X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/book_gpu.git/blobdiff_plain/1874c46934f4ba7e8c2013d3829f65309456d292..b1fd489e34a8d46d286a0d271c38cbfb442f511f:/BookGPU/Chapters/chapter18/ch18.tex?ds=inline

diff --git a/BookGPU/Chapters/chapter18/ch18.tex b/BookGPU/Chapters/chapter18/ch18.tex
index 7c2a393..5f0df4b 100755
--- a/BookGPU/Chapters/chapter18/ch18.tex
+++ b/BookGPU/Chapters/chapter18/ch18.tex
@@ -13,14 +13,14 @@ generated by either a deterministic and reproducible algorithm called
 a pseudorandom number generator (PRNG)\index{PRNG}, or by a physical nondeterministic 
 process having all the characteristics of a random noise, called a truly random number
 generator (TRNG). In this chapter, we focus on
-reproducible generators, useful for instance in MonteCarlo-based
+reproducible generators, useful for instance in Monte Carlo-based
 simulators.  These domains need PRNGs that are statistically
 irreproachable.  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
-is why adhoc PRNGs for each possible architecture must be found to
+is why ad hoc PRNGs for each possible architecture must be found 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
@@ -81,7 +81,7 @@ naive and improved efficient generators for CPU and for GPU.
 These generators are finally experimented in Section~\ref{sec:experiments}.
 
 
-\section{Basic remindees}
+\section{Basic reminders}
 \label{section:BASIC RECALLS}
 
 This section is devoted to basic definitions and terminologies in the fields of
@@ -252,7 +252,7 @@ satisfies the Devaney's definition of chaos.
 
 In Listing~\ref{algo:seqCIPRNG} a sequential  version of the proposed PRNG based
 on  chaotic  iterations  is  presented, which extends the generator family 
-formerly presented in~\cite{bgw09:ip,guyeux10}.   The xor  operator  is  represented  by
+formerly presented in~\cite{bgw09:ip,guyeux10}.   The \texttt{xor}  operator  is  represented  by
 \textasciicircum.  This function uses  three classical 64-bit PRNGs, namely the
 \texttt{xorshift},         the          \texttt{xor128},         and         the
 \texttt{xorwow}~\cite{Marsaglia2003}.  In the following, we call them ``xor-like
@@ -391,7 +391,7 @@ array\_comb1, array\_comb2: Arrays containing combinations of size combination\_
   store internal variables in InternalVarXorLikeArray[threadIdx]\;
 }
 \end{small}
-\caption{Main kernel for the chaotic iterations based PRNG GPU efficient
+\caption{main kernel for the chaotic iterations based PRNG GPU efficient
 version\label{IR}}
 \label{algo:gpu_kernel2} 
 \end{algorithm}