ajout courbe

[prng_gpu.git] / prng_gpu.tex

diff --git a/prng_gpu.tex b/prng_gpu.tex
index fa71e5b1b415daeeffae3cf8f1c37a23acd43c7b..d1fb7a67a7f66791e989f8b6111d6aa4b3c52ec0 100644 (file)
--- a/prng_gpu.tex
+++ b/prng_gpu.tex
@@ -674,7 +674,7 @@ achieved out. Then in order to apply  the negation on these bits we can simply
 apply the  xor operator between  the current number  and the strategy. In
 order to obtain the strategy we also use a classical PRNG.
 
-Here  is an  example with  16-bits numbers  showing how  the bit  operations are
+Here  is an  example with  16-bits numbers  showing how  the bitwise  operations are
 applied.  Suppose  that $x$ and the  strategy $S^i$ are defined  in binary mode.
 Then the following table shows the result of $x$ xor $S^i$.
 $$
@@ -738,17 +738,18 @@ unsigned int CIprng() {
 
 
 
-In listing~\ref{algo:seqCIprng}  a sequential  version of our  chaotic iterations
-based PRNG  is presented. The xor operator is represented by \textasciicircum. This function  uses three classical  64-bits PRNG: the
-\texttt{xorshift},   the  \texttt{xor128}  and   the  \texttt{xorwow}.   In  the
-following,  we  call them  xor-like  PRNGSs.   These  three PRNGs  are  presented
-in~\cite{Marsaglia2003}.  As each xor-like  PRNG used works with 64-bits  and as our PRNG
-works  with 32-bits, the  use of  \texttt{(unsigned int)}  selects the  32 least
-significant bits  whereas \texttt{(unsigned int)(t3$>>$32)} selects  the 32 most
-significants  bits of the  variable \texttt{t}.  So to  produce a  random number
-realizes 6  xor operations with  6 32-bits numbers  produced by 3  64-bits PRNG.
-This version  successes the  BigCrush of the  TestU01 battery [P.   L’ecuyer and
-  R. Simard. Testu01].
+In listing~\ref{algo:seqCIprng}  a sequential version of  our chaotic iterations
+based   PRNG    is   presented.   The    xor   operator   is    represented   by
+\textasciicircum.  This   function  uses  three  classical   64-bits  PRNG:  the
+\texttt{xorshift},  the   \texttt{xor128}  and  the   \texttt{xorwow}.   In  the
+following,  we call  them  xor-like  PRNGSs.  These  three  PRNGs are  presented
+in~\cite{Marsaglia2003}.  As each  xor-like PRNG used works with  64-bits and as
+our PRNG works  with 32-bits, the use of \texttt{(unsigned  int)} selects the 32
+least significant bits whereas  \texttt{(unsigned int)(t3$>>$32)} selects the 32
+most  significants bits  of the  variable \texttt{t}.   So to  produce  a random
+number realizes  6 xor operations with  6 32-bits numbers produced  by 3 64-bits
+PRNG.  This version successes the  BigCrush of the TestU01 battery [P.  L’ecuyer
+  and R. Simard. Testu01].
 
 \section{Efficient prng based on chaotic iterations on GPU}
 
@@ -835,6 +836,8 @@ which represent the indexes of the  other threads for which the results are used
 by the  current thread. In  the algorithm, we  consider that a  64-bits xor-like
 PRNG is used, that is why both 32-bits parts are used.
 
+This version also succeed to the BigCrush batteries of tests.
+
 \begin{algorithm}
 
 \KwIn{InternalVarXorLikeArray: array with internal variables of 1 xor-like PRNGs in global memory\;
@@ -863,10 +866,20 @@ tab1, tab2: Arrays containing permutations of size permutation\_size\;}
 \caption{main kernel for the chaotic iterations based PRNG GPU efficient version}
 \label{algo:gpu_kernel2}
 \end{algorithm}
+
+
+
 \section{Experiments}
 
 Differents experiments have been performed in order to measure the generation speed.
+\begin{figure}[t]
+\begin{center}
+  \includegraphics[scale=.5]{curve_time_gpu.pdf}
 
+\end{center}
+\caption{Number of random numbers generated per second}
+\label{fig:time_naive_gpu}
+\end{figure}
 
 First of all we have compared the time to generate X random numbers with both the CPU version and the GPU version.