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[prng_gpu.git] / prng_gpu.tex

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\title{Efficient and Cryptographically Secure Generation of Chaotic Pseudorandom Numbers on GPU}
\begin{document}

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

\begin{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}

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.
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, DieHARD, and TestU01 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
3.2MSample/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  diferrent computing architectures: CPU,  field-programmable gate array
(FPGA), GPU and massively parallel  processor. This study is interesting because
it  shows the  performance  of the  same  PRNGs on  different architectures.   For
example,  the FPGA  is globally  the  fastest architecture  and it  is also  the
efficient one because it provides the fastest number of generated random numbers
per joule. Concerning the GPU,  authors can generate betweend 11 and 16GSample/s
with a GTX 280  GPU. The drawback of this work is  that those PRNGs only succeed
the {\it Crush} test which is easier than the {\it Big Crush} test.

Cuda  has developped  a  library for  the  generation of  random numbers  called
Curand~\cite{curand11}.        Several       PRNGs        are       implemented:
Xorwow~\cite{Marsaglia2003} and  some variants of Sobol. Some  tests report that
the  fastest version provides  15GSample/s on  the new  Fermi C2050  card. Their
PRNGs fail to succeed the whole tests of TestU01 on only one test.
\newline
\newline
To the best of our knowledge no GPU implementation have been proven to have chaotic properties.

\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 $V$. $f^{k}=f\circ ...\circ f$
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 $f :
\mathcal{X} \rightarrow \mathcal{X}$.

\begin{definition}
$f$ is said to be \emph{topologically transitive} if, for any pair of open sets
$U,V \subset \mathcal{X}$, there exists $k>0$ such that $f^k(U) \cap V \neq
\varnothing$.
\end{definition}

\begin{definition}
An element $x$ is a \emph{periodic point} for $f$ of period $n\in \mathds{N}^*$
if $f^{n}(x)=x$.% The set of periodic points of $f$ is denoted $Per(f).$
\end{definition}

\begin{definition}
$f$ is said to be \emph{regular} on $(\mathcal{X}, \tau)$ if the set of periodic
points for $f$ is dense in $\mathcal{X}$: for any point $x$ in $\mathcal{X}$,
any neighborhood of $x$ contains at least one periodic point (without
necessarily the same period).
\end{definition}


\begin{definition}[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}

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

\begin{definition}
\label{sensitivity} $f$ has \emph{sensitive dependence on initial conditions}
if there exists $\delta >0$ such that, for any $x\in \mathcal{X}$ and any
neighborhood $V$ of $x$, there exist $y\in V$ and $n > 0$ such that
$d\left(f^{n}(x), f^{n}(y)\right) >\delta $.

$\delta$ is called the \emph{constant of sensitivity} of $f$.
\end{definition}

Indeed, Banks \emph{et al.} have proven in~\cite{Banks92} that when $f$ is
chaotic and $(\mathcal{X}, d)$ is a metric space, then $f$ has the property of
sensitive dependence on initial conditions (this property was formerly an
element of the definition of chaos). To sum up, quoting Devaney
in~\cite{Devaney}, a chaotic dynamical system ``is unpredictable because of the
sensitive dependence on initial conditions. It cannot be broken down or
simplified into two subsystems which do not interact because of topological
transitivity. And in the midst of this random behavior, we nevertheless have an
element of regularity''. Fundamentally different behaviors are consequently
possible and occur in an unpredictable way.



\subsection{Chaotic Iterations}
\label{sec:chaotic iterations}


Let us consider  a \emph{system} with a finite  number $\mathsf{N} \in
\mathds{N}^*$ of elements  (or \emph{cells}), so that each  cell has a
Boolean  \emph{state}. Having $\mathsf{N}$ Boolean values for these
 cells  leads to the definition of a particular \emph{state  of the
system}. A sequence which  elements belong to $\llbracket 1;\mathsf{N}
\rrbracket $ is called a \emph{strategy}. The set of all strategies is
denoted by $\llbracket 1, \mathsf{N} \rrbracket^\mathds{N}.$

\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
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
\begin{equation}
\forall    n\in     \mathds{N}^{\ast     },    \forall     i\in
\llbracket1;\mathsf{N}\rrbracket ,x_i^n=\left\{
\begin{array}{ll}
  x_i^{n-1} &  \text{ if  }S^n\neq i \\
  \left(f(x^{n-1})\right)_{S^n} & \text{ if }S^n=i.
\end{array}\right.
\end{equation}
\end{definition}

In other words, at the $n^{th}$ iteration, only the $S^{n}-$th cell is
\textquotedblleft  iterated\textquotedblright .  Note  that in  a more
general  formulation,  $S^n$  can   be  a  subset  of  components  and
$\left(f(x^{n-1})\right)_{S^{n}}$      can     be      replaced     by
$\left(f(x^{k})\right)_{S^{n}}$, where  $k<n$, describing for example,
delays  transmission~\cite{Robert1986,guyeux10}.  Finally,  let us  remark that
the term  ``chaotic'', in  the name of  these iterations,  has \emph{a
priori} no link with the mathematical theory of chaos, 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 $\delta $ be the \emph{discrete Boolean metric}, $\delta
(x,y)=0\Leftrightarrow x=y.$ Given a function $f$, define the function:
\begin{equation}
\begin{array}{lrll}
F_{f}: & \llbracket1;\mathsf{N}\rrbracket\times \mathds{B}^{\mathsf{N}} &
\longrightarrow & \mathds{B}^{\mathsf{N}} \\
& (k,E) & \longmapsto & \left( E_{j}.\delta (k,j)+f(E)_{k}.\overline{\delta
(k,j)}\right) _{j\in \llbracket1;\mathsf{N}\rrbracket},%
\end{array}%
\end{equation}%
\noindent where + and . are the Boolean addition and product operations.
Consider the phase space:
\begin{equation}
\mathcal{X} = \llbracket 1 ; \mathsf{N} \rrbracket^\mathds{N} \times
\mathds{B}^\mathsf{N},
\end{equation}
\noindent and the map defined on $\mathcal{X}$:
\begin{equation}
G_f\left(S,E\right) = \left(\sigma(S), F_f(i(S),E)\right), \label{Gf}
\end{equation}
\noindent where $\sigma$ is the \emph{shift} function defined by $\sigma
(S^{n})_{n\in \mathds{N}}\in \llbracket 1, \mathsf{N} \rrbracket^\mathds{N}\longrightarrow (S^{n+1})_{n\in
\mathds{N}}\in \llbracket 1, \mathsf{N} \rrbracket^\mathds{N}$ 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.
\end{equation}%

With this formulation, a shift function appears as a component of chaotic
iterations. The shift function is a famous example of a chaotic
map~\cite{Devaney} but its presence is not sufficient enough to claim $G_f$ as
chaotic. 
To study this claim, a new distance between two points $X = (S,E), Y =
(\check{S},\check{E})\in
\mathcal{X}$ has been introduced in \cite{guyeux10} as follows:
\begin{equation}
d(X,Y)=d_{e}(E,\check{E})+d_{s}(S,\check{S}),
\end{equation}
\noindent where
\begin{equation}
\left\{
\begin{array}{lll}
\displaystyle{d_{e}(E,\check{E})} & = & \displaystyle{\sum_{k=1}^{\mathsf{N}%
}\delta (E_{k},\check{E}_{k})}, \\
\displaystyle{d_{s}(S,\check{S})} & = & \displaystyle{\dfrac{9}{\mathsf{N}}%
\sum_{k=1}^{\infty }\dfrac{|S^k-\check{S}^k|}{10^{k}}}.%
\end{array}%
\right.
\end{equation}


This new distance has been introduced to satisfy the following requirements.
\begin{itemize}
\item When the number of different cells between two systems is increasing, then
their distance should increase too.
\item In addition, if two systems present the same cells and their respective
strategies start with the same terms, then the distance between these two points
must be small because the evolution of the two systems will be the same for a
while. Indeed, the two dynamical systems start with the same initial condition,
use the same update function, and as strategies are the same for a while, then
components that are updated are the same too.
\end{itemize}
The distance presented above follows these recommendations. Indeed, if the floor
value $\lfloor d(X,Y)\rfloor $ is equal to $n$, then the systems $E, \check{E}$
differ in $n$ cells ($d_e$ 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}
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}

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 $\mathds{B}^\mathsf{N}$ to itself. The
{\emph{asynchronous iteration graph}} associated with $f$ is the
directed graph $\Gamma(f)$ defined by: the set of vertices is
$\mathds{B}^\mathsf{N}$; 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 $x'$.

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


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

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).

\section{Application to pseudorandomness}
\label{sec:pseudorandom}
\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 
possesses various chaos properties that none of the generators used as input
present.

\begin{algorithm}[h!]
%\begin{scriptsize}
\KwIn{a function $f$, an iteration number $b$, an initial configuration $x^0$
($n$ bits)}
\KwOut{a configuration $x$ ($n$ bits)}
$x\leftarrow x^0$\;
$k\leftarrow b + \textit{XORshift}(b)$\;
\For{$i=0,\dots,k$}
{
$s\leftarrow{\textit{XORshift}(n)}$\;
$x\leftarrow{F_f(s,x)}$\;
}
return $x$\;
%\end{scriptsize}
\caption{PRNG with chaotic functions}
\label{CI Algorithm}
\end{algorithm}

\begin{algorithm}[h!]
\KwIn{the internal configuration $z$ (a 32-bit word)}
\KwOut{$y$ (a 32-bit word)}
$z\leftarrow{z\oplus{(z\ll13)}}$\;
$z\leftarrow{z\oplus{(z\gg17)}}$\;
$z\leftarrow{z\oplus{(z\ll5)}}$\;
$y\leftarrow{z}$\;
return $y$\;
\medskip
\caption{An arbitrary round of \textit{XORshift} algorithm}
\label{XORshift}
\end{algorithm}





This generator is synthesized in Algorithm~\ref{CI Algorithm}.
It takes as input: a function $f$;
an integer $b$, ensuring that the number of executed iterations is at least $b$
and at most $2b+1$; and an initial configuration $x^0$.
It returns the new generated configuration $x$.  Internally, it embeds two
\textit{XORshift}$(k)$ PRNGs~\cite{Marsaglia2003} that returns integers
uniformly distributed
into $\llbracket 1 ; k \rrbracket$.
\textit{XORshift} is a category of very fast PRNGs designed by George Marsaglia,
which repeatedly uses the transform of exclusive or (XOR, $\oplus$) on a number
with a bit shifted version of it. This PRNG, which has a period of
$2^{32}-1=4.29\times10^9$, is summed up in Algorithm~\ref{XORshift}. It is used
in our PRNG to compute the strategy length and the strategy elements.


We have proven in \cite{bcgr11:ip} that,
\begin{theorem}
  Let $f: \mathds{B}^{n} \rightarrow \mathds{B}^{n}$, $\Gamma(f)$ its
  iteration graph, $\check{M}$ its adjacency
  matrix and $M$ a $n\times n$ matrix defined as in the previous lemma.
  If $\Gamma(f)$ is strongly connected, then 
  the output of the PRNG detailed in Algorithm~\ref{CI Algorithm} follows 
  a law that tends to the uniform distribution 
  if and only if $M$ is a double stochastic matrix.
\end{theorem} 

This former generator as successively passed various batteries of statistical tests, as the NIST tests~\cite{bcgr11:ip}.

\subsection{Improving the Speed of the Former Generator}

Instead of updating only one cell at each iteration, we can try to choose a
subset of components and to update them together. Such an attempt leads
to a kind of merger of the two sequences used in Algorithm 
\ref{CI Algorithm}. When the updating function is the vectorial negation,
this algorithm can be rewritten as follows:

\begin{equation}
\left\{
\begin{array}{l}
x^0 \in \llbracket 0, 2^\mathsf{N}-1 \rrbracket, S \in \llbracket 0, 2^\mathsf{N}-1 \rrbracket^\mathds{N} \\
\forall n \in \mathds{N}^*, x^n = x^{n-1} \oplus S^n,
\end{array}
\right.
\label{equation Oplus}
\end{equation}
where $\oplus$ is for the bitwise exclusive or between two integers. 
This rewritten can be understood as follows. The $n-$th term $S^n$ of the
sequence $S$, which is an integer of $\mathsf{N}$ binary digits, presents
the list of cells to update in the state $x^n$ of the system (represented
as an integer having $\mathsf{N}$ bits too). More precisely, the $k-$th 
component of this state (a binary digit) changes if and only if the $k-$th 
digit in the binary decomposition of $S^n$ is 1.

The single basic component presented in Eq.~\ref{equation Oplus} is of 
ordinary use as a good elementary brick in various PRNGs. It corresponds
to the following discrete dynamical system in chaotic iterations:

\begin{equation}
\forall    n\in     \mathds{N}^{\ast     },    \forall     i\in
\llbracket1;\mathsf{N}\rrbracket ,x_i^n=\left\{
\begin{array}{ll}
  x_i^{n-1} &  \text{ if  } i \notin \mathcal{S}^n \\
  \left(f(x^{n-1})\right)_{S^n} & \text{ if }i \in \mathcal{S}^n.
\end{array}\right.
\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{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.

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, $Y \in \mathcal{P}\left(X\right) \Longleftrightarrow Y \subset X$.

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}%

Another time, a shift function appears as a component of these general chaotic 
iterations. 

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
$A\,\Delta\,B = (A \setminus B) \cup (B \setminus A)$.


\begin{proposition}
The function $d$ defined in Eq.~\ref{nouveau d} is a metric on $\mathcal{X}$.
\end{proposition}

\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}


Before being able to study the topological behavior of the general 
chaotic iterations, we must firstly establish that:

\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}
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}


It is now possible to study the topological behavior of the general chaotic
iterations. We will prove that,

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

Let us firstly prove the following lemma.

\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}

\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}

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}

In  order to  implement efficiently  a PRNG  based on  chaotic iterations  it is
possible to improve  previous works [ref]. One solution  consists in considering
that the  strategy used contains all the  bits for which the  negation is
achieved out. Then in order to apply  the negation on these bits we can simply
apply the  xor operator between  the current number  and the strategy. In
order to obtain the strategy we also use a classical PRNG.

Here  is an  example with  16-bits numbers  showing how  the bitwise  operations
are
applied.  Suppose  that $x$ and the  strategy $S^i$ are defined  in binary mode.
Then the following table shows the result of $x$ xor $S^i$.
$$
\begin{array}{|cc|cccccccccccccccc|}
\hline
x      &=&1&0&1&1&1&0&1&0&1&0&0&1&0&0&1&0\\
\hline
S^i      &=&0&1&1&0&0&1&1&0&1&1&1&0&0&1&1&1\\
\hline
x \oplus S^i&=&1&1&0&1&1&1&0&0&0&1&1&1&0&1&0&1\\
\hline

\hline
 \end{array}
$$





\lstset{language=C,caption={C code of the sequential chaotic iterations based
PRNG},label=algo:seqCIprng}
\begin{lstlisting}
unsigned int CIprng() {
  static unsigned int x = 123123123;
  unsigned long t1 = xorshift();
  unsigned long t2 = xor128();
  unsigned long t3 = xorwow();
  x = x^(unsigned int)t1;
  x = x^(unsigned int)(t2>>32);
  x = x^(unsigned int)(t3>>32);
  x = x^(unsigned int)t2;
  x = x^(unsigned int)(t1>>32);
  x = x^(unsigned int)t3;
  return x;
}
\end{lstlisting}





In listing~\ref{algo:seqCIprng}  a sequential version of  our chaotic iterations
based PRNG is  presented.  The xor operator is  represented by \textasciicircum.
This  function uses  three classical  64-bits PRNG:  the  \texttt{xorshift}, the
\texttt{xor128}  and  the  \texttt{xorwow}.   In  the following,  we  call  them
xor-like PRNGSs.   These three PRNGs are  presented in~\cite{Marsaglia2003}.  As
each xor-like PRNG  used works with 64-bits and as our  PRNG works with 32-bits,
the use of \texttt{(unsigned int)} selects the 32 least significant bits whereas
\texttt{(unsigned int)(t3$>>$32)}  selects the 32 most significants  bits of the
variable \texttt{t}.   So to produce a  random number realizes  6 xor operations
with 6 32-bits  numbers produced by 3 64-bits PRNG.   This version successes the
BigCrush of the TestU01 battery~\cite{LEcuyerS07}.

\section{Efficient PRNGs based on chaotic iterations on GPU}
\label{sec:efficient prng gpu}

In  order to benefit  from computing  power of  GPU, a  program needs  to define
independent blocks of threads which  can be computed simultaneously. In general,
the larger the number of threads is,  the more local memory is used and the less
branching  instructions are  used (if,  while, ...),  the better  performance is
obtained  on  GPU.  So  with  algorithm  \ref{algo:seqCIprng}  presented in  the
previous section, it is possible to  build a similar program which computes PRNG
on   GPU.  In  the   CUDA~\cite{Nvid10}  environment,   threads  have   a  local
identificator, called \texttt{ThreadIdx} relative to the block containing them.


\subsection{Naive version for GPU}

From the CPU version, it is possible  to obtain a quite similar version for GPU.
The principe consists in assigning the computation of a PRNG as in sequential to
each thread  of the  GPU.  Of course,  it is  essential that the  three xor-like
PRNGs  used for  our computation  have different  parameters. So  we  chose them
randomly with  another PRNG. As the  initialisation is performed by  the CPU, we
have  chosen  to  use  the  ISAAC  PRNG~\cite{Jenkins96}  to  initalize  all  the
parameters for  the GPU version  of our PRNG.   The implementation of  the three
xor-like  PRNGs  is  straightforward  as  soon as  their  parameters  have  been
allocated in  the GPU memory.  Each xor-like PRNGs  used works with  an internal
number  $x$  which keeps  the  last  generated  random numbers.  Other  internal
variables  are   also  used   by  the  xor-like   PRNGs.  More   precisely,  the
implementation of the  xor128, the xorshift and the  xorwow respectively require
4, 5 and 6 unsigned long as internal variables.

\begin{algorithm}

\KwIn{InternalVarXorLikeArray: array with internal variables of the 3 xor-like
PRNGs in global memory\;
NumThreads: Number of threads\;}
\KwOut{NewNb: array containing random numbers in global memory}
\If{threadIdx is concerned by the computation} {
  retrieve data from InternalVarXorLikeArray[threadIdx] in local variables\;
  \For{i=1 to n} {
    compute a new PRNG as in Listing\ref{algo:seqCIprng}\;
    store the new PRNG in NewNb[NumThreads*threadIdx+i]\;
  }
  store internal variables in InternalVarXorLikeArray[threadIdx]\;
}

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

Algorithm~\ref{algo:gpu_kernel}  presents a naive  implementation of  PRNG using
GPU.  According  to the available  memory in the  GPU and the number  of threads
used simultenaously,  the number  of random numbers  that a thread  can generate
inside   a    kernel   is   limited,   i.e.    the    variable   \texttt{n}   in
algorithm~\ref{algo:gpu_kernel}. For example, if  $100,000$ threads are used and
if $n=100$\footnote{in fact, we need to add the initial seed (a 32-bits number)}
then   the  memory   required   to  store   internals   variables  of   xor-like
PRNGs\footnote{we multiply this number by $2$ in order to count 32-bits numbers}
and  random  number of  our  PRNG  is  equals to  $100,000\times  ((4+5+6)\times
2+(1+100))=1,310,000$ 32-bits numbers, i.e. about $52$Mb.

All the  tests performed  to pass the  BigCrush of TestU01  succeeded. Different
number of threads, called \texttt{NumThreads} in our algorithm, have been tested
upto $10$ millions.
\newline
\newline
{\bf QUESTION : on laisse cette remarque, je suis mitigé !!!}

\begin{remark}
Algorithm~\ref{algo:gpu_kernel}  has  the  advantage to  manipulate  independent
PRNGs, so this version is easily usable on a cluster of computer. The only thing
to ensure is to use a single ISAAC PRNG. For this, a simple solution consists in
using a master node for the initialization which computes the initial parameters
for all the differents nodes involves in the computation.
\end{remark}

\subsection{Improved version for GPU}

As GPU cards using CUDA have shared memory between threads of the same block, it
is possible  to use this  feature in order  to simplify the  previous algorithm,
i.e., using less  than 3 xor-like PRNGs. The solution  consists in computing only
one xor-like PRNG by thread, saving  it into shared memory and using the results
of some  other threads in the  same block of  threads. In order to  define which
thread uses the result of which other  one, we can use a permutation array which
contains  the indexes  of  all threads  and  for which  a  permutation has  been
performed.  In Algorithm~\ref{algo:gpu_kernel2}, 2 permutations arrays are used.
The    variable   \texttt{offset}    is    computed   using    the   value    of
\texttt{permutation\_size}.   Then we  can compute  \texttt{o1}  and \texttt{o2}
which represent the indexes of the  other threads for which the results are used
by the  current thread. In  the algorithm, we  consider that a  64-bits xor-like
PRNG is used, that is why both 32-bits parts are used.

This version also succeeds to the {\it BigCrush} batteries of tests.

\begin{algorithm}

\KwIn{InternalVarXorLikeArray: array with internal variables of 1 xor-like PRNGs
in global memory\;
NumThreads: Number of threads\;
tab1, tab2: Arrays containing permutations of size permutation\_size\;}

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

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

\caption{main kernel for the chaotic iterations based PRNG GPU efficient
version}
\label{algo:gpu_kernel2}
\end{algorithm}

\subsection{Theoretical Evaluation of the Improved Version}

A run of Algorithm~\ref{algo:gpu_kernel2} consists in three operations having 
the form of Equation~\ref{equation Oplus}, which is equivalent to the iterative
system of Eq.~\ref{eq:generalIC}. That is, three iterations of the general chaotic
iterations are realized between two stored values of the PRNG.
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 each right 
term, corresponding to terms of the strategies,  can possibly be equal to any
integer of $\llbracket 1, \mathsf{N} \rrbracket$. 

Such a result is obvious for the two first lines, as for the xor-like(), all the
integers belonging into its interval of definition can occur at each iteration.
It can be easily stated for the two last lines by an immediate mathematical
induction.

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.

\section{Experiments}
\label{sec:experiments}

Different experiments  have been  performed in order  to measure  the generation
speed. We have used  a computer equiped with Tesla C1060 NVidia  GPU card and an
Intel  Xeon E5530 cadenced  at 2.40  GHz for  our experiments  and we  have used
another one  equipped with  a less performant  CPU and  a GeForce GTX  280. Both
cards have 240 cores.

In  Figure~\ref{fig:time_xorlike_gpu} we  compare the  number of  random numbers
generated per second with the xor-like based PRNG. In this figure, the optimized
version use the {\it xor64} described in~\cite{Marsaglia2003}. The naive version
use  the three  xor-like  PRNGs described  in Listing~\ref{algo:seqCIprng}.   In
order to obtain the optimal performance we removed the storage of random numbers
in the GPU memory. This step is time consuming and slows down the random numbers
generation.  Moreover, if one is  interested by applications that consume random
numbers  directly   when  they  are  generated,  their   storage  are  completely
useless. In this  figure we can see  that when the number of  threads is greater
than approximately 30,000 upto 5 millions the number of random numbers generated
per second  is almost constant.  With the  naive version, it is  between 2.5 and
3GSample/s.   With  the  optimized   version,  it  is  approximately  equals  to
20GSample/s. Finally  we can remark  that both GPU  cards are quite  similar. In
practice,  the Tesla C1060  has more  memory than  the GTX  280 and  this memory
should be of better quality.

\begin{figure}[htbp]
\begin{center}
  \includegraphics[scale=.7]{curve_time_xorlike_gpu.pdf}
\end{center}
\caption{Number of random numbers generated per second with the xorlike based PRNG}
\label{fig:time_xorlike_gpu}
\end{figure}


In  comparison,   Listing~\ref{algo:seqCIprng}  allows  us   to  generate  about
138MSample/s with only one core of the Xeon E5530.


In Figure~\ref{fig:time_bbs_gpu}  we highlight the performance  of the optimized
BBS based  PRNG on GPU. Performances are  less important. On the  Tesla C1060 we
obtain approximately 1.8GSample/s and on the GTX 280 about 1.6GSample/s.

\begin{figure}[htbp]
\begin{center}
  \includegraphics[scale=.7]{curve_time_bbs_gpu.pdf}
\end{center}
\caption{Number of random numbers generated per second with the BBS based PRNG}
\label{fig:time_bbs_gpu}
\end{figure}

Both  these  experimentations allows  us  to conclude  that  it  is possible  to
generate a  huge number of pseudorandom  numbers with the  xor-like version and
about tens  times less with the BBS  based version. The former  version has only
chaotic properties whereas the latter also has cryptographically properties.


%% \section{Cryptanalysis of the Proposed PRNG}


%% Mettre ici la preuve de PCH

%\section{The relativity of disorder}
%\label{sec:de la relativité du désordre}

%In the next two sections, we investigate the impact of the choices that have
%lead to the definitions of measures in Sections \ref{sec:chaotic iterations} and \ref{deuxième def}.

%\subsection{Impact of the topology's finenesse}

%Let us firstly introduce the following notations.

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



%\begin{theorem}
%\label{Th:chaos et finesse}
%Let $\mathcal{X}$ a set and $\tau, \tau'$ two topologies on $\mathcal{X}$ s.t.
%$\tau'$ is finer than $\tau$. Let $f:\mathcal{X} \to \mathcal{X}$, continuous
%both for $\tau$ and $\tau'$.

%If $(\mathcal{X}_{\tau'},f)$ is chaotic according to Devaney, then
%$(\mathcal{X}_\tau,f)$ is chaotic too.
%\end{theorem}

%\begin{proof}
%Let us firstly establish the transitivity of $(\mathcal{X}_\tau,f)$.

%Let $\omega_1, \omega_2$ two open sets of $\tau$. Then $\omega_1, \omega_2 \in
%\tau'$, becaus $\tau'$ is finer than $\tau$. As $f$ is $\tau'-$transitive, we
%can deduce that $\exists n \in \mathds{N}, \omega_1 \cap f^{(n)}(\omega_2) =
%\varnothing$. Consequently, $f$ is $\tau-$transitive.

%Let us now consider the regularity of $(\mathcal{X}_\tau,f)$, \emph{i.e.}, for
%all $x \in \mathcal{X}$, and for all $\tau-$neighborhood $V$ of $x$, there is a
%periodic point for $f$ into $V$.

%Let $x \in \mathcal{X}$ and $V \in \mathcal{V}_\tau (x)$ a $\tau-$neighborhood
%of $x$. By definition, $\exists \omega \in \tau, x \in \omega \subset V$.

%But $\tau \subset \tau'$, so $\omega \in \tau'$, and then $V \in
%\mathcal{V}_{\tau'} (x)$. As $(\mathcal{X}_{\tau'},f)$ is regular, there is a
%periodic point for $f$ into $V$, and the regularity of $(\mathcal{X}_\tau,f)$ is
%proven. 
%\end{proof}

%\subsection{A given system can always be claimed as chaotic}

%Let $f$ an iteration function on $\mathcal{X}$ having at least a fixed point.
%Then this function is chaotic (in a certain way):

%\begin{theorem}
%Let $\mathcal{X}$ a nonempty set and $f: \mathcal{X} \to \X$ a function having
%at least a fixed point.
%Then $f$ is $\tau_0-$chaotic, where $\tau_0$ is the trivial (indiscrete)
%topology on $\X$.
%\end{theorem}


%\begin{proof}
%$f$ is transitive when $\forall \omega, \omega' \in \tau_0 \setminus
%\{\varnothing\}, \exists n \in \mathds{N}, f^{(n)}(\omega) \cap \omega' \neq
%\varnothing$.
%As $\tau_0 = \left\{ \varnothing, \X \right\}$, this is equivalent to look for
%an integer $n$ s.t. $f^{(n)}\left( \X \right) \cap \X \neq \varnothing$. For
%instance, $n=0$ is appropriate.

%Let us now consider $x \in \X$ and $V \in \mathcal{V}_{\tau_0} (x)$. Then $V =
%\mathcal{X}$, so $V$ has at least a fixed point for $f$. Consequently $f$ is
%regular, and the result is established.
%\end{proof}




%\subsection{A given system can always be claimed as non-chaotic}

%\begin{theorem}
%Let $\mathcal{X}$ be a set and $f: \mathcal{X} \to \X$.
%If $\X$ is infinite, then $\left( \X_{\tau_\infty}, f\right)$ is not chaotic
%(for the Devaney's formulation), where $\tau_\infty$ is the discrete topology.
%\end{theorem}

%\begin{proof}
%Let us prove it by contradiction, assuming that $\left(\X_{\tau_\infty},
%f\right)$ is both transitive and regular.

%Let $x \in \X$ and $\{x\}$ one of its neighborhood. This neighborhood must
%contain a periodic point for $f$, if we want that $\left(\X_{\tau_\infty},
%f\right)$ is regular. Then $x$ must be a periodic point of $f$.

%Let $I_x = \left\{ f^{(n)}(x), n \in \mathds{N}\right\}$. This set is finite
%because  $x$ is periodic, and $\mathcal{X}$ is infinite, then $\exists y \in
%\mathcal{X}, y \notin I_x$.

%As $\left(\X_{\tau_\infty}, f\right)$ must be transitive, for all open nonempty
%sets $A$ and $B$, an integer $n$ must satisfy $f^{(n)}(A) \cap B \neq
%\varnothing$. However $\{x\}$ and $\{y\}$ are open sets and $y \notin I_x
%\Rightarrow \forall n, f^{(n)}\left( \{x\} \right) \cap \{y\} = \varnothing$.
%\end{proof}






%\section{Chaos on the order topology}
%\label{sec: chaos order topology}
%\subsection{The phase space is an interval of the real line}

%\subsubsection{Toward a topological semiconjugacy}

%In what follows, our intention is to establish, by using a topological
%semiconjugacy, that chaotic iterations over $\mathcal{X}$ can be described as
%iterations on a real interval. To do so, we must firstly introduce some
%notations and terminologies. 

%Let $\mathcal{S}_\mathsf{N}$ be the set of sequences belonging into $\llbracket
%1; \mathsf{N}\rrbracket$ and $\mathcal{X}_{\mathsf{N}} = \mathcal{S}_\mathsf{N}
%\times \B^\mathsf{N}$.


%\begin{definition}
%The function $\varphi: \mathcal{S}_{10} \times\mathds{B}^{10} \rightarrow \big[
%0, 2^{10} \big[$ is defined by:
%\begin{equation}
% \begin{array}{cccl}
%\varphi: & \mathcal{X}_{10} = \mathcal{S}_{10} \times\mathds{B}^{10}&
%\longrightarrow & \big[ 0, 2^{10} \big[ \\
% & (S,E) = \left((S^0, S^1, \hdots ); (E_0, \hdots, E_9)\right) & \longmapsto &
%\varphi \left((S,E)\right)
%\end{array}
%\end{equation}
%where $\varphi\left((S,E)\right)$ is the real number:
%\begin{itemize}
%\item whose integral part $e$ is $\displaystyle{\sum_{k=0}^9 2^{9-k} E_k}$, that
%is, the binary digits of $e$ are $E_0 ~ E_1 ~ \hdots ~ E_9$.
%\item whose decimal part $s$ is equal to $s = 0,S^0~ S^1~ S^2~ \hdots =
%\sum_{k=1}^{+\infty} 10^{-k} S^{k-1}.$ 
%\end{itemize}
%\end{definition}



%$\varphi$ realizes the association between a point of $\mathcal{X}_{10}$ and a
%real number into $\big[ 0, 2^{10} \big[$. We must now translate the chaotic
%iterations $\Go$ on this real interval. To do so, two intermediate functions
%over $\big[ 0, 2^{10} \big[$ must be introduced:


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

%We are now able to define the function $g$, whose goal is to translate the
%chaotic iterations $\Go$ on an interval of $\mathds{R}$.

%\begin{definition}
%$g:\big[ 0, 2^{10} \big[ \longrightarrow \big[ 0, 2^{10} \big[$ is defined by:
%\begin{equation}
%\begin{array}{cccc}
%g: & \big[ 0, 2^{10} \big[ & \longrightarrow & \big[ 0, 2^{10} \big[ \\
% & x & \longmapsto & g(x)
%\end{array}
%\end{equation}
%where g(x) is the real number of $\big[ 0, 2^{10} \big[$ defined bellow:
%\begin{itemize}
%\item its integral part has a binary decomposition equal to $e_0', \hdots,
%e_9'$, with:
% \begin{equation}
%e_i' = \left\{
%\begin{array}{ll}
%e(x)_i & \textrm{ if } i \neq s^0\\
%e(x)_i + 1 \textrm{ (mod 2)} & \textrm{ if } i = s^0\\
%\end{array}
%\right.
%\end{equation}
%\item whose decimal part is $s(x)^1, s(x)^2, \hdots$
%\end{itemize}
%\end{definition}

%\bigskip


%In other words, if $x = \displaystyle{\sum_{k=0}^{9} 2^{9-k} e_k + 
%\sum_{k=0}^{+\infty} s^{k} ~10^{-k-1}}$, then:
%\begin{equation}
%g(x) =
%\displaystyle{\sum_{k=0}^{9} 2^{9-k} (e_k + \delta(k,s^0) \textrm{ (mod 2)}) + 
%\sum_{k=0}^{+\infty} s^{k+1} 10^{-k-1}}. 
%\end{equation}


%\subsubsection{Defining a metric on $\big[ 0, 2^{10} \big[$}

%Numerous metrics can be defined on the set $\big[ 0, 2^{10} \big[$, the most
%usual one being the Euclidian distance recalled bellow:

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

%\medskip

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



%\begin{definition}
%Let $x,y \in \big[ 0, 2^{10} \big[$.
%$D$ denotes the function from $\big[ 0, 2^{10} \big[^2$ to $\mathds{R}^+$
%defined by: $D(x,y) = D_e\left(e(x),e(y)\right) + D_s\left(s(x),s(y)\right)$,
%where:
%\begin{center}
%$\displaystyle{D_e(E,\check{E}) = \sum_{k=0}^\mathsf{9} \delta (E_k,
%\check{E}_k)}$, ~~and~ $\displaystyle{D_s(S,\check{S}) = \sum_{k = 1}^\infty
%\dfrac{|S^k-\check{S}^k|}{10^k}}$.
%\end{center}
%\end{definition}

%\begin{proposition}
%$D$ is a distance on $\big[ 0, 2^{10} \big[$.
%\end{proposition}

%\begin{proof}
%The three axioms defining a distance must be checked.
%\begin{itemize}
%\item $D \geqslant 0$, because everything is positive in its definition. If
%$D(x,y)=0$, then $D_e(x,y)=0$, so the integral parts of $x$ and $y$ are equal
%(they have the same binary decomposition). Additionally, $D_s(x,y) = 0$, then
%$\forall k \in \mathds{N}^*, s(x)^k = s(y)^k$. In other words, $x$ and $y$ have
%the same $k-$th decimal digit, $\forall k \in \mathds{N}^*$. And so $x=y$.
%\item $D(x,y)=D(y,x)$.
%\item Finally, the triangular inequality is obtained due to the fact that both
%$\delta$ and $\Delta(x,y)=|x-y|$ satisfy it.
%\end{itemize}
%\end{proof}


%The convergence of sequences according to $D$ is not the same than the usual
%convergence related to the Euclidian metric. For instance, if $x^n \to x$
%according to $D$, then necessarily the integral part of each $x^n$ is equal to
%the integral part of $x$ (at least after a given threshold), and the decimal
%part of $x^n$ corresponds to the one of $x$ ``as far as required''.
%To illustrate this fact, a comparison between $D$ and the Euclidian distance is
%given Figure \ref{fig:comparaison de distances}. These illustrations show that
%$D$ is richer and more refined than the Euclidian distance, and thus is more
%precise.


%\begin{figure}[t]
%\begin{center}
%  \subfigure[Function $x \to dist(x;1,234) $ on the interval
%$(0;5)$.]{\includegraphics[scale=.35]{DvsEuclidien.pdf}}\quad
%  \subfigure[Function $x \to dist(x;3) $ on the interval
%$(0;5)$.]{\includegraphics[scale=.35]{DvsEuclidien2.pdf}}
%\end{center}
%\caption{Comparison between $D$ (in blue) and the Euclidian distane (in green).}
%\label{fig:comparaison de distances}
%\end{figure}




%\subsubsection{The semiconjugacy}

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

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

%\begin{proof}
%$\varphi$ has been constructed in order to be continuous and onto.
%\end{proof}

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






%\subsection{Study of the chaotic iterations described as a real function}


%\begin{figure}[t]
%\begin{center}
%  \subfigure[ICs on the interval
%$(0,9;1)$.]{\includegraphics[scale=.35]{ICs09a1.pdf}}\quad
%  \subfigure[ICs on the interval
%$(0,7;1)$.]{\includegraphics[scale=.35]{ICs07a95.pdf}}\\
%  \subfigure[ICs on the interval
%$(0,5;1)$.]{\includegraphics[scale=.35]{ICs05a1.pdf}}\quad
%  \subfigure[ICs on the interval
%$(0;1)$]{\includegraphics[scale=.35]{ICs0a1.pdf}}
%\end{center}
%\caption{Representation of the chaotic iterations.}
%\label{fig:ICs}
%\end{figure}




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

%\begin{figure}[t]
%\begin{center}
%  \subfigure[ICs on the interval
%$(0;16)$.]{\includegraphics[scale=.3]{ICs0a16.pdf}}\quad
%  \subfigure[ICs on the interval 
%$(40;70)$.]{\includegraphics[scale=.45]{ICs40a70.pdf}}\quad
%\end{center}
%\caption{General aspect of the chaotic iterations.}
%\label{fig:ICs3}
%\end{figure}


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

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

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


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

%\begin{remark}
%Finally, chaotic iterations are elements of the large family of functions that
%are both chaotic and piecewise linear (like the tent map).
%\end{remark}



%\subsection{Comparison of the two metrics on $\big[ 0, 2^\mathsf{N} \big[$}

%The two propositions bellow allow to compare our two distances on $\big[ 0,
%2^\mathsf{N} \big[$:

%\begin{proposition}
%Id: $\left(~\big[ 0, 2^\mathsf{N} \big[,\Delta~\right) \to \left(~\big[ 0,
%2^\mathsf{N} \big[, D~\right)$ is not continuous. 
%\end{proposition}

%\begin{proof}
%The sequence $x^n = 1,999\hdots 999$ constituted by $n$ 9 as decimal part, is
%such that:
%\begin{itemize}
%\item $\Delta (x^n,2) \to 0.$
%\item But $D(x^n,2) \geqslant 1$, then $D(x^n,2)$ does not converge to 0.
%\end{itemize}

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



%A contrario:

%\begin{proposition}
%Id: $\left(~\big[ 0, 2^\mathsf{N} \big[,D~\right) \to \left(~\big[ 0,
%2^\mathsf{N} \big[, \Delta ~\right)$ is a continuous fonction. 
%\end{proposition}

%\begin{proof}
%If $D(x^n,x) \to 0$, then $D_e(x^n,x) = 0$ at least for $n$ larger than a given
%threshold, because $D_e$ only returns integers. So, after this threshold, the
%integral parts of all the $x^n$ are equal to the integral part of $x$. 

%Additionally, $D_s(x^n, x) \to 0$, then $\forall k \in \mathds{N}^*, \exists N_k
%\in \mathds{N}, n \geqslant N_k \Rightarrow D_s(x^n,x) \leqslant 10^{-k}$. This
%means that for all $k$, an index $N_k$ can be found such that, $\forall n
%\geqslant N_k$, all the $x^n$ have the same $k$ firsts digits, which are the
%digits of $x$. We can deduce the convergence $\Delta(x^n,x) \to 0$, and thus the
%result.
%\end{proof}

%The conclusion of these propositions is that the proposed metric is more precise
%than the Euclidian distance, that is:

%\begin{corollary}
%$D$ is finer than the Euclidian distance $\Delta$.
%\end{corollary}

%This corollary can be reformulated as follows:

%\begin{itemize}
%\item The topology produced by $\Delta$ is a subset of the topology produced by
%$D$.
%\item $D$ has more open sets than $\Delta$.
%\item It is harder to converge for the topology $\tau_D$ inherited by $D$, than
%to converge with the one inherited by $\Delta$, which is denoted here by
%$\tau_\Delta$.
%\end{itemize}


%\subsection{Chaos of the chaotic iterations on $\mathds{R}$}
%\label{chpt:Chaos des itérations chaotiques sur R}



%\subsubsection{Chaos according to Devaney}

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

%This result can be formulated as follows.

%\begin{theorem}
%\label{th:IC et topologie de l'ordre}
%The chaotic iterations $g$ on $\mathds{R}$ are chaotic according to the
%Devaney's formulation, when $\mathds{R}$ has his usual topology, which is the
%order topology.
%\end{theorem}

%Indeed this result is weaker than the theorem establishing the chaos for the
%finer topology $d$. However the Theorem \ref{th:IC et topologie de l'ordre}
%still remains important. Indeed, we have studied in our previous works a set
%different from the usual set of study ($\mathcal{X}$ instead of $\mathds{R}$),
%in order to be as close as possible from the computer: the properties of
%disorder proved theoretically will then be preserved when computing. However, we
%could wonder whether this change does not lead to a disorder of a lower quality.
%In other words, have we replaced a situation of a good disorder lost when
%computing, to another situation of a disorder preserved but of bad quality.
%Theorem \ref{th:IC et topologie de l'ordre} prove exactly the contrary.
% 






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



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}

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}
If $H$ is a secure cryptographic PRNG, then $X$ is a secure cryptographic
PRNG too.
\end{proposition}

\begin{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}

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{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}




\section{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}).   It simply consists  in replacing
the  {\it  xor-like} algorithm  by  another  cryptographically  secure prng.  In
practice, we suggest  to use the BBS algorithm~\cite{BBS}  which takes the form:
$$x_{n+1}=x_n^2~ mod~ M$$  where $M$ is the product of  two prime numbers. Those
prime numbers  need to be congruent  to 3 modulus  4. In practice, this  PRNG is
known to  be slow and  not efficient for  the generation of random  numbers. For
current  GPU   cards,  the  modulus   operation  is  the  most   time  consuming
operation. 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
pratice 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   undistinguishing  is  less  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 $M$ is required.

Currently this PRNG does not succeed to pass all the tests of TestU01.


\section{Conclusion}


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 Devenay.
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
20Gsample/s). This PRNG succeeds to pass the hardest batteries of TestU01.

In future  work we plan to  extend this work  for parallel PRNG for  clusters or
grid computing. We also plan to improve  the BBS version in order to succeed all
the tests of TestU01.



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