +1210,47 @@ This corollary can be reformulated as follows:
 
 \subsubsection{Chaos according to Devaney}
 
 
 \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:
+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}
 \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 $\mathds{R}$.
+\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}
 \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.
+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}
 
 \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.
+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{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=.5]{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{Conclusion}
 
 
 \section{Conclusion}