X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/book_gpu.git/blobdiff_plain/4c5e6c1725249ae02b156277ef750a43f5d6144b..5dadaa83fd77112c7c6ace09f8ec840af2109d3e:/BookGPU/Chapters/chapter18/ch18.tex?ds=inline

diff --git a/BookGPU/Chapters/chapter18/ch18.tex b/BookGPU/Chapters/chapter18/ch18.tex
index fb3b560..d72e9a3 100755
--- a/BookGPU/Chapters/chapter18/ch18.tex
+++ b/BookGPU/Chapters/chapter18/ch18.tex
@@ -1,5 +1,5 @@
-\chapterauthor{Raphaël Couturier}{Femto-ST Institute, University of Franche-Comt\'{e}}
-\chapterauthor{Christophe Guyeux}{Femto-ST Institute, University of Franche-Comt\'{e}}
+\chapterauthor{Raphaël Couturier and Christophe Guyeux}{Femto-ST Institute, University of Franche-Comte, France}
+%\chapterauthor{Christophe Guyeux}{Femto-ST Institute, University of Franche-Comt\'{e}}
 
 
 \chapter{Pseudorandom Number Generator on GPU}
@@ -492,10 +492,15 @@ These  experiments allow  us  to conclude  that  it  is possible  to
 generate a very large quantity of pseudorandom  numbers statistically perfect with the  xor-like version.
 
 
+\section{Summary}
 
 
-
-
+In this chapter,  a PRNG based on chaotic iterations is  presented.   It is proven to be
+chaotic according to Devaney.   Efficient implementations  on GPU
+using xor-like PRNGs  as input generators have shown that  a very large quantity
+of pseudorandom  numbers can be  generated per second (about  20Gsamples/s on a Tesla C1060), and
+that these proposed PRNGs succeed to pass the hardest battery in TestU01, namely
+the BigCrush.