X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/prng_gpu.git/blobdiff_plain/716f5cce1f0b4cef3c6dfe87f218f6ad04d0ab97..0067ddaf35f9842d01238d6d0b1161e791e66f2e:/prng_gpu.tex?ds=inline diff --git a/prng_gpu.tex b/prng_gpu.tex index 6ebbcd9..6e063fd 100644 --- a/prng_gpu.tex +++ b/prng_gpu.tex @@ -7,6 +7,8 @@ \usepackage{amscd} \usepackage{moreverb} \usepackage{commath} +\usepackage{algorithm2e} +\usepackage{listings} \usepackage[standard]{ntheorem} % Pour mathds : les ensembles IR, IN, etc. @@ -32,441 +34,1657 @@ \newcommand{\alert}[1]{\begin{color}{blue}\textit{#1}\end{color}} -\title{Efficient generation of pseudo random numbers based on chaotic iterations on GPU} +\title{Efficient Generation of Pseudo-Random Numbers based on Chaotic Iterations +on GPU} \begin{document} + +\author{Jacques M. Bahi, Rapha\"{e}l Couturier, and Christophe +Guyeux, Pierre-Cyrille Heam\thanks{Authors in alphabetic order}} + \maketitle \begin{abstract} -This is the abstract +In this paper we present a new pseudo-random numbers generator (PRNG) on +graphics processing units (GPU). This PRNG is based on chaotic iterations. it +is proven to be chaotic in the Devanay's formulation. We propose an efficient +implementation for GPU which succeeds to the {\it BigCrush}, the hardest +batteries of test of TestU01. Experimentations show that this PRNG can generate +about 20 billions of random numbers per second on Tesla C1060 and NVidia GTX280 +cards. + + \end{abstract} \section{Introduction} -Interet des itérations chaotiques pour générer des nombre alea\\ -Interet de générer des nombres alea sur GPU -... +Random numbers are used in many scientific applications and simulations. On +finite state machines, as computers, it is not possible to generate random +numbers but only pseudo-random numbers. In practice, a good pseudo-random numbers +generator (PRNG) needs to verify some features to be used by scientists. It is +important to be able to generate pseudo-random numbers efficiently, the +generation needs to be reproducible and a PRNG needs to satisfy many usual +statistical properties. Finally, from our point a view, it is essential to prove +that a PRNG is chaotic. Concerning the statistical tests, TestU01 is the +best-known public-domain statistical testing package. So we use it for all our +PRNGs, especially the {\it BigCrush} which provides the largest serie of tests. +Concerning the chaotic properties, Devaney~\cite{Devaney} proposed a common +mathematical formulation of chaotic dynamical systems. + +In a previous work~\cite{bgw09:ip} we have proposed a new familly of chaotic +PRNG based on chaotic iterations. We have proven that these PRNGs are +chaotic in the Devaney's sense. In this paper we propose a faster version which +is also proven to be chaotic. + +Although graphics processing units (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 pseudo-random +numbers inside a GPU when a scientific application runs in a GPU. That is why we +also provide an efficient PRNG for GPU respecting based on IC. Such devices +allows us to generated almost 20 billions of random numbers per second. + +In order to establish that our PRNGs are chaotic according to the Devaney's +formulation, we extend what we have proposed in~\cite{guyeux10}. + +The rest of this paper is organised as follows. In Section~\ref{section:related + works} we review some GPU implementions of PRNG. Section~\ref{section:BASIC + RECALLS} gives some basic recalls on Devanay's formation of chaos and chaotic +iterations. In Section~\ref{sec:pseudo-random} the proof of chaos of our PRNGs +is studied. Section~\ref{sec:efficient prng} presents an efficient +implementation of our chaotic PRNG on a CPU. Section~\ref{sec:efficient prng + gpu} describes the GPU implementation of our chaotic PRNG. In +Section~\ref{sec:experiments} some experimentations are presented. + Finally, we give a conclusion and some perspectives. + + + + +\section{Related works on GPU based PRNGs} +\label{section:related works} +In the litterature many authors have work on defining GPU based PRNGs. We do not +want to be exhaustive and we just give the most significant works from our point +of view. When authors mention the number of random numbers generated per second +we mention it. We consider that a million numbers per second corresponds to +1MSample/s and than a billion numbers per second corresponds to 1GSample/s. + +In \cite{Pang:2008:cec}, the authors define a PRNG based on cellular automata +which does not require high precision integer arithmetics nor bitwise +operations. There is no mention of statistical tests nor proof that this PRNG is +chaotic. Concerning the speed of generation, they can generate about +3.2MSample/s on a GeForce 7800 GTX GPU (which is quite old now). + +In \cite{ZRKB10}, the authors propose different versions of efficient GPU PRNGs +based on Lagged Fibonacci, Hybrid Taus 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} test of TestU01. There is +no mention that their PRNGs have chaos mathematical properties. + + +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 architeture. 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 $f : +\mathcal{X} \rightarrow \mathcal{X}$. -\section{Chaotic iterations} +\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} -Présentation des itérations chaotiques +\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} -\section{The relativity of disorder} -\label{sec:de la relativité du désordre} +The chaos property is strongly linked to the notion of ``sensitivity'', defined +on a metric space $(\mathcal{X},d)$ by: -\subsection{Impact of the topology's finenesse} +\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 $. -Let us firstly introduce the following notations. +$\delta$ is called the \emph{constant of sensitivity} of $f$. +\end{definition} -\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} +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. -\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 $\tau'$. +\subsection{Chaotic Iterations} +\label{sec:chaotic iterations} -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 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 $k0$. \medskip \begin{itemize} -\item whose integral part $e$ is $\displaystyle{\sum_{k=0}^9 2^{9-k} E_k}$, that is, the binary digits of $e$ are $E_0 ~ E_1 ~ \hdots ~ E_9$. -\item whose decimal part $s$ is equal to $s = 0,S^0~ S^1~ S^2~ \hdots = \sum_{k=1}^{+\infty} 10^{-k} S^{k-1}.$ +\item 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} -\end{definition} +\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, -$\varphi$ 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{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{definition} -\label{def:e et s} -Let $x \in \big[ 0, 2^{10} \big[$ and: +\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 $e_0, \hdots, e_9$ 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: -$\displaystyle{x = \lfloor x \rfloor + \sum_{k=0}^{+\infty} s^k 10^{-k-1}}$. + \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} -$e$ and $s$ are thus defined as follows: -$$ -\begin{array}{cccl} -e: & \big[ 0, 2^{10} \big[ & \longrightarrow & \mathds{B}^{10} \\ - & x & \longmapsto & (e_0, \hdots, e_9) -\end{array} -$$ -\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} +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 are now able to define the function $g$, whose goal is to translate the chaotic iterations $\Go$ on an interval of $\mathds{R}$. +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 $t>32); + x = x^(unsigned int)(t3>>32); + x = x^(unsigned int)t2; + x = x^(unsigned int)(t1>>32); + x = x^(unsigned int)t3; + return x; +} +\end{lstlisting} + + + + + +In listing~\ref{algo:seqCIprng} a sequential version of our chaotic iterations +based PRNG is presented. The xor operator is represented by \textasciicircum. +This function uses three classical 64-bits PRNG: the \texttt{xorshift}, the +\texttt{xor128} and the \texttt{xorwow}. In the following, we call them +xor-like PRNGSs. These three PRNGs are presented in~\cite{Marsaglia2003}. As +each xor-like PRNG used works with 64-bits and as our PRNG works with 32-bits, +the use of \texttt{(unsigned int)} selects the 32 least significant bits whereas +\texttt{(unsigned int)(t3$>>$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]\; +} -\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} +\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 !!!} -\medskip +\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} -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: +\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{A cryptographically secure prng for GPU} + +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{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{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{figure}[htbp] \begin{center} -$\displaystyle{D_e(E,\check{E}) = \sum_{k=0}^\mathsf{9} \delta (E_k, \check{E}_k)}$, ~~and~ $\displaystyle{D_s(S,\check{S}) = \sum_{k = 1}^\infty \dfrac{|S^k-\check{S}^k|}{10^k}}$. + \includegraphics[scale=.7]{curve_time_xorlike_gpu.pdf} \end{center} -\end{definition} - -\begin{proposition} -$D$ is a distance on $\big[ 0, 2^{10} \big[$. -\end{proposition} +\caption{Number of random numbers generated per second with the xorlike based PRNG} +\label{fig:time_xorlike_gpu} +\end{figure} -\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 comparison, Listing~\ref{algo:seqCIprng} allows us to generate about +138MSample/s with only one core of the Xeon E5530. -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. +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}[t] +\begin{figure}[htbp] \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}} + \includegraphics[scale=.7]{curve_time_bbs_gpu.pdf} \end{center} -\caption{Comparison between $D$ (in blue) and the Euclidian distane (in green).} -\label{fig:comparaison de distances} +\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 pseudo-random 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} -\subsubsection{The semiconjugacy} -It is now possible to define a topological semiconjugacy between $\mathcal{X}$ and an interval of $\mathds{R}$: +%% Mettre ici la preuve de PCH -\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} +%\section{The relativity of disorder} +%\label{sec:de la relativité du désordre} -\begin{proof} -$\varphi$ has been constructed in order to be continuous and onto. -\end{proof} +%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}. -In other words, $\mathcal{X}$ is approximately equal to $\big[ 0, 2^\mathsf{N} \big[$. +%\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 $\tau'$. -\subsection{Study of the chaotic iterations described as a real function} +%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 $(\mathcal{X}_\tau,f)$. -\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} +%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} -\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} +%\subsection{A given system can always be claimed as chaotic} -\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} +%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} -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 $\left\{ \dfrac{n}{10} ~\big/~ n \in \llbracket 0;2^{10}\times 10\rrbracket \right\}$. +%\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. -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} +%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} -\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{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} -\subsection{Comparison of the two metrics on $\big[ 0, 2^\mathsf{N} \big[$} +%\begin{proof} +%Let us prove it by contradiction, assuming that $\left(\X_{\tau_\infty}, +%f\right)$ is both transitive and regular. -The two propositions bellow allow to compare our two distances on $\big[ 0, 2^\mathsf{N} \big[$: +%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{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} +%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} -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} +%\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} -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} +%\subsection{Chaos of the chaotic iterations on $\mathds{R}$} +%\label{chpt:Chaos des itérations chaotiques sur R} -\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: +%\subsubsection{Chaos according to Devaney} -\begin{corollary} -$D$ is finer than the Euclidian distance $\Delta$. -\end{corollary} +%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 corollary can be reformulated as follows: +%This result can be formulated 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} +%\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. +% -\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. +\section{Security Analysis} -\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. - +In this section the concatenation of two strings $u$ and $v$ is classically +denoted by $uv$. +In a cryptographic context, a pseudo-random 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. -\section{Efficient prng based on chaotic iterations} +\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} -On parle du séquentiel avec des nombres 64 bits\\ +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 +pseudo-random 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. -Faire le lien avec le paragraphe précédent (je considère que la stratégie s'appelle $S^i$\\ +\begin{proposition} +If $H$ is a secure cryptographic PRNG, then $X$ is a secure cryptographic +PRNG too. +\end{proposition} -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 $S^i$ contains all the bits for which the negation is -achieved out. Then instead of applying the negation on these bits we can simply -apply the xor operator between the current number and the strategy $S^i$. In -order to obtain the strategy we also use a classical PRNG. +\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} -\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$>>$32);\\ - x = x\textasciicircum (unsigned int)(t3$>>$32);\\ - x = x\textasciicircum (unsigned int)t2;\\ - x = x\textasciicircum (unsigned int)(t1$>>$32);\\ - x = x\textasciicircum (unsigned int)t3;\\ - return x;\\ -\} -\end{minipage} -} -\end{center} -\caption{sequential Chaotic Iteration PRNG} -\label{algo:seqCIprng} -\end{figure} -In Figure~\ref{algo:seqCIprng} a sequential version of our chaotic iterations -based PRNG is presented. This version uses three classical 64 bits PRNG: the -\texttt{xorshift}, the \texttt{xor128} and the \texttt{xorwow}. These three -PRNGs are presented in~\cite{Marsaglia2003}. As each PRNG used works with -64-bits and as our PRNG works with 32 bits, the use of \texttt{(unsigned int)} -selects the 32 least significant bits whereas \texttt{(unsigned int)(t3$>>$32)} -selects the 32 most significants bits of the variable \texttt{t}. This version -sucesses the BigCrush of the TestU01 battery [P. L’ecuyer and - R. Simard. Testu01]. -\section{Efficient prng based on chaotic iterations on GPU} -On parle du passage du sequentiel au GPU -\section{Experiments} +\section{Conclusion} -On passe le BigCrush\\ -On donne des temps de générations sur GPU/CPU\\ -On donne des temps de générations de nombre sur GPU puis on rappatrie sur CPU / CPU ? bof bof, on verra +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. -\section{Conclusion} -\bibliographystyle{plain} +An efficient implementation on GPU based on a xor-like PRNG allows us to +generate a huge number of pseudo-random 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. + + + +\bibliographystyle{plain} \bibliography{mabase} \end{document}