Ajout de Blum-Goldwasser

[prng_gpu.git] / prng_gpu.tex

diff --git a/prng_gpu.tex b/prng_gpu.tex
index 00991e9cb0d3fe30956f7ea5d4f311d801d8b6ca..5ebe0efb295e6ae78a21bead73fd1cf8cef19b52 100644 (file)
--- a/prng_gpu.tex
+++ b/prng_gpu.tex
@@ -34,66 +34,246 @@
 
 \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 and Cryptographically Secure Generation of Chaotic Pseudorandom Numbers 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,  Christophe
+Guyeux, and Pierre-Cyrille Heam\thanks{Authors in alphabetic order}}
+   

 \maketitle
 
 \begin{abstract}
 \maketitle
 
 \begin{abstract}

-This is the abstract
+In this paper we present a new pseudorandom number generator (PRNG) on
+graphics processing units  (GPU). This PRNG is based  on the so-called chaotic iterations.  It
+is firstly proven  to be chaotic according to the Devaney's  formulation. We thus propose  an efficient
+implementation  for  GPU that successfully passes the   {\it BigCrush} tests, deemed to be the  hardest
+battery of tests in TestU01.  Experiments show that this PRNG can generate
+about 20 billions of random numbers  per second on Tesla C1060 and NVidia GTX280
+cards.
+It is finally established that, under reasonable assumptions, the proposed PRNG can be cryptographically 
+secure.
+
+

 \end{abstract}
 
 \section{Introduction}
 
 \end{abstract}
 
 \section{Introduction}
 

-Interet des itérations chaotiques pour générer des nombre alea\\
-Interet de générer des nombres alea sur GPU
-\alert{RC, un petit state-of-the-art sur les PRNGs sur GPU ?}
-...
-
+Randomness is of importance in many fields as scientific simulations or cryptography. 
+``Random numbers'' can mainly be generated either by a deterministic and reproducible algorithm
+called a pseudorandom number generator (PRNG), or by a physical non-deterministic 
+process having all the characteristics of a random noise, called a truly random number
+generator (TRNG). 
+In this paper, we focus on reproducible generators, useful for instance in
+Monte-Carlo based simulators or in several cryptographic schemes.
+These domains need PRNGs that are statistically irreproachable. 
+On some fields as in numerical simulations, speed is a strong requirement
+that is usually attained by using parallel architectures. In that case,
+a recurrent problem is that a deflate of the statistical qualities is often
+reported, when the parallelization of a good PRNG is realized.
+This is why ad-hoc PRNGs for each possible architecture must be found to
+achieve both speed and randomness.
+On the other side, speed is not the main requirement in cryptography: the great
+need is to define \emph{secure} generators being able to withstand malicious
+attacks. Roughly speaking, an attacker should not be able in practice to make 
+the distinction between numbers obtained with the secure generator and a true random
+sequence. 
+Finally, a small part of the community working in this domain focus on a
+third requirement, that is to define chaotic generators.
+The main idea is to take benefits from a chaotic dynamical system to obtain a
+generator that is unpredictable, disordered, sensible to its seed, or in other words chaotic.
+Their desire is to map a given chaotic dynamics into a sequence that seems random 
+and unassailable due to chaos.
+However, the chaotic maps used as a pattern are defined in the real line 
+whereas computers deal with finite precision numbers.
+This distortion leads to a deflation of both chaotic properties and speed.
+Furthermore, authors of such chaotic generators often claim their PRNG
+as secure due to their chaos properties, but there is no obvious relation
+between chaos and security as it is understood in cryptography.
+This is why the use of chaos for PRNG still remains marginal and disputable.
+
+The authors' opinion is that topological properties of disorder, as they are
+properly defined in the mathematical theory of chaos, can reinforce the quality
+of a PRNG. But they are not substitutable for security or statistical perfection.
+Indeed, to the authors' point of view, such properties can be useful in the two following situations. On the
+one hand, a post-treatment based on a chaotic dynamical system can be applied
+to a PRNG statistically deflective, in order to improve its statistical 
+properties. Such an improvement can be found, for instance, in~\cite{bgw09:ip,bcgr11:ip}.
+On the other hand, chaos can be added to a fast, statistically perfect PRNG and/or a
+cryptographically secure one, in case where chaos can be of interest,
+\emph{only if these last properties are not lost during
+the proposed post-treatment}. Such an assumption is behind this research work.
+It leads to the attempts to define a 
+family of PRNGs that are chaotic while being fast and statistically perfect,
+or cryptographically secure.
+Let us finish this paragraph by noticing that, in this paper, 
+statistical perfection refers to the ability to pass the whole 
+{\it BigCrush} battery of tests, which is widely considered as the most
+stringent statistical evaluation of a sequence claimed as random.
+This battery can be found into the well-known TestU01 package~\cite{LEcuyerS07}.
+Chaos, for its part, refers to the well-established definition of a
+chaotic dynamical system proposed by Devaney~\cite{Devaney}.
+
+
+In a previous work~\cite{bgw09:ip,guyeux10} we have proposed a post-treatment on PRNGs making them behave
+as a chaotic dynamical system. Such a post-treatment leads to a new category of
+PRNGs. We have shown that proofs of Devaney's chaos can be established for this
+family, and that the sequence obtained after this post-treatment can pass the
+NIST~\cite{Nist10}, DieHARD~\cite{Marsaglia1996}, and TestU01~\cite{LEcuyerS07} batteries of tests, even if the inputted generators
+cannot.
+The proposition of this paper is to improve widely the speed of the formerly
+proposed generator, without any lack of chaos or statistical properties.
+In particular, a version of this PRNG on graphics processing units (GPU)
+is proposed.
+Although GPU was initially designed  to accelerate
+the manipulation of  images, they are nowadays commonly  used in many scientific
+applications. Therefore,  it is important  to be able to  generate pseudorandom
+numbers inside a GPU when a scientific application runs in it. This remark
+motivates our proposal of a chaotic and statistically perfect PRNG for GPU.  
+Such device
+allows us to generated almost 20 billions of pseudorandom numbers per second.
+Last, but not least, we show that the proposed post-treatment preserves the
+cryptographical security of the inputted PRNG, when this last has such a 
+property.
+
+The remainder of this paper  is organized as follows. In Section~\ref{section:related
+  works} we  review some GPU implementations  of PRNGs.  Section~\ref{section:BASIC
+  RECALLS} gives some basic recalls  on the well-known Devaney's formulation of chaos, 
+  and on an iteration process called ``chaotic
+iterations'' on which the post-treatment is based. 
+Proofs of chaos are given in  Section~\ref{sec:pseudorandom}.
+Section~\ref{sec:efficient    PRNG}   presents   an   efficient
+implementation of  this chaotic PRNG  on a CPU, whereas   Section~\ref{sec:efficient PRNG
+  gpu}   describes   the  GPU   implementation. 
+Such generators are experimented in 
+Section~\ref{sec:experiments}.
+We show in Section~\ref{sec:security analysis} that, if the inputted
+generator is cryptographically secure, then it is the case too for the
+generator provided by the post-treatment.
+Such a proof leads to the proposition of a cryptographically secure and
+chaotic generator on GPU based on the famous Blum Blum Shum
+in Section~\ref{sec:CSGPU}.
+This research work ends by a conclusion section, in which the contribution is
+summarized and intended future work is presented.
+
+
+
+
+\section{Related works on GPU based PRNGs}
+\label{section:related works}
+
+Numerous research works on defining GPU based PRNGs have yet been proposed  in the
+literature, so that completeness is impossible.
+This is why authors of this document only give reference to the most significant attempts 
+in this domain, from their subjective point of view. 
+The  quantity of pseudorandom numbers generated per second is mentioned here 
+only when the information is given in the related work. 
+A million numbers  per second will be simply written as
+1MSample/s whereas a billion numbers per second is 1GSample/s.
+
+In \cite{Pang:2008:cec}  a PRNG based on  cellular automata is defined
+with no  requirement to an high  precision  integer   arithmetic  or to any bitwise
+operations. Authors can   generate  about
+3.2MSamples/s on a GeForce 7800 GTX GPU, which is quite an old card now.
+However, there is neither a mention of statistical tests nor any proof of
+chaos or cryptography in this document.
+
+In \cite{ZRKB10}, the authors propose  different versions of efficient GPU PRNGs
+based on  Lagged Fibonacci or Hybrid  Taus.  They have  used these
+PRNGs   for  Langevin   simulations   of  biomolecules   fully  implemented   on
+GPU. Performance of  the GPU versions are far better than  those obtained with a
+CPU, and these PRNGs succeed to pass the {\it BigCrush} battery of TestU01. 
+However the evaluations of the proposed PRNGs are only statistical ones.
+
+
+Authors of~\cite{conf/fpga/ThomasHL09}  have studied the  implementation of some
+PRNGs on  different computing architectures: CPU,  field-programmable gate array
+(FPGA), massively parallel  processors, and GPU. This study is of interest, because
+the  performance  of the  same  PRNGs on  different architectures are compared. 
+FPGA appears as  the  fastest  and the most
+efficient architecture, providing the fastest number of generated pseudorandom numbers
+per joule. 
+However, we can notice that authors can ``only'' generate between 11 and 16GSamples/s
+with a GTX 280  GPU, which should be compared with
+the results presented in this document.
+We can remark too that the PRNGs proposed in~\cite{conf/fpga/ThomasHL09} are only
+able to pass the {\it Crush} battery, which is very easy compared to the {\it Big Crush} one.
+
+Lastly, Cuda  has developed  a  library for  the  generation of  pseudorandom numbers  called
+Curand~\cite{curand11}.        Several       PRNGs        are       implemented, among
+other things 
+Xorwow~\cite{Marsaglia2003} and  some variants of Sobol. The  tests reported show that
+their  fastest version provides  15GSamples/s on  the new  Fermi C2050  card. 
+But their PRNGs cannot pass the whole TestU01 battery (only one test is failed).
+\newline
+\newline
+We can finally remark that, to the best of our knowledge, no GPU implementation have been proven to be chaotic, and the cryptographically secure property is surprisingly never regarded.

 
 \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.
-\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: $\llbracket1;N\rrbracket=\{1,2,\hdots,N\}$.
+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 $V$. $f^{k}=f\circ ...\circ f$
+is for 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}
 
 
 \end{definition}
 
 

-\begin{definition}
-$f$ is said to be \emph{chaotic} on $(\mathcal{X},\tau)$ if $f$ is regular and topologically transitive.
+\begin{definition}[Devaney's formulation of chaos~\cite{Devaney}]
+$f$ 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.

 
 
 
 
 
 

-\subsection{Chaotic iterations}
+\subsection{Chaotic Iterations}

 \label{sec:chaotic iterations}
 
 
 \label{sec:chaotic iterations}
 
 


@@ -103,23 +283,23 @@ 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
  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 $\mathbb{S}.$
+denoted by $\llbracket 1, \mathsf{N} \rrbracket^\mathds{N}.$

 
 \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
 
 \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
+a  function  and  $S\in  \llbracket 1, \mathsf{N} \rrbracket^\mathds{N}$  be  a  ``strategy''.  The  so-called

 \emph{chaotic      iterations}     are     defined      by     $x^0\in
 \mathds{B}^{\mathsf{N}}$ and
 \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


@@ -129,49 +309,59 @@ $\left(f(x^{n-1})\right)_{S^{n}}$      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
 $\left(f(x^{k})\right)_{S^{n}}$, 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.
+priori} no link with the mathematical theory of chaos, presented 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 $\sigma
+(S^{n})_{n\in \mathds{N}}\in \llbracket 1, \mathsf{N} \rrbracket^\mathds{N}\longrightarrow (S^{n+1})_{n\in
+\mathds{N}}\in \llbracket 1, \mathsf{N} \rrbracket^\mathds{N}$ and $i$ is the \emph{initial function} 
+$i:(S^{n})_{n\in \mathds{N}} \in \llbracket 1, \mathsf{N} \rrbracket^\mathds{N}\longrightarrow S^{0}\in \llbracket
+1;\mathsf{N}\rrbracket$. Then the chaotic iterations proposed in
+Definition \ref{Def: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*}%
-
-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
+\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:
 \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}%


@@ -180,62 +370,89 @@ 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 ($d_e$ is indeed the Hamming distance). 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 impact of this choice for a distance will be investigate at the end of the document.

 
 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 $\mathds{B}^\mathsf{N}$ 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 $f(x_1,\hdots, x_\mathsf{N}) =  (\overline{x_1},\hdots, \overline{x_\mathsf{N}})$ \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
+Let $f$ be a map from $\mathds{B}^\mathsf{N}$ to itself. The

 {\emph{asynchronous iteration graph}} associated with $f$ is the
 directed graph $\Gamma(f)$ defined by: the set of vertices is
 {\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 $i\in \llbracket1;n\rrbracket$,
+$\mathds{B}^\mathsf{N}$; for all $x\in\mathds{B}^\mathsf{N}$ and 
+$i\in \llbracket1;\mathsf{N}\rrbracket$,

 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 $x'$.
 
 the graph $\Gamma(f)$ contains an arc from $x$ to $F_f(i,x)$. 
 The relation between $\Gamma(f)$ and $G_f$ is clear: there exists a
 path from $x$ to $x'$ in $\Gamma(f)$ if and only if there exists a
 strategy $s$ such that the parallel iteration of $G_f$ from the
 initial point $(s,x)$ reaches the point $x'$.
 

-We have finally proven in \cite{FCT11} that,
+We have finally proven in \cite{bcgr11:ip} that,

 
 
 \begin{theorem}
 \label{Th:Caractérisation   des   IC   chaotiques}  
 
 
 \begin{theorem}
 \label{Th:Caractérisation   des   IC   chaotiques}  

-Let $f:\mathds{B}^n\to\mathds{B}^n$. $G_f$ is chaotic  (according to  Devaney) 
+Let $f:\mathds{B}^\mathsf{N}\to\mathds{B}^\mathsf{N}$. $G_f$ 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
+pseudorandom number generator (PRNG) based on the chaotic iterations. 
+As $G_f$, defined on the domain   $\llbracket 1 ;  \mathsf{N} \rrbracket^{\mathds{N}} 
+\times \mathds{B}^\mathsf{N}$, is build from Boolean networks $f : \mathds{B}^\mathsf{N}
+\rightarrow \mathds{B}^\mathsf{N}$, we can preserve the theoretical properties on $G_f$
+during implementations (due to the discrete nature of $f$). It is as if
+$\mathds{B}^\mathsf{N}$ represents the memory of the computer whereas $\llbracket 1 ;  \mathsf{N}
+\rrbracket^{\mathds{N}}$ is its input stream (the seeds, for instance, in PRNG, or a physical noise in TRNG).
+
+\section{Application to Pseudorandomness}
+\label{sec:pseudorandom}

 
 

-\section{Application to Pseudo-Randomness}
+\subsection{A First Pseudorandom 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 +464,6 @@ 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)}
 $z\leftarrow{z\oplus{(z\ll13)}}$\;


@@ -265,16 +481,21 @@ return $y$\;
 
 
 This generator is synthesized in Algorithm~\ref{CI 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 takes as input: a Boolean function $f$ satisfying Theorem~\ref{Th:Caractérisation   des   IC   chaotiques};
+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
 It returns the new generated configuration $x$.  Internally, it embeds two

-\textit{XORshift}$(k)$ PRNGs \cite{Marsaglia2003} that returns integers uniformly distributed
+\textit{XORshift}$(k)$ PRNGs~\cite{Marsaglia2003} that returns integers
+uniformly distributed

 into $\llbracket 1 ; k \rrbracket$.
 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.
+\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,
-
+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
 \begin{theorem}
   Let $f: \mathds{B}^{n} \rightarrow \mathds{B}^{n}$, $\Gamma(f)$ its
   iteration graph, $\check{M}$ its adjacency


@@ -285,534 +506,890 @@ We have proven in \cite{FCT11} that,
   if and only if $M$ is a double stochastic matrix.
 \end{theorem} 
 
   if and only if $M$ is a double stochastic matrix.
 \end{theorem} 
 

+This former generator as successively passed various batteries of statistical tests, as the NIST~\cite{bcgr11:ip}, DieHARD~\cite{Marsaglia1996}, and TestU01~\cite{LEcuyerS07}.

 
 

+\subsection{Improving the Speed of the Former Generator}

 
 

-\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}
-
+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:

 
 

-\section{The relativity of disorder}
-\label{sec:de la relativité du désordre}
-
-\subsection{Impact of the topology's finenesse}
-
-Let us firstly introduce the following notations.
+\begin{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.
+\label{eq:generalIC}
+\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 pseudorandom numbers 
+faster, does not deflate their topological chaos properties.
+
+\subsection{Proofs of Chaos of the General Formulation of the Chaotic Iterations}
+\label{deuxième def}
+Let us consider the discrete dynamical systems in chaotic iterations having 
+the general form:

 
 

-\begin{notation}
-$\mathcal{X}_\tau$ will denote the topological space $\left(\mathcal{X},\tau\right)$, whereas $\mathcal{V}_\tau (x)$ will be the set of all the neighborhoods of $x$ when considering the topology $\tau$ (or simply $\mathcal{V} (x)$, if there is no ambiguity).
-\end{notation}
+\begin{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.
+\label{general CIs}
+\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.

 
 

-\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 us introduce the following function:
+\begin{equation}
+\begin{array}{cccc}
+ \chi: & \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, $Y \in \mathcal{P}\left(X\right) \Longleftrightarrow Y \subset X$.

 
 

-If $(\mathcal{X}_{\tau'},f)$ is chaotic according to Devaney, then $(\mathcal{X}_\tau,f)$ is chaotic too.
-\end{theorem}
+Given a function $f:\mathds{B}^\mathsf{N} \longrightarrow \mathds{B}^\mathsf{N} $, define the function:
+\begin{equation}
+\begin{array}{lrll}
+F_{f}: & \mathcal{P}\left(\llbracket1;\mathsf{N}\rrbracket \right) \times \mathds{B}^{\mathsf{N}} &
+\longrightarrow & \mathds{B}^{\mathsf{N}} \\
+& (P,E) & \longmapsto & \left( E_{j}.\chi (j,P)+f(E)_{j}.\overline{\chi
+(j,P)}\right) _{j\in \llbracket1;\mathsf{N}\rrbracket},%
+\end{array}%
+\end{equation}%
+where + and . are the Boolean addition and product operations, and $\overline{x}$ 
+is the negation of the Boolean $x$.
+Consider the phase space:
+\begin{equation}
+\mathcal{X} = \mathcal{P}\left(\llbracket 1 ; \mathsf{N} \rrbracket\right)^\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 $\sigma
+(S^{n})_{n\in \mathds{N}}\in \mathcal{P}\left(\llbracket 1 ; \mathsf{N} \rrbracket\right)^\mathds{N}\longrightarrow (S^{n+1})_{n\in
+\mathds{N}}\in \mathcal{P}\left(\llbracket 1 ; \mathsf{N} \rrbracket\right)^\mathds{N}$ and $i$ is the \emph{initial function} 
+$i:(S^{n})_{n\in \mathds{N}} \in \mathcal{P}\left(\llbracket 1 ; \mathsf{N} \rrbracket\right)^\mathds{N}\longrightarrow S^{0}\in \mathcal{P}\left(\llbracket 1 ; \mathsf{N} \rrbracket\right)$. 
+Then the general chaotic iterations defined in Equation \ref{general CIs} can 
+be described by the following discrete dynamical system:
+\begin{equation}
+\left\{
+\begin{array}{l}
+X^0 \in \mathcal{X} \\
+X^{k+1}=G_{f}(X^k).%
+\end{array}%
+\right.
+\end{equation}%

 
 

-\begin{proof}
-Let us firstly establish the transitivity of $(\mathcal{X}_\tau,f)$.
+Another time, a shift function appears as a component of these general chaotic 
+iterations. 

 
 

-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.
+To study the Devaney's chaos property, a distance between two points 
+$X = (S,E), Y = (\check{S},\check{E})$ of $\mathcal{X}$ must be defined.
+Let us introduce:
+\begin{equation}
+d(X,Y)=d_{e}(E,\check{E})+d_{s}(S,\check{S}),
+\label{nouveau d}
+\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})}\textrm{ is another time the Hamming distance}, \\
+\displaystyle{d_{s}(S,\check{S})} & = & \displaystyle{\dfrac{9}{\mathsf{N}}%
+\sum_{k=1}^{\infty }\dfrac{|S^k\Delta {S}^k|}{10^{k}}}.%
+\end{array}%
+\right.
+\end{equation}
+where $|X|$ is the cardinality of a set $X$ and $A\Delta B$ is for the symmetric difference, defined for sets A, B as
+$A\,\Delta\,B = (A \setminus B) \cup (B \setminus A)$.

 
 

-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, $\exists \omega \in \tau, x \in \omega \subset V$.
+\begin{proposition}
+The function $d$ defined in Eq.~\ref{nouveau d} is a metric on $\mathcal{X}$.
+\end{proposition}

 
 

-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.
+\begin{proof}
+ $d_e$ is the Hamming distance. We will prove that $d_s$ is a distance
+too, thus $d$ will be a distance as sum of two distances.
+ \begin{itemize}
+\item Obviously, $d_s(S,\check{S})\geqslant 0$, and if $S=\check{S}$, then 
+$d_s(S,\check{S})=0$. Conversely, if $d_s(S,\check{S})=0$, then 
+$\forall k \in \mathds{N}, |S^k\Delta {S}^k|=0$, and so $\forall k, S^k=\check{S}^k$.
+ \item $d_s$ is symmetric 
+($d_s(S,\check{S})=d_s(\check{S},S)$) due to the commutative property
+of the symmetric difference. 
+\item Finally, $|S \Delta S''| = |(S \Delta \varnothing) \Delta S''|= |S \Delta (S'\Delta S') \Delta S''|= |(S \Delta S') \Delta (S' \Delta S'')|\leqslant |S \Delta S'| + |S' \Delta S''|$, 
+and so for all subsets $S,S',$ and $S''$ of $\llbracket 1, \mathsf{N} \rrbracket$, 
+we have $d_s(S,S'') \leqslant d_e(S,S')+d_s(S',S'')$, and the triangle
+inequality is obtained.
+ \end{itemize}

 \end{proof}
 
 \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):
+Before being able to study the topological behavior of the general 
+chaotic iterations, we must firstly establish that:

 
 

-\begin{theorem}
-Let $\mathcal{X}$ a nonempty set and $f: \mathcal{X} \to \X$ a function having at least a fixed point.
-Then $f$ is $\tau_0-$chaotic, where $\tau_0$ is the trivial (indiscrete) topology on $\X$.
-\end{theorem}
+\begin{proposition}
+ For all $f:\mathds{B}^\mathsf{N} \longrightarrow \mathds{B}^\mathsf{N} $, the function $G_f$ is continuous on 
+$\left( \mathcal{X},d\right)$.
+\end{proposition}

 
 
 \begin{proof}
 
 
 \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.
+We use the sequential continuity.
+Let $(S^n,E^n)_{n\in \mathds{N}}$ be a sequence of the phase space $%
+\mathcal{X}$, which converges to $(S,E)$. We will prove that $\left(
+G_{f}(S^n,E^n)\right) _{n\in \mathds{N}}$ converges to $\left(
+G_{f}(S,E)\right) $. Let us remark that for all $n$, $S^n$ is a strategy,
+thus, we consider a sequence of strategies (\emph{i.e.}, a sequence of
+sequences).\newline
+As $d((S^n,E^n);(S,E))$ converges to 0, each distance $d_{e}(E^n,E)$ and $d_{s}(S^n,S)$ converges
+to 0. But $d_{e}(E^n,E)$ is an integer, so $\exists n_{0}\in \mathds{N},$ $%
+d_{e}(E^n,E)=0$ for any $n\geqslant n_{0}$.\newline
+In other words, there exists a threshold $n_{0}\in \mathds{N}$ after which no
+cell will change its state:
+$\exists n_{0}\in \mathds{N},n\geqslant n_{0}\Rightarrow E^n = E.$
+
+In addition, $d_{s}(S^n,S)\longrightarrow 0,$ so $\exists n_{1}\in %
+\mathds{N},d_{s}(S^n,S)<10^{-1}$ for all indexes greater than or equal to $%
+n_{1}$. This means that for $n\geqslant n_{1}$, all the $S^n$ have the same
+first term, which is $S^0$: $\forall n\geqslant n_{1},S_0^n=S_0.$
+
+Thus, after the $max(n_{0},n_{1})^{th}$ term, states of $E^n$ and $E$ are
+identical and strategies $S^n$ and $S$ start with the same first term.\newline
+Consequently, states of $G_{f}(S^n,E^n)$ and $G_{f}(S,E)$ are equal,
+so, after the $max(n_0, n_1)^{th}$ term, the distance $d$ between these two points is strictly less than 1.\newline
+\noindent We now prove that the distance between $\left(
+G_{f}(S^n,E^n)\right) $ and $\left( G_{f}(S,E)\right) $ is convergent to
+0. Let $\varepsilon >0$. \medskip
+\begin{itemize}
+\item If $\varepsilon \geqslant 1$, we see that distance
+between $\left( G_{f}(S^n,E^n)\right) $ and $\left( G_{f}(S,E)\right) $ is
+strictly less than 1 after the $max(n_{0},n_{1})^{th}$ term (same state).
+\medskip
+\item If $\varepsilon <1$, then $\exists k\in \mathds{N},10^{-k}\geqslant
+\varepsilon > 10^{-(k+1)}$. But $d_{s}(S^n,S)$ converges to 0, so
+\begin{equation*}
+\exists n_{2}\in \mathds{N},\forall n\geqslant
+n_{2},d_{s}(S^n,S)<10^{-(k+2)},
+\end{equation*}%
+thus after $n_{2}$, the $k+2$ first terms of $S^n$ and $S$ are equal.
+\end{itemize}
+\noindent As a consequence, the $k+1$ first entries of the strategies of $%
+G_{f}(S^n,E^n)$ and $G_{f}(S,E)$ are the same ($G_{f}$ is a shift of strategies) and due to the definition of $d_{s}$, the floating part of
+the distance between $(S^n,E^n)$ and $(S,E)$ is strictly less than $%
+10^{-(k+1)}\leqslant \varepsilon $.\bigskip \newline
+In conclusion,
+$$
+\forall \varepsilon >0,\exists N_{0}=max(n_{0},n_{1},n_{2})\in \mathds{N}%
+,\forall n\geqslant N_{0},
+ d\left( G_{f}(S^n,E^n);G_{f}(S,E)\right)
+\leqslant \varepsilon .
+$$
+$G_{f}$ is consequently continuous.

 \end{proof}
 
 
 \end{proof}
 
 

-
-
-\subsection{A given system can always be claimed as non-chaotic}
+It is now possible to study the topological behavior of the general chaotic
+iterations. We will prove that,

 
 \begin{theorem}
 
 \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.
+\label{t:chaos des general}
+ The general chaotic iterations defined on Equation~\ref{general CIs} satisfy
+the Devaney's property of chaos.

 \end{theorem}
 
 \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 firstly prove the following lemma.

 
 

-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 $f$.
+\begin{lemma}[Strong transitivity]
+\label{strongTrans}
+ For all couples $X,Y \in \mathcal{X}$ and any neighborhood $V$ of $X$, we can 
+find $n \in \mathds{N}^*$ and $X' \in V$ such that $G^n(X')=Y$.
+\end{lemma}

 
 

-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 $y \notin I_x \Rightarrow \forall n, f^{(n)}\left( \{x\} \right) \cap \{y\} = \varnothing$.
+\begin{proof}
+ Let $X=(S,E)$, $\varepsilon>0$, and $k_0 = \lfloor log_{10}(\varepsilon)+1 \rfloor$. 
+Any point $X'=(S',E')$ such that $E'=E$ and $\forall k \leqslant k_0, S'^k=S^k$, 
+are in the open ball $\mathcal{B}\left(X,\varepsilon\right)$. Let us define 
+$\check{X} = \left(\check{S},\check{E}\right)$, where $\check{X}= G^{k_0}(X)$.
+We denote by $s\subset \llbracket 1; \mathsf{N} \rrbracket$ the set of coordinates
+that are different between $\check{E}$ and the state of $Y$. Thus each point $X'$ of
+the form $(S',E')$ where $E'=E$ and $S'$ starts with 
+$(S^0, S^1, \hdots, S^{k_0},s,\hdots)$, verifies the following properties:
+\begin{itemize}
+ \item $X'$ is in $\mathcal{B}\left(X,\varepsilon\right)$,
+ \item the state of $G_f^{k_0+1}(X')$ is the state of $Y$.
+\end{itemize}
+Finally the point $\left(\left(S^0, S^1, \hdots, S^{k_0},s,s^0, s^1, \hdots\right); E\right)$, 
+where $(s^0,s^1, \hdots)$ is the strategy of $Y$, satisfies the properties
+claimed in the lemma.

 \end{proof}
 
 \end{proof}
 

+We can now prove the Theorem~\ref{t:chaos des general}...
+
+\begin{proof}[Theorem~\ref{t:chaos des general}]
+Firstly, strong transitivity implies transitivity.
+
+Let $(S,E) \in\mathcal{X}$ and $\varepsilon >0$. To
+prove that $G_f$ is regular, it is sufficient to prove that
+there exists a strategy $\tilde S$ such that the distance between
+$(\tilde S,E)$ and $(S,E)$ is less than $\varepsilon$, and such that
+$(\tilde S,E)$ is a periodic point.
+
+Let $t_1=\lfloor-\log_{10}(\varepsilon)\rfloor$, and let $E'$ be the
+configuration that we obtain from $(S,E)$ after $t_1$ iterations of
+$G_f$. As $G_f$ is strongly transitive, there exists a strategy $S'$ 
+and $t_2\in\mathds{N}$ such
+that $E$ is reached from $(S',E')$ after $t_2$ iterations of $G_f$.
+
+Consider the strategy $\tilde S$ that alternates the first $t_1$ terms
+of $S$ and the first $t_2$ terms of $S'$: $$\tilde
+S=(S_0,\dots,S_{t_1-1},S'_0,\dots,S'_{t_2-1},S_0,\dots,S_{t_1-1},S'_0,\dots,S'_{t_2-1},S_0,\dots).$$ It
+is clear that $(\tilde S,E)$ is obtained from $(\tilde S,E)$ after
+$t_1+t_2$ iterations of $G_f$. So $(\tilde S,E)$ is a periodic
+point. Since $\tilde S_t=S_t$ for $t<t_1$, by the choice of $t_1$, we
+have $d((S,E),(\tilde S,E))<\epsilon$.
+\end{proof}

 
 
 
 
 
 

+\section{Efficient PRNG based on Chaotic Iterations}
+\label{sec:efficient PRNG}

 
 

+Based on the proof presented in the previous section, it is now possible to 
+improve the speed of the generator formerly presented in~\cite{bgw09:ip,guyeux10}. 
+The first idea is to consider
+that the provided strategy is a pseudorandom Boolean vector obtained by a
+given PRNG.
+An iteration of the system is simply the bitwise exclusive or between
+the last computed state and the current strategy.
+Topological properties of disorder exhibited by chaotic 
+iterations can be inherited by the inputted generator, hoping by doing so to 
+obtain some statistical improvements while preserving speed.

 
 

-\section{Chaos on the order topology}
-
-\subsection{The phase space is an interval of the real line}
-
-\subsubsection{Toward a topological semiconjugacy}
-
-In what follows, our intention is to establish, by using a topological semiconjugacy, that chaotic iterations over $\mathcal{X}$ can be described as iterations on a real interval. To do so, we must firstly introduce some notations and terminologies. 
-
-Let $\mathcal{S}_\mathsf{N}$ be the set of sequences belonging into $\llbracket 1; \mathsf{N}\rrbracket$ and $\mathcal{X}_{\mathsf{N}} = \mathcal{S}_\mathsf{N} \times \B^\mathsf{N}$.
-
-
-\begin{definition}
-The function $\varphi: \mathcal{S}_{10} \times\mathds{B}^{10} \rightarrow \big[ 0, 2^{10} \big[$ is defined by:
-$$
-\begin{array}{cccl}
-\varphi: & \mathcal{X}_{10} = \mathcal{S}_{10} \times\mathds{B}^{10}& \longrightarrow & \big[ 0, 2^{10} \big[ \\
- & (S,E) = \left((S^0, S^1, \hdots ); (E_0, \hdots, E_9)\right) & \longmapsto & \varphi \left((S,E)\right)
-\end{array}
-$$
-\noindent where $\varphi\left((S,E)\right)$ is the real number:
-\begin{itemize}
-\item whose integral part $e$ is $\displaystyle{\sum_{k=0}^9 2^{9-k} E_k}$, that is, the binary digits of $e$ are $E_0 ~ E_1 ~ \hdots ~ E_9$.
-\item whose decimal part $s$ is equal to $s = 0,S^0~ S^1~ S^2~ \hdots = \sum_{k=1}^{+\infty} 10^{-k} S^{k-1}.$ 
-\end{itemize}
-\end{definition}
-
-
-
-$\varphi$ realizes the association between a point of $\mathcal{X}_{10}$ and a real number into $\big[ 0, 2^{10} \big[$. We must now translate the chaotic iterations $\Go$ on this real interval. To do so, two intermediate functions over $\big[ 0, 2^{10} \big[$ must be introduced:

 
 

+Let us give an example using 16-bits numbers, to clearly understand how the bitwise xor operations
+are
+done.  
+Suppose  that $x$ and the  strategy $S^i$ are given as
+binary vectors.
+Table~\ref{TableExemple} shows the result of $x \oplus S^i$.

 
 

-\begin{definition}
-\label{def:e et s}
-Let $x \in \big[ 0, 2^{10} \big[$ and:
-\begin{itemize}
-\item $e_0, \hdots, e_9$ the binary digits of the integral part of $x$: $\displaystyle{\lfloor x \rfloor = \sum_{k=0}^{9} 2^{9-k} e_k}$.
-\item $(s^k)_{k\in \mathds{N}}$ the digits of $x$, where the chosen decimal decomposition of $x$ is the one that does not have an infinite number of 9: 
-$\displaystyle{x = \lfloor x \rfloor + \sum_{k=0}^{+\infty} s^k 10^{-k-1}}$.
-\end{itemize}
-$e$ and $s$ are thus defined as follows:
-$$
-\begin{array}{cccl}
-e: & \big[ 0, 2^{10} \big[ & \longrightarrow & \mathds{B}^{10} \\
- & x & \longmapsto & (e_0, \hdots, e_9)
-\end{array}
+\begin{table}

 $$
 $$

-\noindent and
-$$
-\begin{array}{cccl}
-s: & \big[ 0, 2^{10} \big[ & \longrightarrow & \llbracket 0, 9 \rrbracket^{\mathds{N}} \\
- & x & \longmapsto & (s^k)_{k \in \mathds{N}}
-\end{array}
-$$
-\end{definition}
-
-We are now able to define the function $g$, whose goal is to translate the chaotic iterations $\Go$ on an interval of $\mathds{R}$.
+\begin{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

 
 

-\begin{definition}
-$g:\big[ 0, 2^{10} \big[ \longrightarrow \big[ 0, 2^{10} \big[$ is defined by:
-$$
-\begin{array}{cccl}
-g: & \big[ 0, 2^{10} \big[ & \longrightarrow & \big[ 0, 2^{10} \big[ \\
-& \\
- & x & \longmapsto & g(x)
-\end{array}
-$$
-\noindent where g(x) is the real number of $\big[ 0, 2^{10} \big[$ defined bellow:
-\begin{itemize}
-\item its integral part has a binary decomposition equal to $e_0', \hdots, e_9'$, with:
-$$
-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.
+\hline
+ \end{array}

 $$
 $$

-\item whose decimal part is $s(x)^1, s(x)^2, \hdots$
-\end{itemize}
-\end{definition}
-
-\bigskip
-
+\caption{Example of an arbitrary round of the proposed generator}
+\label{TableExemple}
+\end{table}

 
 

-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: $$g(x) = \displaystyle{\sum_{k=0}^{9} 2^{9-k} (e_k + \delta(k,s^0) \textrm{ (mod 2)}) +  \sum_{k=0}^{+\infty} s^{k+1} 10^{-k-1}}.$$

 
 

-\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:
+\lstset{language=C,caption={C code of the sequential PRNG based on chaotic iterations},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}

 
 

-\begin{notation}
-\index{distance!euclidienne}
-$\Delta$ is the Euclidian distance on $\big[ 0, 2^{10} \big[$, that is, $\Delta(x,y) = |y-x|^2$.
-\end{notation}

 
 

-\medskip

 
 

-This Euclidian distance does not reproduce exactly the notion of proximity induced by our first distance $d$ on $\X$. Indeed $d$ is finer than $\Delta$. This is the reason why we have to introduce the following metric:

 
 
 
 

+In Listing~\ref{algo:seqCIPRNG}  a sequential version of  the proposed PRNG based on chaotic iterations
+ is  presented.  The xor operator is  represented by \textasciicircum.
+This  function uses  three classical  64-bits PRNGs, namely the  \texttt{xorshift}, the
+\texttt{xor128},  and  the  \texttt{xorwow}~\cite{Marsaglia2003}.   In  the following,  we  call  them
+``xor-like PRNGs''. 
+As
+each xor-like PRNG  uses 64-bits whereas our proposed generator works with 32-bits,
+we use the command \texttt{(unsigned int)}, that selects the 32 least significant bits of a given integer, and the code
+\texttt{(unsigned int)(t3$>>$32)}  in order to obtain the 32 most significant  bits of \texttt{t}.   

 
 

-\begin{definition}
-Let $x,y \in \big[ 0, 2^{10} \big[$.
-$D$ denotes the function from $\big[ 0, 2^{10} \big[^2$ to $\mathds{R}^+$ defined by: $D(x,y) = D_e\left(e(x),e(y)\right) + D_s\left(s(x),s(y)\right)$, where:
-\begin{center}
-$\displaystyle{D_e(E,\check{E}) = \sum_{k=0}^\mathsf{9} \delta (E_k, \check{E}_k)}$, ~~and~ $\displaystyle{D_s(S,\check{S}) = \sum_{k = 1}^\infty \dfrac{|S^k-\check{S}^k|}{10^k}}$.
-\end{center}
-\end{definition}
+So producing a  pseudorandom number needs  6 xor operations
+with 6 32-bits  numbers that are provided by 3 64-bits PRNGs.   This version successfully passes the
+stringent BigCrush battery of tests~\cite{LEcuyerS07}.

 
 

-\begin{proposition}
-$D$ is a distance on $\big[ 0, 2^{10} \big[$.
-\end{proposition}
+\section{Efficient PRNGs based on Chaotic Iterations on GPU}
+\label{sec:efficient PRNG gpu}

 
 

-\begin{proof}
-The three axioms defining a distance must be checked.
-\begin{itemize}
-\item $D \geqslant 0$, because everything is positive in its definition. If $D(x,y)=0$, then $D_e(x,y)=0$, so the integral parts of $x$ and $y$ are equal (they have the same binary decomposition). Additionally, $D_s(x,y) = 0$, then $\forall k \in \mathds{N}^*, s(x)^k = s(y)^k$. In other words, $x$ and $y$ have the same $k-$th decimal digit, $\forall k \in \mathds{N}^*$. And so $x=y$.
-\item $D(x,y)=D(y,x)$.
-\item Finally, the triangular inequality is obtained due to the fact that both $\delta$ and $\Delta(x,y)=|x-y|$ satisfy it.
-\end{itemize}
-\end{proof}
+In order to  take benefits from the computing power  of GPU, a program
+needs  to have  independent blocks  of  threads that  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 the  performances  on  GPU  is.
+Obviously, having these requirements in  mind, it is possible to build
+a   program    similar   to    the   one   presented    in   Algorithm
+\ref{algo:seqCIPRNG}, which computes  pseudorandom numbers on GPU.  To
+do  so,  we  must   firstly  recall  that  in  the  CUDA~\cite{Nvid10}
+environment,    threads    have     a    local    identifier    called
+\texttt{ThreadIdx},  which   is  relative  to   the  block  containing
+them. With  CUDA parts of  the code which  are executed by the  GPU are
+called {\it kernels}.

 
 
 
 

-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.
+\subsection{Naive Version for GPU}

 
 

+ 
+It is possible to deduce from the CPU version a quite similar version adapted to GPU.
+The simple principle consists to make each thread of the GPU computing the CPU version of our PRNG.  
+Of course,  the  three xor-like
+PRNGs  used in these computations must have different  parameters. 
+In a given thread, these lasts are
+randomly picked from another PRNGs. 
+The  initialization stage is performed by  the CPU.
+To do it, the  ISAAC  PRNG~\cite{Jenkins96} is used to  set  all  the
+parameters embedded into each thread.   
+
+The implementation of  the three
+xor-like  PRNGs  is  straightforward  when  their  parameters  have  been
+allocated in  the GPU memory.  Each xor-like  works with  an internal
+number  $x$  that saves  the  last  generated  pseudorandom number. Additionally,  the
+implementation of the  xor128, the xorshift, and the  xorwow respectively require
+4, 5, and 6 unsigned long as internal variables.

 
 

-\begin{figure}[t]
-\begin{center}
-  \subfigure[Function $x \to dist(x;1,234) $ on the interval $(0;5)$.]{\includegraphics[scale=.35]{DvsEuclidien.pdf}}\quad
-  \subfigure[Function $x \to dist(x;3) $ on the interval $(0;5)$.]{\includegraphics[scale=.35]{DvsEuclidien2.pdf}}
-\end{center}
-\caption{Comparison between $D$ (in blue) and the Euclidian distane (in green).}
-\label{fig:comparaison de distances}
-\end{figure}
+\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 of the GPU ``naive'' version of the PRNG based on chaotic iterations}
+\label{algo:gpu_kernel}
+\end{algorithm}

 
 

+Algorithm~\ref{algo:gpu_kernel}  presents a naive  implementation of the proposed  PRNG on
+GPU.  Due 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  (\emph{i.e.},    the    variable   \texttt{n}   in
+algorithm~\ref{algo:gpu_kernel}). For instance, 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 all of the  internals   variables  of both the  xor-like
+PRNGs\footnote{we multiply this number by $2$ in order to count 32-bits numbers}
+and  the pseudorandom  numbers generated by  our  PRNG,  is  equal to  $100,000\times  ((4+5+6)\times
+2+(1+100))=1,310,000$ 32-bits numbers, that is, approximately $52$Mb.

 
 

-\subsubsection{The semiconjugacy}
+This generator is able to pass the whole BigCrush battery of tests, for all
+the versions that have been tested depending on their number of threads 
+(called \texttt{NumThreads} in our algorithm, tested until $10$ millions).

 
 

-It is now possible to define a topological semiconjugacy between $\mathcal{X}$ and an interval of $\mathds{R}$:
+\begin{remark}
+The proposed algorithm has  the  advantage to  manipulate  independent
+PRNGs, so this version is easily adaptable on a cluster of computers too. The only thing
+to ensure is to use a single ISAAC PRNG. To achieve this requirement, a simple solution consists in
+using a master node for the initialization. This master node computes the initial parameters
+for all the differents nodes involves in the computation.
+\end{remark}

 
 

-\begin{theorem}
-Chaotic iterations on the phase space $\mathcal{X}$ are simple iterations on $\mathds{R}$, which is illustrated by the semiconjugacy of the diagram bellow:
-\begin{equation*}
-\begin{CD}
-\left(~\mathcal{S}_{10} \times\mathds{B}^{10}, d~\right) @>G_{f_0}>> \left(~\mathcal{S}_{10} \times\mathds{B}^{10}, d~\right)\\
-    @V{\varphi}VV                    @VV{\varphi}V\\
-\left( ~\big[ 0, 2^{10} \big[, D~\right)  @>>g> \left(~\big[ 0, 2^{10} \big[, D~\right)
-\end{CD}
-\end{equation*}
-\end{theorem}
+\subsection{Improved Version for GPU}

 
 

-\begin{proof}
-$\varphi$ has been constructed in order to be continuous and onto.
-\end{proof}
+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., to use less  than 3 xor-like PRNGs. The solution  consists in computing only
+one xor-like PRNG by thread, saving  it into the shared memory, and then to use 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 combination array that
+contains  the indexes  of  all threads  and  for which  a combination has  been
+performed. 

 
 

-In other words, $\mathcal{X}$ is approximately equal to $\big[ 0, 2^\mathsf{N} \big[$.
+In Algorithm~\ref{algo:gpu_kernel2}, two combination arrays are used.
+The    variable   \texttt{offset}    is    computed   using    the   value    of
+\texttt{combination\_size}.   Then we  can compute  \texttt{o1}  and \texttt{o2}
+representing the indexes of the  other threads whose results are used
+by the  current one. In  this algorithm, we  consider that a  64-bits xor-like
+PRNG has been chosen, and so its two 32-bits parts are used.

 
 

+This version also can pass the whole {\it BigCrush} battery 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 combinations of size combination\_size\;}

 
 

+\KwOut{NewNb: array containing random numbers in global memory}
+\If{threadId is concerned} {
+  retrieve data from InternalVarXorLikeArray[threadId] in local variables including shared memory and x\;
+  offset = threadIdx\%combination\_size\;
+  o1 = threadIdx-offset+tab1[offset]\;
+  o2 = threadIdx-offset+tab2[offset]\;
+  \For{i=1 to n} {
+    t=xor-like()\;
+    t=t $\hat{ }$ shmem[o1] $\hat{ }$ shmem[o2]\;
+    shared\_mem[threadId]=t\;
+    x = x $\hat{ }$ t\;

 
 

+    store the new PRNG in NewNb[NumThreads*threadId+i]\;
+  }
+  store internal variables in InternalVarXorLikeArray[threadId]\;
+}

 
 

-\subsection{Study of the chaotic iterations described as a real function}
+\caption{main kernel for the chaotic iterations based PRNG GPU efficient
+version}
+\label{algo:gpu_kernel2}
+\end{algorithm}

 
 

+\subsection{Theoretical Evaluation of the Improved Version}
+
+A run of Algorithm~\ref{algo:gpu_kernel2} consists in an operation ($x=x\oplus t$) having 
+the form of Equation~\ref{equation Oplus}, which is equivalent to the iterative
+system of Eq.~\ref{eq:generalIC}. That is, an iteration of the general chaotic
+iterations is realized between the last stored value $x$ of the thread and a strategy $t$
+(obtained by a bitwise exclusive or between a value provided by a xor-like() call
+and two values previously obtained by two other threads).
+To be certain that we are in the framework of Theorem~\ref{t:chaos des general},
+we must guarantee that this dynamical system iterates on the space 
+$\mathcal{X} = \mathcal{P}\left(\llbracket 1, \mathsf{N} \rrbracket\right)^\mathds{N}\times\mathds{B}^\mathsf{N}$.
+The left term $x$ obviously belongs into $\mathds{B}^ \mathsf{N}$.
+To prevent from any flaws of chaotic properties, we must check that the right 
+term (the last $t$), corresponding to the strategies,  can possibly be equal to any
+integer of $\llbracket 1, \mathsf{N} \rrbracket$. 
+
+Such a result is obvious, as for the xor-like(), all the
+integers belonging into its interval of definition can occur at each iteration, and thus the 
+last $t$ respects the requirement. Furthermore, it is possible to
+prove by an immediate mathematical induction that, as the initial $x$
+is uniformly distributed (it is provided by a cryptographically secure PRNG),
+the two other stored values shmem[o1] and shmem[o2] are uniformly distributed too,
+(this can be stated by an immediate mathematical
+induction), and thus the next $x$ is finally uniformly distributed.
+
+Thus Algorithm~\ref{algo:gpu_kernel2} is a concrete realization of the general
+chaotic iterations presented previously, and for this reason, it satisfies the 
+Devaney's formulation of a chaotic behavior.

 
 

-\begin{figure}[t]
+\section{Experiments}
+\label{sec:experiments}
+
+Different experiments  have been  performed in order  to measure  the generation
+speed. We have used a first computer equipped with a Tesla C1060 NVidia  GPU card
+and an
+Intel  Xeon E5530 cadenced  at 2.40  GHz,  and 
+a second computer  equipped with a smaller  CPU and  a GeForce GTX  280. 
+All the
+cards have 240 cores.
+
+In  Figure~\ref{fig:time_xorlike_gpu} we  compare the  quantity of  pseudorandom numbers
+generated per second with various xor-like based PRNG. In this figure, the optimized
+versions use the {\it xor64} described in~\cite{Marsaglia2003}, whereas the naive versions
+embed  the three  xor-like  PRNGs described  in Listing~\ref{algo:seqCIPRNG}.   In
+order to obtain the optimal performances, the storage of pseudorandom numbers
+into the GPU memory has been removed. This step is time consuming and slows down the numbers
+generation.  Moreover this   storage  is  completely
+useless, in case of applications that consume the pseudorandom
+numbers  directly   after generation. We can see  that when the number of  threads is greater
+than approximately 30,000 and lower than 5 millions, the number of pseudorandom numbers generated
+per second  is almost constant.  With the  naive version, this value ranges from 2.5 to
+3GSamples/s.   With  the  optimized   version,  it  is  approximately  equal to
+20GSamples/s. Finally  we can remark  that both GPU  cards are quite  similar, but in
+practice,  the Tesla C1060  has more  memory than  the GTX  280, and  this memory
+should be of better quality.
+As a  comparison,   Listing~\ref{algo:seqCIPRNG}  leads   to the  generation of  about
+138MSample/s when using one core of the Xeon E5530.
+
+\begin{figure}[htbp]

 \begin{center}
 \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}}
+  \includegraphics[scale=.7]{curve_time_xorlike_gpu.pdf}

 \end{center}
 \end{center}

-\caption{Representation of the chaotic iterations.}
-\label{fig:ICs}
+\caption{Quantity of pseudorandom numbers generated per second with the xorlike-based PRNG}
+\label{fig:time_xorlike_gpu}

 \end{figure}
 
 
 
 
 \end{figure}
 
 
 
 

-\begin{figure}[t]
-\begin{center}
-  \subfigure[ICs on the interval $(510;514)$.]{\includegraphics[scale=.35]{ICs510a514.pdf}}\quad
-  \subfigure[ICs on the interval $(1000;1008)$]{\includegraphics[scale=.35]{ICs1000a1008.pdf}}
-\end{center}
-\caption{ICs on small intervals.}
-\label{fig:ICs2}
-\end{figure}

 
 

-\begin{figure}[t]
+In Figure~\ref{fig:time_bbs_gpu}  we highlight the performances  of the optimized
+BBS-based  PRNG on GPU. On the  Tesla C1060 we
+obtain approximately 700MSample/s and on the GTX 280 about 670MSample/s, which is
+obviously slower than the xorlike-based PRNG on GPU. However, we will show in the 
+next sections that 
+this new PRNG has a strong level of security, which is necessary paid by a speed
+reduction. 
+
+\begin{figure}[htbp]

 \begin{center}
 \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
+  \includegraphics[scale=.7]{curve_time_bbs_gpu.pdf}

 \end{center}
 \end{center}

-\caption{General aspect of the chaotic iterations.}
-\label{fig:ICs3}
+\caption{Quantity of pseudorandom numbers generated per second using the BBS-based PRNG}
+\label{fig:time_bbs_gpu}

 \end{figure}
 
 \end{figure}
 

+All  these  experiments allow  us  to conclude  that  it  is possible  to
+generate a very large quantity of pseudorandom  numbers statistically perfect with the  xor-like version.
+In a certain extend, it is the case too with the secure BBS-based version, the speed deflation being
+explained by the fact that the former  version has ``only''
+chaotic properties and statistical perfection, whereas the latter is also cryptographically secure,
+as it is shown in the next sections.

 
 

-We have written a Python program to represent the chaotic iterations with the vectorial negation on the real line $\mathds{R}$. Various representations of these CIs are given in Figures \ref{fig:ICs}, \ref{fig:ICs2} and \ref{fig:ICs3}. It can be remarked that the function $g$ is a piecewise linear function: it is linear on each interval having the form $\left[ \dfrac{n}{10}, \dfrac{n+1}{10}\right[$, $n \in \llbracket 0;2^{10}\times 10 \rrbracket$ and its slope is equal to 10. Let us justify these claims:

 
 

-\begin{proposition}
-\label{Prop:derivabilite des ICs}
-Chaotic iterations $g$ defined on $\mathds{R}$ have derivatives of all orders on $\big[ 0, 2^{10} \big[$, except on the 10241 points in $I$ defined by $\left\{ \dfrac{n}{10} ~\big/~ n \in \llbracket 0;2^{10}\times 10\rrbracket \right\}$.

 
 

-Furthermore, on each interval of the form $\left[ \dfrac{n}{10}, \dfrac{n+1}{10}\right[$, with $n \in \llbracket 0;2^{10}\times 10 \rrbracket$, $g$ is a linear function, having a slope equal to 10: $\forall x \notin I, g'(x)=10$.
-\end{proposition}

 
 
 
 

-\begin{proof}
-Let $I_n = \left[ \dfrac{n}{10}, \dfrac{n+1}{10}\right[$, with $n \in \llbracket 0;2^{10}\times 10 \rrbracket$. All the points of $I_n$ have the same integral prat $e$ and the same decimal part $s^0$: on the set $I_n$,  functions $e(x)$ and $x \mapsto s(x)^0$ of Definition \ref{def:e et s} only depend on $n$. So all the images $g(x)$ of these points $x$:
-\begin{itemize}
-\item Have the same integral part, which is $e$, except probably the bit number $s^0$. In other words, this integer has approximately the same binary decomposition than $e$, the sole exception being the digit $s^0$ (this number is then either $e+2^{10-s^0}$ or $e-2^{10-s^0}$, depending on the parity of $s^0$, \emph{i.e.}, it is equal to $e+(-1)^{s^0}\times 2^{10-s^0}$).
-\item A shift to the left has been applied to the decimal part $y$, losing by doing so the common first digit $s^0$. In other words, $y$ has been mapped into $10\times y - s^0$.
-\end{itemize}
-To sum up, the action of $g$ on the points of $I$ is as follows: first, make a multiplication by 10, and second, add the same constant to each term, which is $\dfrac{1}{10}\left(e+(-1)^{s^0}\times 2^{10-s^0}\right)-s^0$.
-\end{proof}
-
-\begin{remark}
-Finally, chaotic iterations are elements of the large family of functions that are both chaotic and piecewise linear (like the tent map).
-\end{remark}

 
 
 
 

+\section{Security Analysis}
+\label{sec:security analysis}

 
 

-\subsection{Comparison of the two metrics on $\big[ 0, 2^\mathsf{N} \big[$}
-
-The two propositions bellow allow to compare our two distances on $\big[ 0, 2^\mathsf{N} \big[$:
-
-\begin{proposition}
-Id: $\left(~\big[ 0, 2^\mathsf{N} \big[,\Delta~\right) \to \left(~\big[ 0, 2^\mathsf{N} \big[, D~\right)$ is not continuous. 
-\end{proposition}
-
-\begin{proof}
-The sequence $x^n = 1,999\hdots 999$ constituted by $n$ 9 as decimal part, is such that:
-\begin{itemize}
-\item $\Delta (x^n,2) \to 0.$
-\item But $D(x^n,2) \geqslant 1$, then $D(x^n,2)$ does not converge to 0.
-\end{itemize}

 
 

-The sequential characterization of the continuity concludes the demonstration.
-\end{proof}

 
 

+In this section the concatenation of two strings $u$ and $v$ is classically
+denoted by $uv$.
+In a cryptographic context, a pseudorandom generator is a deterministic
+algorithm $G$ transforming strings  into strings and such that, for any
+seed $w$ of length $N$, $G(w)$ (the output of $G$ on the input $w$) has size
+$\ell_G(N)$ with $\ell_G(N)>N$.
+The notion of {\it secure} PRNGs can now be defined as follows. 

 
 

+\begin{definition}
+A cryptographic PRNG $G$ is secure if for any probabilistic polynomial time
+algorithm $D$, for any positive polynomial $p$, and for all sufficiently
+large $k$'s,
+$$| \mathrm{Pr}[D(G(U_k))=1]-Pr[D(U_{\ell_G(k)})=1]|< \frac{1}{p(N)},$$
+where $U_r$ is the uniform distribution over $\{0,1\}^r$ and the
+probabilities are taken over $U_N$, $U_{\ell_G(N)}$ as well as over the
+internal coin tosses of $D$. 
+\end{definition}

 
 

-A contrario:
+Intuitively, it means that there is no polynomial time algorithm that can
+distinguish a perfect uniform random generator from $G$ with a non
+negligible probability. The interested reader is referred
+to~\cite[chapter~3]{Goldreich} for more information. Note that it is
+quite easily possible to change the function $\ell$ into any polynomial
+function $\ell^\prime$ satisfying $\ell^\prime(N)>N)$~\cite[Chapter 3.3]{Goldreich}.
+
+The generation schema developed in (\ref{equation Oplus}) is based on a
+pseudorandom generator. Let $H$ be a cryptographic PRNG. We may assume,
+without loss of generality, that for any string $S_0$ of size $N$, the size
+of $H(S_0)$ is $kN$, with $k>2$. It means that $\ell_H(N)=kN$. 
+Let $S_1,\ldots,S_k$ be the 
+strings of length $N$ such that $H(S_0)=S_1 \ldots S_k$ ($H(S_0)$ is the concatenation of
+the $S_i$'s). The cryptographic PRNG $X$ defined in (\ref{equation Oplus})
+is the algorithm mapping any string of length $2N$ $x_0S_0$ into the string
+$(x_0\oplus S_0 \oplus S_1)(x_0\oplus S_0 \oplus S_1\oplus S_2)\ldots
+(x_o\bigoplus_{i=0}^{i=k}S_i)$. Particularly one has $\ell_{X}(2N)=kN=\ell_H(N)$. 
+We claim now that if this PRNG is secure,
+then the new one is secure too.

 
 \begin{proposition}
 
 \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. 
+\label{cryptopreuve}
+If $H$ is a secure cryptographic PRNG, then $X$ is a secure cryptographic
+PRNG too.

 \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.
-\end{proof}
+The proposition is proved by contraposition. Assume that $X$ is not
+secure. By Definition, there exists a polynomial time probabilistic
+algorithm $D$, a positive polynomial $p$, such that for all $k_0$ there exists
+$N\geq \frac{k_0}{2}$ satisfying 
+$$| \mathrm{Pr}[D(X(U_{2N}))=1]-\mathrm{Pr}[D(U_{kN}=1]|\geq \frac{1}{p(2N)}.$$
+We describe a new probabilistic algorithm $D^\prime$ on an input $w$ of size
+$kN$:
+\begin{enumerate}
+\item Decompose $w$ into $w=w_1\ldots w_{k}$, where each $w_i$ has size $N$.
+\item Pick a string $y$ of size $N$ uniformly at random.
+\item Compute $z=(y\oplus w_1)(y\oplus w_1\oplus w_2)\ldots (y
+  \bigoplus_{i=1}^{i=k} w_i).$
+\item Return $D(z)$.
+\end{enumerate}
+
+
+Consider  for each $y\in \mathbb{B}^{kN}$ the function $\varphi_{y}$
+from $\mathbb{B}^{kN}$ into $\mathbb{B}^{kN}$ mapping $w=w_1\ldots w_k$
+(each $w_i$ has length $N$) to 
+$(y\oplus w_1)(y\oplus w_1\oplus w_2)\ldots (y
+  \bigoplus_{i=1}^{i=k_1} w_i).$ By construction, one has for every $w$,
+\begin{equation}\label{PCH-1}
+D^\prime(w)=D(\varphi_y(w)),
+\end{equation}
+where $y$ is randomly generated. 
+Moreover, for each $y$, $\varphi_{y}$ is injective: if 
+$(y\oplus w_1)(y\oplus w_1\oplus w_2)\ldots (y\bigoplus_{i=1}^{i=k_1}
+w_i)=(y\oplus w_1^\prime)(y\oplus w_1^\prime\oplus w_2^\prime)\ldots
+(y\bigoplus_{i=1}^{i=k} w_i^\prime)$, then for every $1\leq j\leq k$,
+$y\bigoplus_{i=1}^{i=j} w_i^\prime=y\bigoplus_{i=1}^{i=j} w_i$. It follows,
+by a direct induction, that $w_i=w_i^\prime$. Furthermore, since $\mathbb{B}^{kN}$
+is finite, each $\varphi_y$ is bijective. Therefore, and using (\ref{PCH-1}),
+one has
+\begin{equation}\label{PCH-2}
+\mathrm{Pr}[D^\prime(U_{kN})=1]=\mathrm{Pr}[D(\varphi_y(U_{kN}))=1]=\mathrm{Pr}[D(U_{kN})=1].
+\end{equation}

 
 

-The conclusion of these propositions is that the proposed metric is more precise than the Euclidian distance, that is:
+Now, using (\ref{PCH-1}) again, one has  for every $x$,
+\begin{equation}\label{PCH-3}
+D^\prime(H(x))=D(\varphi_y(H(x))),
+\end{equation}
+where $y$ is randomly generated. By construction, $\varphi_y(H(x))=X(yx)$,
+thus
+\begin{equation}\label{PCH-3}
+D^\prime(H(x))=D(yx),
+\end{equation}
+where $y$ is randomly generated. 
+It follows that 

 
 

-\begin{corollary}
-$D$ is finer than the Euclidian distance $\Delta$.
-\end{corollary}
+\begin{equation}\label{PCH-4}
+\mathrm{Pr}[D^\prime(H(U_{N}))=1]=\mathrm{Pr}[D(U_{2N})=1].
+\end{equation}
+ From (\ref{PCH-2}) and (\ref{PCH-4}), one can deduce that
+there exist a polynomial time probabilistic
+algorithm $D^\prime$, a positive polynomial $p$, such that for all $k_0$ there exists
+$N\geq \frac{k_0}{2}$ satisfying 
+$$| \mathrm{Pr}[D(H(U_{N}))=1]-\mathrm{Pr}[D(U_{kN}=1]|\geq \frac{1}{p(2N)},$$
+proving that $H$ is not secure, a contradiction. 
+\end{proof}

 
 

-This corollary can be reformulated as follows:

 
 

+\section{Cryptographical Applications}
+
+\subsection{A Cryptographically Secure PRNG for GPU}
+\label{sec:CSGPU}
+
+It is  possible to build a  cryptographically secure PRNG based  on the previous
+algorithm (Algorithm~\ref{algo:gpu_kernel2}).   Due to Proposition~\ref{cryptopreuve},
+it simply consists  in replacing
+the  {\it  xor-like} PRNG  by  a  cryptographically  secure one.  
+We have chosen the Blum Blum Shum generator~\cite{BBS} (usually denoted by BBS) having the form:
+$$x_{n+1}=x_n^2~ mod~ M$$  where $M$ is the product of  two prime numbers. These
+prime numbers  need to be congruent  to 3 modulus  4. BBS is
+very slow and only usable for cryptographic applications. 
+
+  
+The modulus operation is the most time consuming operation for current
+GPU cards.  So in order to obtain quite reasonable performances, it is
+required to use only modulus  on 32 bits integer numbers. Consequently
+$x_n^2$ need  to be less than $2^{32}$  and the number $M$  need to be
+less than $2^{16}$.  So in practice we can choose prime numbers around
+256 that are congruent to 3 modulus 4.  With 32 bits numbers, only the
+4 least significant bits of $x_n$ can be chosen (the maximum number of
+indistinguishable    bits    is    lesser    than   or    equals    to
+$log_2(log_2(x_n))$). So to generate a  32 bits number, we need to use
+8 times  the BBS  algorithm with different  combinations of  $M$. This
+approach is  not sufficient to pass  all the tests  of TestU01 because
+the fact  of having chosen  small values of  $M$ for the BBS  leads to
+have a  small period. So, in  order to add randomness  we proceed with
+the followings  modifications. 

 \begin{itemize}
 \begin{itemize}

-\item The topology produced by $\Delta$ is a subset of the topology produced by $D$.
-\item $D$ has more open sets than $\Delta$.
-\item It is harder to converge for the topology $\tau_D$ inherited by $D$, than to converge with the one inherited by $\Delta$, which is denoted here by $\tau_\Delta$.
+\item
+First we  define 16 arrangement arrays  instead of 2  (as described in
+algorithm \ref{algo:gpu_kernel2}) but only 2  are used at each call of
+the  PRNG kernels. In  practice, the  selection of  which combinations
+arrays will be used is different for all the threads and is determined
+by using  the three last bits  of two internal variables  used by BBS.
+This approach  adds more randomness.   In algorithm~\ref{algo:bbs_gpu},
+character  \& performs the  AND bitwise.  So using  \&7 with  a number
+gives the last 3 bits, so it provides a number between 0 and 7.
+\item
+Second, after the  generation of the 8 BBS numbers  for each thread we
+have a 32 bits number for which the period is possibly quite small. So
+to add randomness,  we generate 4 more BBS numbers  which allows us to
+shift  the 32 bits  numbers and  add upto  6 new  bits.  This  part is
+described  in algorithm~\ref{algo:bbs_gpu}.  In  practice, if  we call
+{\it strategy}, the number representing  the strategy, the last 2 bits
+of the first new BBS number are  used to make a left shift of at least
+3 bits. The  last 3 bits of the  second new BBS number are  add to the
+strategy whatever the value of the first left shift. The third and the
+fourth new BBS  numbers are used similarly to apply  a new left shift
+and add 3 new bits.
+\item
+Finally, as  we use 8 BBS numbers  for each thread, the  store of these
+numbers at the end of the  kernel is performed using a rotation. So,
+internal  variable for  BBS number  1 is  stored in  place  2, internal
+variable  for BBS  number 2  is  store ind  place 3,  ... and  internal
+variable for BBS number 8 is stored in place 1.

 \end{itemize}
 
 
 \end{itemize}
 
 

-\subsection{Chaos of the chaotic iterations on $\mathds{R}$}
-\label{chpt:Chaos des itérations chaotiques sur R}
-
-
-
-\subsubsection{Chaos according to Devaney}
-
-We have recalled previously that the chaotic iterations $\left(\Go, \mathcal{X}_d\right)$ are chaotic according to the formulation of Devaney. We can deduce that they are chaotic on $\mathds{R}$ too, when considering the order topology, because:
-\begin{itemize}
-\item $\left(\Go, \mathcal{X}_d\right)$ and $\left(g, \big[ 0, 2^{10} \big[_D\right)$ are semiconjugate by $\varphi$,
-\item Then $\left(g, \big[ 0, 2^{10} \big[_D\right)$ is a system chaotic according to Devaney, because the semiconjugacy preserve this character.
-\item But the topology generated by $D$ is finer than the topology generated by the Euclidian distance $\Delta$ -- which is the order topology.
-\item According to Theorem \ref{Th:chaos et finesse}, we can deduce that the chaotic iterations $g$ are indeed chaotic, as defined by Devaney, for the order topology on $\mathds{R}$.
-\end{itemize}
+\begin{algorithm}

 
 

-This result can be formulated as follows.
+\KwIn{InternalVarBBSArray: array with internal variables of the 8 BBS
+in global memory\;
+NumThreads: Number of threads\;
+tab: 2D Arrays containing 16 combinations (in first dimension)  of size combination\_size (in second dimension)\;}

 
 

-\begin{theorem}
-\label{th:IC et topologie de l'ordre}
-The chaotic iterations $g$ on $\mathds{R}$ are chaotic according to the Devaney's formulation, when $\mathds{R}$ has his usual topology, which is the order topology.
-\end{theorem}
+\KwOut{NewNb: array containing random numbers in global memory}
+\If{threadId is concerned} {
+  retrieve data from InternalVarBBSArray[threadId] in local variables including shared memory and x\;
+  we consider that bbs1 ... bbs8 represent the internal states of the 8 BBS numbers\;
+  offset = threadIdx\%combination\_size\;
+  o1 = threadIdx-offset+tab[bbs1\&7][offset]\;
+  o2 = threadIdx-offset+tab[8+bbs2\&7][offset]\;
+  \For{i=1 to n} {
+    t<<=4\;
+    t|=BBS1(bbs1)\&15\;
+    ...\;
+    t<<=4\;
+    t|=BBS8(bbs8)\&15\;
+    //two new shifts\;
+    t<<=BBS3(bbs3)\&3\;
+    t|=BBS1(bbs1)\&7\;
+     t<<=BBS7(bbs7)\&3\;
+    t|=BBS2(bbs2)\&7\;
+    t=t $\hat{ }$ shmem[o1] $\hat{ }$ shmem[o2]\;
+    shared\_mem[threadId]=t\;
+    x = x $\hat{ }$ t\;

 
 

-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.
- 
+    store the new PRNG in NewNb[NumThreads*threadId+i]\;
+  }
+  store internal variables in InternalVarXorLikeArray[threadId] using a rotation\;
+}

 
 

+\caption{main kernel for the BBS based PRNG GPU}
+\label{algo:bbs_gpu}
+\end{algorithm}

 
 

+In algorithm~\ref{algo:bbs_gpu}, t<<=4 performs a left shift of 4 bits
+on the variable  t and stores the result  in t. BBS1(bbs1)\&15 selects
+the last  four bits of the result  of BBS1. It should  be noticed that
+for the two new shifts, we use arbitrarily 4 BBSs that have previously
+been used.

 
 

-\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 bit  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
+\subsection{A Cryptographically Secure and Chaotic Asymetric Cryptosystem}

 
 

-\hline
- \end{array}
-$$
+\subsubsection{Recalls of the Blum-Goldwasser Probabilistic Cryptosystem}

 
 

-%% \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}
+The Blum-Goldwasser cryptosystem is a cryptographically secure asymmetric key encryption algorithm 
+proposed in 1984~\cite{Blum:1985:EPP:19478.19501}.  The encryption algorithm 
+implements an XOR-based stream cipher using the BBS PRNG, in order to generate 
+the keystream. Decryption is done by obtaining the initial seed thanks to
+the final state of the BBS generator and the secret key, thus leading to the
+ reconstruction of the keystream.

 
 

+The key generation consists in generating two prime numbers $(p,q)$, 
+randomly and independently of each other, that are
+ congruent to 3 mod 4, and to compute the modulus $N=pq$.
+The public key is $N$, whereas the secret key is the factorization $(p,q)$.

 
 
 
 

+Suppose Bob wishes to send a string $m=(m_0, \dots, m_{L-1})$ of $L$ bits to Alice:
+\begin{enumerate}
+\item Bob picks an integer $r$ randomly in the interval $[1,N$ and computes $x_0 = r^2~mod~N$.
+\item He uses the BBS to generate the keystream of $L$ pseudorandom bits $(b_0, \dots, b_{L-1})$, as follows. For $i=0$ to $L-1$,
+\begin{itemize}
+\item $i=0$.
+\item While $i \leqslant L-1$:
+\begin{itemize}
+\item Set $b_i$ equal to the least-significant\footnote{BBS can securely output up to O(loglogN) of the least-significant bits of xi during each round.} bit of $x_i$,
+\item $i=i+1$,
+\item $x_i = (x_{i-1})^2~mod~N.$
+\end{itemize}
+\end{itemize}
+\item The ciphertext is computed by XORing the plaintext bits $m$ with the keystream: $ c = (c_0, \dots, c_{L-1}) = m \oplus  b$.
+\end{enumerate}
+The ciphertext is $(c, y)$, where $y=x_{0}^{2^{L}}~mod~N.$.

 
 
 
 

-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. 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.
+When Alice receives $(c_0, \dots, c_{L-1}), y$, she can recover $m$ as follows:
+\begin{enumerate}
+\item Using the secret key $(p,q)$, she computes $r_p = y^{((p+1)/4)^{L}}~mod~p$ and $r_q = y^{((q+1)/4)^{L}}~mod~q$.
+\item The initial seed can be obtained using the following procedure: $x_0=q(q^{-1}~{mod}~p)r_p + p(p^{-1}~{mod}~q)r_q~{mod}~N$
+\item Recompute the bit-vector $b$ by using BBS and $x_0$.
+\item Compute finally the plaintext by XORing the keystream with the ciphertext: $ m = c \oplus  b$.
+\end{enumerate}

 
 

-\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{threadId is concerned} {
-  retrieve data from InternalVarXorLikeArray[threadId] 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*threadId+i]\;
-  }
-  store internal variables in InternalVarXorLikeArray[threadId]\;
-}
+\subsubsection{Proposal of a new Asymmetric Cryptosystem Adapted from Blum-Goldwasser}

 
 

-\caption{main kernel for the chaotic iterations based PRNG GPU version}
-\label{algo:gpu_kernel}
-\end{algorithm}

 
 

-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 have been tested upto $10$ millions.
+\section{Conclusion}

 
 

-\section{Experiments}

 
 

-Differents experiments have been performed in order to measure the generation speed.
+In  this  paper  we have  presented  a  new  class  of  PRNGs based  on  chaotic
+iterations. We have proven that these PRNGs are chaotic in the sense of Devaney.
+We also propose a PRNG cryptographically secure and its implementation on GPU.

 
 

+An  efficient implementation  on  GPU based  on  a xor-like  PRNG  allows us  to
+generate   a  huge   number   of  pseudorandom   numbers   per  second   (about
+20Gsamples/s). This PRNG succeeds to pass the hardest batteries of TestU01.

 
 

-First of all we have compared the time to generate X random numbers with both the CPU version and the GPU version. 
+In future  work we plan to  extend this work  for parallel PRNG for  clusters or
+grid computing.

 
 

-Faire une courbe du nombre de random en fonction du nombre de threads, éventuellement en fonction du nombres de threads par bloc.

 
 
 
 

-\section{Conclusion}
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+\bibliographystyle{plain} 

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