new

[book_gpu.git] / BookGPU / Chapters / chapter18 / ch18.tex

diff --git a/BookGPU/Chapters/chapter18/ch18.tex b/BookGPU/Chapters/chapter18/ch18.tex
index fb3b56028429901bccf3e06fc145eebdbafba959..d72e9a352c8c1ab6a0080d03bbec43b3a5c4f143 100755 (executable)
--- 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}
 
 
 \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.
 
 
 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.