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.
+This version also succeeds to the {\it BigCrush} batteries of tests.
\begin{algorithm}
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.
+Intel Xeon E5530 cadenced at 2.40 GHz for our experiments and we have used
+another one equipped with a less performant CPU and a GeForce GTX 280. Both
+cards have 240 cores.
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
+per second. In order to obtain the optimal performance 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
+that consome random numbers 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.
+2.5 and 3GSample/s. With the optimized version, it is approximately equals to
+20GSample/s. Finally we can remark that both GPU cards are quite similar. In
+practice, the Tesla C1060 has more memory than the GTX 280 and this memory
+should be of better quality.
\begin{figure}[htbp]
\begin{center}
\end{figure}
-First of all we have compared the time to generate X random numbers with both
-the CPU version and the GPU version.
+In comparison, Listing~\ref{algo:seqCIprng} allows us to generate about
+138MSample/s with only one core of the Xeon E5530.
+
-Faire une courbe du nombre de random en fonction du nombre de threads,
-éventuellement en fonction du nombres de threads par bloc.
