speed is a strong requirement that is usually attained by using
parallel architectures. In that case, a recurrent problem is that a
deflation of the statistical qualities is often reported, when the
-parallelization of a good PRNG is realized. In some fields such 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 deflation of the statistical qualities is often
-reported, when the parallelization of a good PRNG is realized. This
+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
+achieve both speed and randomness. On the other hand, speed is not
+the main requirement in cryptography: the most important point is to
define \emph{secure} generators 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
Finally, a small part of the community working in this domain focuses on a
third requirement, that is to define chaotic generators~\cite{kellert1994wake, Wu20051195,gleick2011chaos}.
-The main idea is to take benefits from a chaotic dynamical system to obtain a
+The main idea is to benefits from a chaotic dynamical system to obtain a
generator that is unpredictable, disordered, sensible to its seed, or in other word chaotic.
-Their desire is to map a given chaotic dynamics into a sequence that seems random
+These scientists' 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.
stringent statistical evaluation of a sequence claimed as random.
This battery can be found in the well-known TestU01 package~\cite{LEcuyerS07}\index{TestU01}.
More precisely, each time we performed a test on a PRNG, we ran it
-twice in order to observe if all $p-$values are inside [0.01, 0.99]. In
+twice in order to observe if all $p-$values were inside [0.01, 0.99]. In
fact, we observed that few $p-$values (less than ten) are sometimes
outside this interval but inside [0.001, 0.999], so that is why a
-second run allows us to confirm that the values outside are not for
+second run has allowed us to confirm that the values outside are not for
the same test. With this approach all our PRNGs pass the {\it
BigCrush} successfully and all $p-$values are at least once inside
[0.01, 0.99].
The remainder of this chapter is organized as follows.
Basic definitions and terminologies about both topological chaos
and chaotic iterations are provided in the next section. Various
-chaotic iterations based pseudorandom number generators are then
+chaotic iterations based on pseudorandom number generators are then
presented in Section~\ref{sec:efficient PRNG}. They encompass
naive and improved efficient generators for CPU and for GPU.
These generators are finally experimented in Section~\ref{sec:experiments}.
-\section{Basic Recalls}
+\section{Basic Remindees}
\label{section:BASIC RECALLS}
This section is devoted to basic definitions and terminologies in the fields of
\subsection{A Short Presentation of Chaos}
-Chaos theory studies the behavior of dynamical systems that are perfectly predictable, yet appear to be wildly amorphous and without meaningful.
+Chaos theory studies the behavior of dynamical systems that are perfectly predictable, yet appear to be wildly amorphous and meaningless.
Chaotic systems\index{chaotic systems} are highly sensitive to initial conditions,
which is popularly referred to as the butterfly effect.
In other words, small differences in initial conditions (such as those due to rounding errors in numerical computation) yield widely diverging outcomes,
rendering long-term prediction impossible in general \cite{kellert1994wake}. This happens even though these systems are deterministic, meaning that their future behavior is fully determined by their initial conditions, with no random elements involved \cite{kellert1994wake}. That is, the deterministic nature of these systems does not make them predictable \cite{kellert1994wake,Werndl01032009}. This behavior is known as deterministic chaos, or simply chaos. It has been well-studied in mathematics and
-physics, leading among other things to the well-established definition of Devaney
-recalled thereafter.
+physics, leading among other things to the well-established definition of Devaney which can be found next.
\overline{P}=\mathcal{X} .
\end{equation}
-Density of periodic orbits means that every point in the space is approached arbitrarily closely by periodic orbits. Topologically mixing systems failing this condition may not display sensitivity to initial conditions presented below, and hence may not be chaotic.
+The density of periodic orbits means that every point in space is closely approached by periodic orbits in an arbitrary way. Topologically mixing systems failing this condition may not display sensitivity to initial conditions presented below, and hence may not be chaotic.
\item Sensitive dependence on initial conditions\index{sensitive dependence on initial conditions}:
$\exists \varepsilon>0,$ $\forall x \in \mathcal{X},$ $\forall \delta >0,$ $\exists y \in \mathcal{X},$ $\exists n \in \mathbb{N},$ $d(x,y)<\delta$ and $d\left(f^n(x),f^n(y)\right) \geqslant \varepsilon.$
-Intuitively, a map possesses sensitive dependence on initial conditions if there exist points arbitrarily close to $x$ that eventually separate from $x$ by at least $\varepsilon$ under iteration of $f$. Not all points near $x$ need eventually separate from $x$ under iteration, but there must be at least one such point in every neighborhood of $x$. If a map possesses sensitive dependence on initial conditions, then for all practical purposes, the dynamics of the map defy numerical computation. Small errors in computation that are introduced by round-off may become magnified upon iteration. The results of numerical computation of an orbit, no matter how accurate, may bear no resemblance whatsoever with the real orbit.
+Intuitively, a map possesses sensitive dependence on initial conditions if there exist points arbitrarily close to $x$ that eventually separate from $x$ by at least $\varepsilon$ under the iteration of $f$. Not all points near $x$ need eventually separate from $x$ under iteration, but there must be at least one such point in every neighborhood of $x$. If a map possesses sensitive dependence on initial conditions, then for all practical purposes, the dynamics of the map defy numerical computation. Small errors in computation that are introduced by round-off may become magnified upon iteration. The results of numerical computation of an orbit, no matter how accurate, may bear no resemblance whatsoever with the real orbit.
\end{itemize}
\end{definition}
\subsection{Efficient PRNGs based on Chaotic Iterations on GPU}
\label{sec:efficient PRNG gpu}
-In order to take benefits from the computing power of GPU, a program
+In order to benefit from the computing power of GPU, a program
needs to have independent blocks of threads that can be computed
simultaneously. In general, the larger the number of threads is, the
more local memory is used, and the less branching instructions are
-used (if, while, ...), the better the performances on GPU is.
+used (if, while, ...) and so, the better the performances on GPU are.
Obviously, having these requirements in mind, it is possible to build
a program similar to the one presented in Listing
\ref{algo:seqCIPRNG}, which computes pseudorandom numbers on GPU. To
In a given thread, these parameters are
randomly picked from another PRNGs.
The initialization stage is performed by the CPU.
-To do it, the ISAAC PRNG~\cite{Jenkins96} is used to set all the
+To do this, the ISAAC PRNG~\cite{Jenkins96} is used to set all the
parameters embedded into each thread.
The implementation of the three
inside a kernel is limited (\emph{i.e.}, the variable \texttt{n} in
algorithm~\ref{algo:gpu_kernel}). For instance, if $100,000$ threads are used and
if $n=100$\footnote{in fact, we need to add the initial seed (a 32-bits number)},
-then the memory required to store all of the internals variables of both the xor-like
+then the memory required to store all of the internal variables of both the xor-like
PRNGs\footnote{we multiply this number by $2$ in order to count 32-bits numbers}
and the pseudorandom numbers generated by our PRNG, is equal to $100,000\times ((4+5+6)\times
2+(1+100))=1,310,000$ 32-bits numbers, that is, approximately $52$Mb.
(obtained by a bitwise exclusive or between a value provided by a xor-like() call
and two values previously obtained by two other threads).
To be certain that such iterations corresponds to the chaotic one recalled at the
-end of the Section~\ref{sec:dev},
+end of Section~\ref{sec:dev},
we must guarantee that this dynamical system iterates on the space
$\mathcal{X} =\mathds{B}^\mathsf{N} \times \mathcal{P}\left(\llbracket 1, 2^\mathsf{N} \rrbracket\right)^\mathds{N}$.
The left term $x$ obviously belongs to $\mathds{B}^ \mathsf{N}$.
Such a result is obvious, as for the xor-like(), all the
integers belonging into its interval of definition can occur at each iteration, and thus the
last $t$ respects the requirement. Furthermore, it is possible to
-prove by an immediate mathematical induction that, supposes that the initial $x$
+prove by an immediate mathematical induction that, supposing that the initial $x$
is uniformly distributed, %(it is provided by a cryptographically secure PRNG),
the two other stored values shmem[o1] and shmem[o2] are uniformly distributed too,
(this is the induction hypothesis), and thus the next $x$ is finally uniformly distributed.
into the GPU memory has been removed. This step is time consuming and slows down the numbers
generation. Moreover this storage is completely
useless, in case of applications that consume the pseudorandom
-numbers directly after generation. We can see that when the number of threads is greater
+numbers directly after they have been generated. We can see that when the number of threads is greater
than approximately 30,000 and lower than 5 million, the number of pseudorandom numbers generated
per second is almost constant. With the naive version, this value ranges from 2.5 to
3GSamples/s. With the optimized version, it is approximately equal to
