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 used by the
+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.
store internal variables in InternalVarXorLikeArray[threadId]\;
}
-\caption{PRNG with chaotic functions}
-\label{main kernel for the chaotic iterations based PRNG GPU version}
+\caption{main kernel for the chaotic iterations based PRNG GPU version}
+\label{algo:gpu_kernel}
\end{algorithm}
+According to the available memory in the GPU and the number of threads used
+simultenaously, the number of random numbers that a thread can generate inside a
+kernel is limited, i.e. the variable \texttt{n} in
+algorithm~\ref{algo:gpu_kernel}. For example, if $100,000$ threads are used and
+if $n=100$\footnote{in fact, we need to add the initial seed (a 32-bits number)}
+then the memory required to store internals variables of xor-like
+PRNGs\footnote{we multiply this number by $2$ in order to count 32-bits numbers}
+and random number of our PRNG is equals to $100,000\times ((4+5+6)\times
+2+(1+100))=1,310,000$ 32-bits numbers, i.e. about $52$Mb.
+
\section{Experiments}
On passe le BigCrush\\
