new

diff --git a/mabase.bib b/mabase.bib
index 3bba1776e0f6787eea53a70e223903a4eb85ed74..ae0a8d5456e6f870e9b5f9adbefeedc1f6eb2f02 100644 (file)
--- a/mabase.bib
+++ b/mabase.bib
@@ -4252,4 +4252,21 @@ booktitle =      "Proceedings of the {ACM}/{SIGDA} 17th International
   ISBN =       "978-1-60558-410-2",
   pages =      "63--72",
   URL =        "http://doi.acm.org/10.1145/1508128.1508139",
   ISBN =       "978-1-60558-410-2",
   pages =      "63--72",
   URL =        "http://doi.acm.org/10.1145/1508128.1508139",

-}
\ No newline at end of file
+}
+
+
+
+@InProceedings{Jenkins96,
+  author =     "Jenkins",
+  title =      "{ISAAC}",
+  booktitle =  "IWFSE: International Workshop on Fast Software
+                Encryption, LNCS",
+  year =       "1996",
+}
+
+@manual{Nvid10,
+ author = {Nvidia},
+ title = {Cuda cublas library},
+ year = {2010},
+ Note = {Version 3.1},
+ }

diff --git a/prng_gpu.tex b/prng_gpu.tex
index 82a4927989005c610369c27a847ea4c72a5d3c6a..bc5b3e55f5d7896f4374abcec6c8f56b0b4f3ee8 100644 (file)
--- a/prng_gpu.tex
+++ b/prng_gpu.tex
@@ -817,8 +817,8 @@ the larger the number of threads is,  the more local memory is used and the less
 branching  instructions are  used (if,  while, ...),  the better  performance is
 obtained  on  GPU.  So  with  algorithm  \ref{algo:seqCIprng}  presented in  the
 previous section, it is possible to  build a similar program which computes PRNG
 branching  instructions are  used (if,  while, ...),  the better  performance is
 obtained  on  GPU.  So  with  algorithm  \ref{algo:seqCIprng}  presented in  the
 previous section, it is possible to  build a similar program which computes PRNG

-on  GPU. In  the CUDA  [ref] environment,  threads have  a  local identificator,
-called \texttt{ThreadIdx} relative to the block containing them.
+on   GPU.  In  the   CUDA~\cite{Nvid10}  environment,   threads  have   a  local
+identificator, called \texttt{ThreadIdx} relative to the block containing them.

 
 
 \subsection{Naive version for GPU}
 
 
 \subsection{Naive version for GPU}


@@ -828,14 +828,14 @@ The principe consists in assigning the computation of a PRNG as in sequential to
 each thread  of the  GPU.  Of course,  it is  essential that the  three xor-like
 PRNGs  used for  our computation  have different  parameters. So  we  chose them
 randomly with  another PRNG. As the  initialisation is performed by  the CPU, we
 each thread  of the  GPU.  Of course,  it is  essential that the  three xor-like
 PRNGs  used for  our computation  have different  parameters. So  we  chose them
 randomly with  another PRNG. As the  initialisation is performed by  the CPU, we

-have chosen to use the ISAAC PRNG  [ref] to initalize all the parameters for the
-GPU version  of our  PRNG.  The  implementation of the  three xor-like  PRNGs is
-straightforward  as soon  as their  parameters have  been allocated  in  the GPU
-memory. Each xor-like  PRNGs used works with an internal  number $x$ which keeps
-the last generated random numbers. Other internal variables are also used by the
-xor-like PRNGs. More  precisely, the implementation of the  xor128, the xorshift
-and  the xorwow  respectively  require 4,  5  and 6  unsigned  long as  internal
-variables.
+have  chosen  to  use  the  ISAAC  PRNG~\ref{Jenkins96}  to  initalize  all  the
+parameters for  the GPU version  of our PRNG.   The implementation of  the three
+xor-like  PRNGs  is  straightforward  as  soon as  their  parameters  have  been
+allocated in  the GPU memory.  Each xor-like PRNGs  used works with  an internal
+number  $x$  which keeps  the  last  generated  random numbers.  Other  internal
+variables  are   also  used   by  the  xor-like   PRNGs.  More   precisely,  the
+implementation of the  xor128, the xorshift and the  xorwow respectively require
+4, 5 and 6 unsigned long as internal variables.

 
 \begin{algorithm}
 
 
 \begin{algorithm}
 


@@ -954,8 +954,20 @@ Devaney's formulation of a chaotic behavior.
 \section{Experiments}
 \label{sec:experiments}
 
 \section{Experiments}
 \label{sec:experiments}
 

-Different experiments have been performed in order to measure the generation
-speed. In Figure~\ref{fig:time_gpu} we compare the number of random numbers generated per second.
+Different experiments  have been  performed in order  to measure  the generation
+speed. We have used  a computer equiped with Tesla C1060 NVidia  GPU card and an
+Intel Xeon E5530 cadenced at 2.40 GHz for our experiments.
+
+In Figure~\ref{fig:time_gpu}  we compare the number of  random numbers generated
+per second.   In order  to obtain the  optimal number  we remove the  storage of
+random numbers  in the GPU memory. This  step is time consumming  and slows down
+the random number  generation.  Moreover, if you are  interested by applications
+that consome  random number directly when  they are generated,  their storage is
+completely useless. In this figure we can see that when the number of threads is
+greater than approximately  30,000 upto 5 millions the  number of random numbers
+generated per second is almost constant.   With the naive version, it is between
+2.5  and  3GSample/s.   With the  optimized  version,  it  is almost  equals  to
+20GSample/s.

 
 \begin{figure}[htbp]
 \begin{center}
 
 \begin{figure}[htbp]
 \begin{center}