X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/prng_gpu.git/blobdiff_plain/1ef8a2c0156684f00384e536ee77170afcca38d4..01d6ea25639436faca9b43ab4c739ec2f86a24c7:/prng_gpu.tex

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--- a/prng_gpu.tex
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@@ -1,5 +1,83 @@
 \documentclass{article}
+\usepackage[utf8]{inputenc}
+\usepackage[T1]{fontenc}
+\usepackage{fullpage}
+\usepackage{fancybox}
+\usepackage{amsmath}
+\usepackage{moreverb}
+\usepackage{commath}
+
+\title{Efficient generation of pseudo random numbers based on chaotic iterations on GPU}
 \begin{document}
-qdsqsd
-qsdqsd
+\maketitle
+
+\begin{abstract}
+This is the abstract
+\end{abstract}
+
+\section{Introduction}
+
+Interet des itérations chaotiques pour générer des nombre alea\\
+Interet de générer des nombres alea sur GPU
+...
+
+\section{Chaotic iterations}
+
+Présentation des itérations chaotiques
+
+\section{Efficient prng based on chaotic iterations}
+
+On parle du séquentiel avec des nombres 64 bits\\
+
+Faire le lien avec le paragraphe précédent (je considère que la stratégie s'appelle $S^i$\\
+
+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 $S^i$.
+
+\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\^\ (unsigned int)t;\\
+  x = x\^\ (unsigned int)(t2$>>$32);\\
+  x = x\^\ (unsigned int)(t3$>>$32);\\
+  x = x\^\ (unsigned int)t2;\\
+  x = x\^\ (unsigned int)(t$>>$32);\\
+  x = x\^\ (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  xorshift, the
+xor128 and the xorwow. These three PRNGs are presented in~\cite{Marsaglia2003}.
+
+\section{Efficient prng based on chaotic iterations on GPU}
+
+On parle du passage du sequentiel au GPU
+
+\section{Experiments}
+
+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
+
+\section{Lyapunov}
+
+\section{Conclusion}
+\bibliographystyle{plain}
+\bibliography{mabase}
 \end{document}