From: Christophe Guyeux <christophe.guyeux@univ-fcomte.fr>
Date: Thu, 8 Sep 2011 14:58:17 +0000 (+0200)
Subject: Ajout des sections sur les basic recalls
X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/prng_gpu.git/commitdiff_plain/6e51d690ec930ddb226ef13eb704e9dec7d2ea9f?ds=inline

Ajout des sections sur les basic recalls
---

diff --git a/mabase.bib b/mabase.bib
index 26c2851..e3bb9ae 100644
--- a/mabase.bib
+++ b/mabase.bib
@@ -4164,6 +4164,24 @@
   booktitle = {FOCS}
 }
 
+@inproceedings{FCT11,
+inhal = {no},
+domainehal = {INFO:INFO_DC, INFO:INFO_CR, INFO:INFO_MO},
+equipe = {and},
+classement = {ACTI},
+author = {Bahi, Jacques and Couchot, Jean-fran\c{c}ois and Guyeux, Christophe and Richard, Adrien},
+title = {On the Link Between Strongly Connected Iteration Graphs and Chaotic Boolean Discrete-Time Dynamical Systems},
+booktitle = {FCT'11, 18th Int. Symp. on Fundamentals of Computation Theory},
+series = {LNCS},
+volume = {*},
+pages = {***--***},
+address = {Oslo, Norway},
+month = aug,
+year = 2011,
+note = {To appear},
+
+}
+
 @comment{jabref-meta: selector_publisher:}
 
 @comment{jabref-meta: selector_author:}
diff --git a/prng_gpu.tex b/prng_gpu.tex
index 082c379..985faee 100644
--- a/prng_gpu.tex
+++ b/prng_gpu.tex
@@ -7,6 +7,7 @@
 \usepackage{amscd}
 \usepackage{moreverb}
 \usepackage{commath}
+\usepackage{algorithm2e}
 \usepackage[standard]{ntheorem}
 
 % Pour mathds : les ensembles IR, IN, etc.
@@ -34,6 +35,9 @@
 
 \title{Efficient generation of pseudo random numbers based on chaotic iterations on GPU}
 \begin{document}
+
+\author{Jacques M. Bahi, Rapha\"{e}l Couturier, and Christophe Guyeux\thanks{Authors in alphabetic order}}
+
 \maketitle
 
 \begin{abstract}
@@ -46,11 +50,205 @@ Interet des itérations chaotiques pour générer des nombre alea\\
 Interet de générer des nombres alea sur GPU
 ...
 
-\section{Chaotic iterations}
+% >>>>>>>>>>>>>>>>>>>>>> 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.
+\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 $f : \mathcal{X} \rightarrow \mathcal{X}$.
 
-Présentation des itérations chaotiques
+\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
+$$
+\forall    n\in     \mathds{N}^{\ast     },    \forall     i\in
+\llbracket1;\mathsf{N}\rrbracket ,x_i^n=\left\{
+\begin{array}{ll}
+  x_i^{n-1} &  \text{ if  }S^n\neq i \\
+  \left(f(x^{n-1})\right)_{S^n} & \text{ if }S^n=i.
+\end{array}\right.
+$$
+\end{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. 
+
+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 proven in \cite{FCT11} 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}
+
+
+
+
+\section{Application to Pseudo-Randomness}
+
+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)}
+$x\leftarrow x^0$\;
+$k\leftarrow b + \textit{XORshift}(b+1)$\;
+\For{$i=0,\dots,k-1$}
+{
+$s\leftarrow{\textit{XORshift}(n)}$\;
+$x\leftarrow{F_f(s,x)}$\;
+}
+return $x$\;
+%\end{scriptsize}
+\caption{PRNG with chaotic functions}
+\label{CI Algorithm}
+\end{algorithm}
+
+\begin{algorithm}[h!]
+\SetAlgoLined
+\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{FCT11} 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} 
 
 
 \section{The relativity of disorder}