X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/prng_gpu.git/blobdiff_plain/b64600704673c37dbb08701c0b811f65d159db34..3139e5ae44965e6ea8f37d04f84687189d4b5c42:/prng_gpu.tex?ds=inline

diff --git a/prng_gpu.tex b/prng_gpu.tex
index 00991e9..8d76043 100644
--- a/prng_gpu.tex
+++ b/prng_gpu.tex
@@ -758,12 +758,18 @@ 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
-on  GPU. 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 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
+on  GPU. 
+
+
+\subsection{Naive version}
+
+From the CPU version, it is possible  to obtain a quite similar version for GPU.
+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
+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
@@ -802,6 +808,23 @@ and  random  number of  our  PRNG  is  equals to  $100,000\times  ((4+5+6)\times
 All the  tests performed  to pass the  BigCrush of TestU01  succeeded. Different
 number of threads have been tested upto $10$ millions.
 
+\begin{remark}
+Algorithm~\ref{algo:gpu_kernel}  has  the  advantage to  manipulate  independent
+PRNGs, so this version is easily usable on a cluster of computer. The only thing
+to ensure is to use a single ISAAC PRNG. For this, a simple solution consists in
+using a master node for the initialization which computes the initial parameters
+for all the differents nodes involves in the computation.
+\end{remark}
+
+\subsection{Version more suited to GPU}
+
+As GPU offers  shared memory mechanism between threads of the  same block, it is
+possible to  use this in  order to simplify  the previous algorithm,  i.e. using
+less than 3 xor-like PRNGs. The solution consists in 
+
+  threads of the same block  compute a random
+number and uses other random numbers of
+
 \section{Experiments}
 
 Differents experiments have been performed in order to measure the generation speed.
